Keywords: Stability of nonlinear systems, Modeling
Abstract: Today, through the process of cell fate reprogramming, it is becoming possible to generate healthy cells of a required type from a patient’s own cells. This is unleashing the ability to regenerate damaged tissue by replacing it with patient-specific cells of the type in need. One approach to produce cells of the required type is to first reprogram skin cells to pluripotent stem cells, and to then differentiate these into the target cell type. These processes require accurate control of the concentration of fate-specific proteins, called transcription factors, in the cell in order to efficiently generate high quality output cells. So far, accurate control of cellular concentrations has been out of reach. Practitioners inject DNA that produces transcription factors in the starting cells at constant rates, without any control over cellular concentrations. In the past decade, however, the advances in engineering biology have reached a place where we can implement nonlinear controllers to regulate the cellular level of key molecular players. In this talk, I will describe critical challenges to accurate control of protein levels inside the cell and will show how overcoming these requires solving a classical disturbance rejection problem. I will introduce designs of quasi-integral feedback and feed-forward controllers in mammalian cells to achieve set-point regulation robustly to disturbances. Finally, I will show some of our controllers in action both as a means to uncover optimal trajectories conducive to pluripotent stem cells and as a tool to enforce more accurately optimal transcription factor levels during pluripotent stem cell reprogramming. This is the first instance in which biomolecular controllers are used for cell fate reprogramming. With this work, we have set the foundations for future research on the engineering of sophisticated biomolecular networks as controllers of complicated biological processes.

Keywords: Hybrid systems, Adaptive systems, Learning
Abstract: The convergence of physical and digital systems in modern engineering applications has inevitably led to closed-loop systems that exhibit both continuous-time and discrete-time dynamics. These closed-loop architectures are modeled as hybrid dynamical systems, prevalent across various technological domains, including robotics, power grids, transportation networks, and manufacturing systems. Unlike traditional “smooth” ordinary differential equations or discrete-time recursions, solutions to hybrid dynamical systems are generally discontinuous, lack uniqueness, and have convergence and stability properties that are defined with respect to complex sets. Therefore, effectively designing and controlling such systems, especially under disturbances and uncertainty, is crucial for the development of autonomous and efficient data-driven engineering systems capable of achieving adaptive and self-optimizing behaviors. In this talk, I will delve into recent advancements in the analysis and design of feedback controllers that can achieve such properties in complex scenarios via the synergistic use of adaptive “seeking” dynamics, robust hybrid control, and decision-making algorithms. These controllers can be systematically designed and analyzed using modern tools from hybrid dynamical systems theory, which facilitate the incorporation of "exploration” and “exploitation" behaviors within complex closed-loop systems via multi-time scale tools and perturbation theory. The proposed methodology leads to a family of provably stable and robust algorithms suitable for solving model-free feedback stabilization and decision-making problems in single-agent and multi-agent systems for which smooth feedback solutions fall short.

Keywords: Agents-based systems, Control applications
Abstract: The population growth rate has increased, leading to a need for new technologies like unmanned aerial vehicles (UAVs) to expand crop production and growth. UAVs are used in mapping, spraying, planting, crop monitoring, irrigation, and insect pest diagnosis. However, challenges such as maximum effective signal range, fuel limitations, uncovered crop areas, overlapped spraying, and pesticide waste need to be considered. In this context, we propose a multi-agent control method that associates the energy consumption with the total crop coverage. Our proposed method is able to generate the path planning for the UAVs considering a target density function, while the total flying time is decreased. The simulation results show that the proposed method decreases the total energy consumption with a negligible increment in the spray error.

Keywords: Agents-based systems, Cooperative control, Lyapunov methods
Abstract: Distance-based formation control over minimally rigid graphs suffer from so-called flip ambiguities. We here show how such controllers over 2D minimally globally rigid graphs can be proven stable, thus, avoiding the ambiguities. The approach is demonstrated on a 5-agent system, and involves embedding the 2D formation in 3D space and introducing virtual 3D body coordinate frames for the agents. By translating two of these frames along the z-axis, the rigidity matrix is full row rank and a Lyapunov stability analysis can be used to show exponential convergence to the desired formation shape for almost all initial conditions. Simulations are include to illustrate the benefit of utilizing distance-based formation control with minimally globally rigid graphs.

Keywords: Agents-based systems, Cooperative control, Networked control systems
Abstract: In this paper, an observer-based event-triggered consensus control (OETCC) strategy is proposed for linear multiagent systems (MASs) under strongly connected network. Two event-triggering conditions (ETCs) are designed to trigger information transmitting and control update separately so that continuously applying these tasks is avoided. In addition, the triggering times for both tasks are unnecessarily to be the same. Since the triggering time for control input update is predictable, continuously monitoring the related ETC can be avoided. It is proved that under the proposed strategy, consensus can be achieved exponentially. Its effectiveness is also verified by a numerical example.

Keywords: Agents-based systems, Cooperative control, Stability of nonlinear systems
Abstract: In this paper, we address the attitude alignment problem for a group of rigid body systems evolving on SO(3) under an undirected, connected and acyclic communication graph topology. We propose a velocity-free distributed hybrid feedback control law, relying on the relative orientations, with global asymptotic stability guarantees. Numerical simulation results are presented to illustrate the performance of the proposed distributed hybrid feedback control law.

Keywords: Agents-based systems, Distributed control, Cooperative control
Abstract: In this paper, we introduce a comprehensive framework called 'edge agreement', where agents collaboratively strive to reach an agreement on non-linear edge constraints defined for each of their neighbors on every edge. This provides a novel perspective on the traditional 'global consensus' problem, extending the goal beyond reaching the same value to achieving agreement over the whole multi-agent team. We present a distributed algorithm for achieving edge agreement, along with a convergence guarantee based on certain assumptions. Additionally, we illustrate how this framework encompasses a variety of cooperation problems, including global consensus, recent results on edge agreement with linear constraints, distributed solutions to linear algebraic equations, and formation control (distance and displacement-based) as special cases. Finally, to validate and demonstrate the significance of this framework, we provide numerical simulations for all the mentioned applications.

Keywords: Agents-based systems, Distributed control, Game theory
Abstract: We propose a trust-based sequential negotiation protocol for a two-player negotiation game. The protocol encompasses negotiations evolving over multi-stages, which builds upon the foundations of the monotonic concession algorithm. Trust is a crucial element built into our methodology. It serves to gauge the reliability of the opponent and dynamically evolves throughout each negotiation stage based on the degree of concessions made by the opponent in the previous negotiation step(s). Depending on the trust level, utility functions, negotiation tasks, and concession limits for subsequent negotiations are determined from our algorithm. A higher trust value encourages agents to adopt a more cooperative stance, whereas a lower trust value mitigates the risk of exposing sensitive information about tasks to potentially selfish agents. Furthermore, we prove that the extended Zeuthen strategy, renowned as the stable Nash strategy for conventional monotonic concession protocols, is also the Nash strategy for our trust based protocol by reducing the negotiation set through concession limits. To illustrate the efficacy of the proposed protocol and the stability of the strategy, we present a task allocation example. The example showcases both cooperative negotiations and negotiations against a deceptive selfish agent.

Keywords: Optimization, Game theory, Stability of nonlinear systems
Abstract: In this letter, we study distributed optimization and Nash equilibrium-seeking dynamics from a contraction theoretic perspective. Our first result is a novel bound on the logarithmic norm of saddle matrices. Second, for distributed gradient flows based upon incidence and Laplacian constraints over arbitrary topologies, we establish strong contractivity over an appropriate invariant vector subspace. Third, we give sufficient conditions for strong contractivity in pseudogradient and best response games with complete information, show the equivalence of these conditions, and consider the special case of aggregative games.

Keywords: Optimization algorithms, Optimization, Sensor networks
Abstract: We present a distributed conjugate gradient method for distributed optimization problems, where each agent computes an optimal solution of the problem locally without any central computation or coordination, while communicating with its immediate, one-hop neighbors over a communication network. Each agent updates its local problem variable using an estimate of the average conjugate direction across the network, computed via a dynamic consensus approach. Our algorithm enables the agents to use uncoordinated step-sizes. We prove convergence of the local variable of each agent to the optimal solution of the aggregate optimization problem, without requiring decreasing step-sizes. In addition, we demonstrate the efficacy of our algorithm in distributed state estimation problems, and its robust counterparts, where we show its performance compared to existing distributed first-order optimization methods.

Keywords: Constrained control, Robotics, Optimization
Abstract: This paper addresses synthesizing receding-horizon controllers for nonlinear, control-affine dynamical systems under multiple incompatible hard and soft constraints. Handling incompatibility of constraints has mostly been addressed in literature by relaxing the soft constraints via slack variables. However, this may lead to trajectories that are far from the optimal solution and may compromise satisfaction of the hard constraints over time. In that regard, permanently dropping incompatible soft constraints may be beneficial for the satisfaction over time of the hard constraints (under the assumption that hard constraints are compatible with each other at initial time). To this end, motivated by approximate methods on the maximal feasible subset (maxFS) selection problem, we propose heuristics that depend on the Lagrange multipliers of the constraints. The main observation for using heuristics based on the Lagrange multipliers instead of slack variables (which is the standard approach in the related literature of finding maxFS) is that when the optimization is feasible, the Lagrange multiplier of a given constraint is non-zero, in contrast to the slack variable which is zero. This observation is particularly useful in the case of a dynamical nonlinear system where its control input is computed recursively as the optimization of a cost functional subject to the system dynamics and constraints, in the sense that the Lagrange multipliers of the constraints over a prediction horizon can indicate the constraints to be dropped so that the resulting constraints are compatible. The method is evaluated empirically in a case study with a robot navigating under multiple time and state constraints, and compared to a greedy method based on the Lagrange multiplier.

Keywords: Optimization algorithms, Optimization, Distributed control
Abstract: We introduce the Projected Push-Pull algorithm that enables multiple agents to solve a distributed constrained optimization problem with private cost functions and global constraints, in a collaborative manner. Our algorithm employs projected gradient method to deal with constraints and a lazy update rule to control the trade-off between the consensus and optimization steps in the protocol. We prove that our algorithm achieves geometric convergence over time-varying directed graphs while ensuring that decision variables always stay within the constraint set. We derive explicit bounds for step sizes that guarantee geometric convergence based on the strong-convexity and smoothness properties of cost functions, and graph properties. Moreover, we provide additional theoretical results on the usefulness of lazy updates, revealing the challenges in the analysis of any gradient tracking method that uses projection operators in a distributed constrained optimization setting. We validate our theoretical results with numerical studies over different graph types, showing that our algorithm achieves geometric convergence empirically.

Keywords: Agents-based systems, Large-scale systems, Statistical learning
Abstract: Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving N agents, this beneficial effect usually comes in the form of an N-fold linear convergence speedup, i.e., a reduction - proportional to N - in the number of iterations required to reach a certain convergence precision. In this paper, we show for the first time that this speedup property also holds for a MARL framework subject to asynchronous delays in the local agents’ updates. In particular, we consider a policy evaluation problem in which multiple agents cooperate to evaluate a common policy by communicating with a central aggregator. In this setting, we study the finite-time convergence of AsyncMATD, an asynchronous multi-agent TD learning algorithm in which agents’ local TD update directions are subject to asynchronous bounded delays. Our main contribution is providing a finite-time analysis of AsyncMATD, for which we establish a linear convergence speedup while highlighting the effect of time-varying asynchronous delays on the resulting convergence rate.

Keywords: Optimization algorithms, Optimization
Abstract: This paper considers the decentralized (discrete) optimal transport (D-OT) problem. In this setting, a network of agents seeks to design a transportation plan jointly, where the cost function is the sum of privately held costs for each agent. Therefore, no agent has access to the total costs of a transport plan. We reformulate the D-OT problem as a constraint-coupled optimization problem and propose a single-loop decentralized algorithm with an iteration complexity of O(1/ε) that matches existing centralized first-order approaches. Moreover, we propose the decentralized equitable optimal transport (DE-OT) problem. In DE-OT, in addition to cooperatively designing a transportation plan that minimizes transportation costs, agents seek to ensure equity in their individual transport costs. The iteration complexity of the proposed method to solve DE-OT is also shown to be O(1/ε). This rate improves existing centralized algorithms, where the best iteration complexity obtained is O(1/ε^2).

Keywords: Robotics, Sensor fusion, Filtering
Abstract: This paper proposes a particle filtering method for mobile robot localization. The system model is created based on the robot motion and range-bearing measurements using the Lie group representation, and the perturbation system is obtained by introducing the random variable on the Lie group in order to solve the state estimation problem and reduce impacts from nonlinear approximations. The particle filter is derived to estimate the robot and landmark locations, where the posterior distribution of the robot pose perturbation is approximated by a mixture of Gaussian distributions to mitigate particle impoverishment and improve localization accuracy. The experiment is conducted to evaluate the performance of the proposed method.

Keywords: Mechatronics, Sampled-data control, Computational methods
Abstract: While digital implementation of control design is standard, the form of discrete model used is far less settled. At one end is the zero-order hold (ZOH) equivalent, which can be viewed as an “exact” model when the continuous-time (CT) system model is linear and time invariant (LTI) and driven only by outputs from one or more digital-to-analog converters (DACs) at a single sample rate. At the other end are ad-hoc methods that often discretize individual subsystems or blocks, before combining them into a single overall discrete model. The issue with the ZOH equivalent is that for all but the simplest models closed form solutions become largely intractable. ZOH equivalents are largely computed numerically for larger problems, but this makes it hard to comprehend such basic features as the meanings of the internal states, or the effects on the model as physical parameters or sample periods change. By contrast, discretizing individual subblocks of the model – as is often done in practice – retains much of the continuous model’s intuition, allowing for easier debugging of the discrete model. We propose a “best-of-both-worlds” methodology in which we use the availability of excellent numerical software such as MATLAB and the knowledge that model differences imparted by different discretization methods tend to shrink with the diminishing sample period. In the proposed methodology, the “one-block-at-a-time” (OBLAAT) discretized model is evaluated at different sample rates and each is compared to a numerically computed ZOH equivalent of the full system CT model. An error metric of the intuition preserving discrete model is then compared against the “exact” ZOH equivalent. This is used to gauge when the inaccuracy of the intuition-preserving discrete model is small enough that it can be chosen for implementation.

Keywords: MEMs and Nano systems, Lyapunov methods, Control applications
Abstract: Considering the non-affine-in-control system governing the motion of a spherical particle trapped inside an optical tweezer, this paper investigates the problem of stabilization of the particle position at the origin through a control Lyapunov function (CLF) framework. The proposed CLF framework enables nonlinear optimization-based closed-loop control of position of tiny beads using optical tweezers and serves as a first step towards design of effective control algorithms for nanomanipulation of biomolecules. After deriving necessary and sufficient conditions for having smooth uniform CLFs for the optical tweezer control system under study, we present a static nonlinear programming problem (NLP) for generation of robustly stabilizing feedback control inputs. Furthermore, the NLP can be solved in real-time with no need for running computationally demanding algorithms. Numerical simulations demonstrate the effectiveness of the proposed control framework in the presence of external disturbances and initial bead positions that are located far away from the laser beam.

Keywords: Autonomous robots, Predictive control for nonlinear systems, Learning
Abstract: This paper proposes a novel computationally efficient nonlinear model predictive controller (NMPC) for learning-based models. The proposed NMPC scheme uses a hybrid model of the dynamic system, including a nominal derived model and a learning-based model that compensates for the incomplete knowledge of the system, i.e., unmodeled dynamics. The NMPC is designed with a tailored cost function that takes into account the learned-dynamics of the system. The cost function is formulated without stabilizing terminal conditions required for stabilization. Moreover, the proposed scheme facilitates the computation of the shortest possible stabilizing prediction horizon that guarantees the asymptotic stability of the closed-loop system. The proposed scheme is applied to an unmanned aerial vehicle (UAV) for validation. The performance of the proposed scheme is investigated through extensive numerical simulations and compared against the state-of-the-art traditional NMPC and traditional learning-based NMPC schemes proposed in literature. The results show superior trajectory tracking performance of the proposed learning-based NMPC scheme at short prediction horizons.

Keywords: Mechatronics
Abstract: Transmissibility operators are mathematical objects that characterize the relationship between outputs of a dynamic system. Transmissibility operators have been used in applications including health monitoring, fault detection, fault localization, fault mitigation, output prediction, state estimation, and system identification. The transmissibility relationship can be either constructed if a model of the system is available, or estimated otherwise. The constructed or estimated transmissibility is used along with one subset of outputs to predict the response of the other subset of outputs. Transmissibility operators are usually constructed or estimated such that the dimension of the transmissibility input is equal to the dimension of the excitation signal acting on the underlying system. Numerical evidence introduced in previous papers showed that the accuracy of the predicted output improves as the number of transmissibility inputs increases. In this paper, we relax the assumption that requires the dimension of the transmissibility input to be equal to the dimension of the excitation signal acting on the underlying system, which results in a more general mathematical representation of transmissibility operators, which we call generalized transmissibility operators.

Keywords: Mechatronics
Abstract: This paper introduces an output feedback control method for an offshore crane system (OCS) with unknown wave disturbance caused by roll-heave motions. The method combines a backstepping controller and a high-gain observer to achieve the desired tracking trajectories of the cart position and the rope length. The dynamic model of the offshore crane system is initially formulated to account for unknown dynamic friction, dynamic coupling, and external disturbances while ensuring dynamic decoupling of the cart position and hoisting dynamics. The backstepping controller is proposed to stabilize the system and achieve trajectory tracking, where the extended high gain observer is used to estimate the dynamic states and external disturbances. The effectiveness of the proposed control approach is verified through simulations for both the system with and without disturbances. The simulation results demonstrate that the proposed approach is capable of achieving the desired trajectories, even under conditions of unknown nonlinearities and wave motion disturbances

Keywords: Markov processes, Stochastic systems, Autonomous systems
Abstract: Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e.g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes. The method uses a finite partitioning of the noise domain to construct an interval Markov chain (IMC) abstraction of the system via transition probability intervals. Noise partitioning allows for a general class of distributions and structures, including multiplicative and mixture models, and admits both known and data-driven systems. The partitions required for optimal transition bounds are specified for systems that are monotonic with respect to the noise, and explicit partitions are provided for affine and multiplicative structures. By the soundness of the abstraction procedure, verification on the IMC provides guarantees on the stochastic system against a temporal logic specification. In addition, we present a novel refinement-free algorithm that improves the verification results. Case studies on linear and nonlinear systems with non-Gaussian noise, including a data-driven example, demonstrate the generality and effectiveness of the method without introducing excessive conservatism.

Keywords: Model Validation, Estimation, Identification
Abstract: A Kullback–Leibler (K-L) divergence based metric is proposed to compare the performance of a set of linear Kalman Filters for a flexible wing wind tunnel model. Each of the Kalman Filters is designed with the Autocovariance Least Squares (ALS) technique based on common data sets but different state-space models. The metric is used to identify areas of model uncertainty and informs selection of the best model. The method is demonstrated both for simulation of a simple mass-spring-damper system and for a gust load alleviation test-bed in the commercial Kirsten Wind Tunnel facility.

Keywords: Output regulation, Hybrid systems, Identification
Abstract: This paper studies adaptive pre-processing output regulation problem of linear systems in the presence of non-vanishing measurements and unknown linear exosystems. The proposed regulator is comprised of a washout filter, a pre-processing internal model and a stabilizer, all in continuous-time form and parameterized by parameters acting as the state of a discrete-time identifier. Under a persistency of excitation condition, it is shown that the resulting closed-loop signals are bounded and the regulation error asymptotically converges to zero. Finally, as an extension, we show through an example that the proposed regulator can be applied to a class of nonlinear systems.

Keywords: Discrete event systems, Identification, Petri nets
Abstract: This paper proposes a new approach to obtain a model of a discrete manufacturing system by considering observed input and output sequences. In the recent work [1], it has been shown that a model of the plant can be obtained by the Pareto modeling approach. The Pareto modeling approach transforms the signal interpreted Petri net (SIPN) model of the controller that controls the plant into a plant model. The system may evolve along states which do not influence the control algorithm and thus are not considered in the basic plant model obtained by the Pareto modeling approach. The proposed approach uses the observed input and output sequences of the plant to enhance the model obtained by Pareto modeling. At first, it is found out which observed sensor signals and actuator signals cannot be represented by the basic SIPN model. Then for each observed sensor output that cannot be associated to a marking of the SIPN a new place is added. To integrate the added places into the model, new transitions are added as well whose firing condition corresponds to the observed actuator signals. Since the basic structure of the model is obtained by the Pareto modeling approach, the identification process can be much accelerated. The proposed approach is illustrated by an example of a mixing tank and can be applied to any manufacturing system controlled by a programmable logic controller.

Keywords: Data storage systems, Numerical algorithms, Multivehicle systems
Abstract: This work develops and explains a method of using a database's organizational structure to implement data manipulations (grouping, addition, averaging) that enable the database to exhibit intentional algorithmic behavior. Specifically, the Allan VARiance (AVAR) estimation using B+ tree data structures is developed. AVAR is a crucial algorithm to determine the computational limits on data accuracy imposed by real-world noise sources. Unfortunately, AVAR is computationally challenging and is typically applied to large datasets (1e6 or larger), which can be difficult to manage without a database. In the previous work, the authors proposed Fast Allan VARiance (FAVAR) algorithms inspired by the FFT to improve computation speed by up to four orders of magnitude. These FAVAR algorithms apply to data sets that are manageable locally on a computer (MB to GB in memory) but can be difficult to deploy on ``big data", e.g., data sets that cannot fit in main memory. Notably, B+ trees index one-dimensional data similar to FAVAR, i.e., using data aggregations that scale in size as a function of tree order. This paper utilizes the similarity in B+ trees and FAVAR to use the database's operational process to automatically deploy an algorithm to estimate AVAR using a B+ tree for data corrupted by common noise types - white noise and random walk. Comparing AVAR estimates to algorithm-targeted B+ tree calculations, the results match within 95% confidence bounds.

Keywords: Estimation, Optimization, Switched systems
Abstract: In this paper, a multiple model (MM) framework using horizon scenario tree (HST) is proposed for optimization-based state estimators. The problem is addressed by structuring a sliding window (SW) of measurement history as a bounded tree of scenarios. The scenario with minimum measurement residual is then used to advance the sliding window optimizer. A benchmark multi-model estimation target tracking problem having constant velocity, and constant turn rate models is used in the study to evaluate the proposed architecture. Compared to the interacting multiple model (IMM) filter solution, Monte Carlo numerical results validate the ability of the proposed algorithm to efficiently identify the dynamic changes while producing estimates which switches the model to achieve accurate tracking performance.

Keywords: Optimization, Numerical algorithms, Data storage systems
Abstract: Tensor structures are fundamental in addressing the intricate challenges posed by high-dimensional data across a spectrum of scientific and computational domains. Within this context, low-rank tensor approximation plays a pivotal role in enhancing data processing efficiency. This paper develops a novel adaptive low-rank tensor approximation method by introducing mixed-integer representations to identify an appropriate low-rank approximation for high-dimensional tensors. The approach takes into consideration both tensor rank determination and approximation accuracy, leveraging binary variables to represent tensor ranks that will be optimized as unknowns with the tensor arrays. By integrating the alternating least squares (ALS) technique with the truncation method, the integrated algorithm effectively achieves a proper low-rank tensor with high approximation precision. To substantiate its efficacy and efficiency in solving tensor approximation problems, the paper provides extensive simulation results and analysis.

Keywords: Optimization, Optimization algorithms, Game theory
Abstract: We consider a class of hierarchical multi-agent optimization problems over networks where agents seek to compute an approximate solution to a single-stage stochastic mathematical program with equilibrium constraints (MPEC). MPECs subsume several important problem classes including Stackelberg games, bilevel programs, and traffic equilibrium problems, to name a few. Our goal in this work is to provably resolve stochastic MPECs in distributed regimes where the agents only have access to their local objectives and an inexact best-response to the lower-level equilibrium problem. To this end, we devise a new method called randomized smoothed distributed zeroth-order gradient tracking (rs-DZGT). This is a novel gradient tracking scheme where agents employ a zeroth-order implicit scheme to approximate their (unavailable) local gradients. Leveraging the properties of a randomized smoothing technique, we establish the convergence of the method and derive complexity guarantees for computing a stationary point of an optimization problem with a smoothed implicit global objective. We also provide preliminary numerical experiments where we compare the performance of rs-DZGT on networks under different settings with that of its centralized counterpart.

Keywords: Optimization, Optimization algorithms, Learning
Abstract: This is part II of a multi-part paper on “Mutual Learning in Optimization”. In part I of the paper [1] two agents attempting jointly to determine the optimum of a function f[x_1,x_2,...,x_n] of n variables, use the same or different optimization techniques (well known in the literature). The agents are assumed to communicate with each other at random instants of time, and convey to the other all the information that they possess which may prove useful. It was shown through simple examples depending upon the initial conditions, the number of steps taken, and the methods used by the agents that totally different decisions may be made by the participants at every stage. At the same time, in all cases, the agents improve their performance and achieve the optimum asymptotically.

In this paper the problem of determining the maximum of a function f[x_1,x_2,...,x_n] of n variables is considered. The number of agents is assumed to be N, and all three cases N>n+1, N=n+1 and N

Keywords: Optimization, Optimization algorithms, Numerical algorithms
Abstract: This paper proposes a set of novel optimization algorithms for solving a class of convex optimization problems with time-varying streaming cost functions. We develop an approach to track the optimal solution with a bounded error. Unlike prior work, our algorithm is executed only by using the first-order derivatives of the cost function, which makes it computationally efficient for optimization with time-varying cost function. We compare our algorithms to the gradient descent algorithm and show why gradient descent is not an effective solution for optimization problems with time-varying cost. Several examples, including solving a model predictive control problem cast as a convex optimization problem with a streaming time-varying cost function, demonstrate our results.

Keywords: Optimization, Optimization algorithms, Numerical algorithms
Abstract: This work details an algorithm for solving large weapon-target assignment problems using a novel abstraction approach that improves average computation time over other methods. Provided proofs show that the algorithm converges to a solution in finite time, if one exists. Numerical results demonstrate the improved computational efficiency of the algorithm compared to a baseline approach that does not use abstraction. Application to additional domains is discussed, facilitating application.

Keywords: Optimization, Optimization algorithms
Abstract: We design and analyze a novel regularized accelerated proximal gradient method for a class of bilevel optimization problems where from the optimal solution set of a composite convex optimization problem, we seek to find a solution that minimizes a secondary convex objective function. When the optimal solution set of the lower-level problem admits a weak sharpness property, we significantly improve existing iteration complexity to mathcal{O}(epsilon^{-0.5}) for both suboptimality and infeasibility error metrics, where epsilon>0 denotes an arbitrary scalar. We also obtain guarantees in absence of the weak sharpness property. In addition, contrary to some existing methods that require solving the optimization problem sequentially (initially solving an optimization problem to approximate the solution of the lower-level problem followed by a second scheme), our method concurrently solves the bilevel optimization problem. To the best of our knowledge, the proposed algorithm achieves the best-known iteration complexity, which matches the optimal complexity for single-level convex optimization. Preliminary numerical experiments on a sparse linear regression problem validate the efficacy of our approach.

Keywords: Modeling, Automotive systems, Automotive control
Abstract: With the rapid advancements in connected and automated vehicles (CAV) technologies and vehicle onboard computational units, various model predictive control-based algorithms have emerged for electric vehicle (EV) thermal management systems. However, the nonlinear dynamics of refrigerant circuits, coupled with battery cooling systems, often require assumptions and simplifications when developing computationally inexpensive physics-based control-oriented models, and these approximations may lead to non-negligible prediction errors. To address this challenge, this paper proposes a data-driven Koopman model to capture the behavior of integrated HVAC and battery cooling systems in an EV. The proposed model is developed using the Extended Dynamic Mode Decomposition (EDMD) structure, leveraging the data acquired from a high-fidelity EV thermal management system (TMS) model. The dimension of the lifted space is investigated, considering both a corrected Akaike Information Criterion (AIC_c) and open-loop prediction performance. The validation of the proposed model against the high-fidelity model shows its superiority over a physics-based model: the root mean square errors (RMSEs) for the refrigerant saturated temperature at the outdoor condenser and the evaporator are 1.51 degC and 5.13 degC, respectively; the RMSEs for mass-averaged battery temperature and battery coolant temperature are 0.10 degC and 0.67 degC, respectively.

Keywords: Energy systems, Control applications
Abstract: Pack-level battery usage and the inherent cell heterogeneity necessitate effective active equalization systems to enhance their usable capacity and lifetime. Due to the lack of a unified mathematical model, it is difficult to quantitatively analyze and compare the performance of state-of-the-art active equalization systems at the pack level. To address this gap, we introduce a novel, hypergraph-based approach to establish the first unified model for various active battery equalization systems. This model reveals the intrinsic relationship between battery cells and equalizers by representing them as the vertices and hyperedges of hypergraphs, respectively. It can offer a convenient and concise format for comprehensive analysis and comparison. Extensive results demonstrate the efficiency of the proposed model.

Keywords: Energy systems, Optimal control, Machine learning
Abstract: In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the imitation learning paradigm, this manuscript introduces an innovative solution to confront the inherent challenges often associated with conventional predictive control strategies for constrained battery charging. A significant contribution of this study lies in the adaptation of the Dataset Aggregation (DAGGER) algorithm to address scenarios where battery parameters are uncertain, and internal states are unobservable. Results drawn from a practical battery simulator that incorporates an electrochemical model highlight substantial improvements in battery charging performance, particularly in meeting all safety constraints and outperforming traditional strategies in computational processing.

Keywords: Predictive control for nonlinear systems, Power systems
Abstract: In many state-of-the-art control approaches for power systems with storage units, an explicit model of the storage dynamics is required. With growing numbers of storage units, identifying these dynamics can be cumbersome. This paper employs recent data-driven control approaches that do not require an explicit identification step. Instead, they use measured input/output data in control formulations. In detail, we propose an economic data-driven model predictive control (MPC) scheme to operate a small power system with inputnonlinear battery dynamics. First, a linear data-driven MPC approach that uses a slack variable to account for plantmodel-mismatch is proposed. In a second step, an inputnonlinear data-driven MPC scheme is deduced. Comparisons with a reference indicate that the linear data-driven MPC approximates the nonlinear plant in an acceptable manner. Even better results, however, can be obtained with the inputnonlinear data-driven MPC scheme which provides increased prediction accuracy.

Keywords: Fault diagnosis, Fault detection, Energy systems
Abstract: One of the key challenges towards the reliable operation of cyber-physical systems (CPS) is the threat of cyberattacks on system actuation signals and measurements. In recent years, system theoretic research has focused on effectively detecting and isolating these cyberattacks to ensure proper restorative measures. Although both model-based and model-free approaches have been used in this context, the latter are increasingly becoming more popular as complexities and model uncertainties in CPS increases. Thus, in this paper we propose a Koopman operator-based model-free cyberattack detection-isolation scheme for CPS. The algorithm uses limited system measurements for its training and generates real-time detection-isolation flags. Furthermore, we present a simulation case study to detect and isolate actuation and sensor attacks in a Lithium-ion battery system of a plug-in electric vehicle during charging.

Keywords: Simulation, Optimization, Smart grid
Abstract: In this work, we perform a techno-economic analysis on using hydrogen underground storage (HUS) for grid electricity storage, comparing two control strategies, fixed setpoint (SP) control and dynamic control, via dynamic simulations and optimization. Both strategies demonstrate similar arbitrage profit margins, generating approximately 40.74 million and 41.74 million annually, based on 2022 CAISO grid data. A primary challenge identified for HUS in electricity storage is the significant capital investment required for electrolyzers and combined cycle power plants (CCPP), coupled with a lower capital utilization rate due to lower round-trip efficiency. To enhance the economic attractiveness of the HUS system, the study proposes a novel solution: leveraging natural gas (NG) for power generation when the CCPP is not in operation. This strategy significantly boosts the CCPP's operational time and increases the net present value to an appealing 21.94 million per year. The research concludes with a positive outlook on HUS's potential as a sustainable and economically viable grid electricity storage solution.

Keywords: Air traffic management, Traffic control, Transportation networks
Abstract: In the vision of urban air mobility, air transport systems serve the demands of urban communities by routing flight traffic in networks formed by vertiports and flight corridors. We develop a routing algorithm to ensure that the air traffic flow fairly serves the demand of multiple communities subject to stochastic network capacity constraints. This algorithm guarantees that the flight traffic volume allocated to different communities satisfies the emph{alpha-fairness conditions}, a commonly used family of fairness conditions in resource allocation. It further ensures robust satisfaction of stochastic network capacity constraints by bounding the coherent risk measures of capacity violation. We prove that implementing the proposed algorithm is equivalent to solving a convex optimization problem. We demonstrate the proposed algorithm using a case study based on the city of Austin. Compared with one that maximizes the total served demands, the proposed algorithm promotes even distributions of served demands for different communities.

Keywords: Autonomous vehicles, Traffic control, Transportation networks
Abstract: Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users to monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. In this paper, we address the problem of effectively coordinating CAVs on lane-based roadways. Our approach utilizes potential functions to generate repulsive forces between CAVs that ensure collision avoidance. However, such potential functions can lead to unrealistic acceleration profiles and large inter-vehicle distances. The primary contribution of this work is the introduction of performance-sensitive potential functions to address these challenges. In our approach, the parameters of a potential function are determined through an optimization problem aiming to reduce both acceleration and inter-vehicle distances. To circumvent the computational implications due to the complexity of the resulting optimization problem that prevents the derivation of a real-time solution, we train a neural network model to learn the mapping of initial conditions to optimal parameters derived offline. Then, we prove sufficient criteria for the sampled-data model to ensure that the neural network output does not activate any of the state and safety constraints. Finally, we provide simulation results to demonstrate the effectiveness of the proposed approach.

Keywords: Multivehicle systems, Traffic control, Adaptive control
Abstract: Truck platooning has been demonstrated as one of the promising solutions to reduce freeway congestion, engine energy consumption, and associated emissions. However, we still lack efficient local-level algorithms to form a truck platoon while avoiding their disruptive impact on surrounding traffic flow. Motivated by this view, this study developed a Sequential Truck Platoon Formation algorithm (StPF) built upon a Reactive Controller following spring-mass-damper (SMD) control. Specifically, the StPF identifies the pair of connected trucks to form/join a platoon and adjusts for any non-cooperating human driven vehicles (HDVs) while the Reactive Controller manages the movement of the trucks. The controller considers a mixed, hybrid, and dynamic traffic environment, including adjacent connected and autonomous vehicles following cooperative adaptive cruise control and human-driven vehicles as well as distant aggregated traffic flow of the mixed traffic on the freeway segment, and the Cell Transmission Model (CTM) to capture the dynamics of the macroscopic flow. Numerical experiments indicated that platoon formation is favorable in low-flow, high CAV penetration traffic conditions and provided this-and-that benefits to the truck and its surrounding traffic.

Keywords: Traffic control, Control applications, Optimal control
Abstract: The recently developed DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) method has shown promising performance for data-driven predictive control of Connected and Autonomous Vehicles (CAVs) in mixed traffic. However, its simplistic zero assumption of the future velocity errors for the head vehicle may pose safety concerns and limit its performance of smoothing traffic flow. In this paper, we propose a robust DeeP-LCC method to control CAVs in mixed traffic with enhanced safety performance. In particular, we first present a robust formulation that enforces a safety constraint for a range of potential velocity error trajectories, and then estimate all potential velocity errors based on the past data from the head vehicle. We also provide efficient computational approaches to solve the robust optimization for online predictive control. Nonlinear traffic simulations show that our robust DeeP-LCC can provide better traffic efficiency and stronger safety performance while requiring less offline data.

Keywords: Multivehicle systems, Optimal control, Hybrid systems
Abstract: Virtual Coupling (VC) has the potential to significantly increase the capacity of heavy-haul railways. However, existing controllers do not fully exploit this potential due to inaccuracies in the model of heavy-haul trains. To address this issue, we propose a hybrid model predictive control framework to implement the virtual coupling of heavy haul trains. Firstly, we model the traction and brake system in which the discrete notch is directly considered as the control input. Then we approximate the nonlinearities in propulsion resistance, track gradient, curvature and traction/brake force by piecewise affine functions. The control law can be solved by mixed integer linear programming (MILP). Finally, we simulate the convoy operation and show that the proposed method can improve the stability of the convoy and enable the track to accommodate more trains.

Keywords: Transportation networks, Aerospace, Optimization
Abstract: Electric airplanes are expected to take to the skies soon, finding first use cases in small networks within hardly accessible areas, such as island communities. In this context, the environmental footprint of such airplanes will be strongly determined by the energy sources employed when charging them. This paper presents a framework to optimize aircraft assignment, routing and charge schedules explicitly accounting for the energy availability at the different airports, which are assumed to be equipped with renewable energy sources and stationary batteries. Specifically, considering the daily travel demand and weather conditions forecast in advance, we first capture the aircraft operations within a time-expanded directed acyclic graph, and combine it with a dynamic energy model of the individual airports. Second, aiming at minimizing grid-dependency, we leverage our models to frame the optimal electric aircraft and airport operational problem as a mixed-integer linear program that can be solved with global optimality guarantees. Finally, we showcase our framework in a real-world case-study considering one week of operations on the Dutch Leeward Antilles. Our results show that, depending on weather conditions and compared to current schedules, optimizing flights and operations in a renewable-energy-aware manner can reduce grid dependency from 18 to 100 %, whilst significantly shrinking the operational window of the airplanes.

Keywords: Spacecraft control, Formal verification/synthesis, Switched systems
Abstract: The safety of satellites is an increasingly difficult requirement as launches of new satellites increase the clutter of space environments. The deployment of new, experimental controllers is important to increase the autonomous capabilities of satellites but may be at odds with safety. In this work, we consolidate these two goals by synthesizing a formally safe controller and a runtime assurance logic that can switch between the safety and experimental controllers to guarantee the safe operation of a satellite. This switching logic leverages reachable and recoverable sets. We deploy the synthesized safety controller and switching logic in a close-proximity scenarios with both static and dynamic obstacles and show that the satellite remains safe.

Keywords: Adaptive control, Spacecraft control, Learning
Abstract: In this paper, a new multi-thread, quasi-attracting manifold controller for attitude tracking of a spacecraft with an unknown inertia matrix is introduced. The controller guarantees closed-loop stability for any given reference trajectory while adapting many estimates of unknown parameters such that any one particular estimate almost never moves further away from the truth than its previous estimate. While maintaining this ``no-regret'' learning feature for each individual thread, all of the threads are mixed into a single composite estimate using a judiciously designed weighting scheme that rewards both past and present performance of each individual thread. This provides faster error convergence and superior transient response performance compared with traditional single-thread based adaptive control implementations. The need for rapid convergence of poorly determined inertia properties is particularly relevant when recovering non-cooperative or uncontrolled space objects such as space debris or other on-orbit servicing applications. The beneficial features of the proposed multi-thread learning scheme is demonstrated via numerical simulations.

Keywords: Constrained control, Optimal control, Autonomous systems
Abstract: In many resource-limited optimal control problems, multiple constraints may be enforced that are jointly infeasible due to external factors such as subsystem failures, unexpected disturbances, or fuel limitations. In this manuscript, we introduce the concept of blameless optimality to characterize control actions that a) satisfy the highest prioritized and feasible constraints and b) remain optimal with respect to a mission objective. For a general optimal control problem with jointly infeasible constraints, we prove that a single optimization problem cannot find a blamelessly optimal control sequence. Instead, finding blamelessly optimal control actions requires sequentially solving at least two optimal control problems: one to determine the highest priority level of constraints that is feasible and another to determine the optimal control action with respect to these constraints. We apply our results to a rocket landing scenario in which violating at least one prioritized landing constraint is unavoidable. Leveraging the concept of blameless optimality, we formulate blamelessly optimal controllers that can autonomously prioritize the constraints most critical to a mission.

Keywords: Spacecraft control, Stochastic optimal control, Autonomous robots
Abstract: This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.

Keywords: Optimal control, Computational methods, Optimization algorithms
Abstract: An adaptive mesh refinement method for nu- merically solving optimal control problems is described. The method employs collocation at the Legendre-Gauss-Radau points. Within each mesh interval, a relative error estimate is derived based on the difference between the Lagrange polynomial approximation of the state and an adaptive forward- backward explicit integration of the state dynamics. Accuracy in the method is achieved by adjusting the number of mesh intervals and degree of the approximating polynomial in each mesh interval. The method is demonstrated on time-optimal transfers from an L1 halo orbit to an L2 halo orbit in the Earth- Moon system, and performance is compared against previously developed mesh refinement methods.

Keywords: Spacecraft control, Formal verification/synthesis, Machine learning
Abstract: Autonomous spacecraft control via Shielded Deep Reinforcement Learning (SDRL) has become a rapidly growing research area. However, the construction of shields and the definition of tasking remains informal, resulting in policies with no guarantees on safety and ambiguous goals for the RL agent. In this paper, we first explore the use of formal languages, namely Linear Temporal Logic (LTL), to formalize spacecraft tasks and safety requirements. We then define a manner in which to construct a reward function from a co-safe LTL specification automatically for effective training in SDRL framework. We also investigate methods for constructing a shield from a safe LTL specification for spacecraft applications and propose three designs that provide probabilistic guarantees. We show how these shields interact with different policies and the flexibility of the reward structure through experimentation.

Keywords: Adaptive control, Mechanical systems/robotics, Neural networks
Abstract: A neural-adaptive controller with fractional-order integral (FOI) feedback and update laws is derived to enhance performance and react quickly to uncertainties. Feedback control law with an FOIs react quickly to uncertainties, such as biases, which complement slow-responding artificial neural networks (ANN). Adaptive ANNs are further enhanced by including FOI in network training, improving convergence speed and depth. Lyapunov stability methods enabled the creation of the proposed method generalized on a nonlinear direct-input system. Simulated quadcopter and serial manipulator systems utilize the proposed method to follow trajectories in their respective spaces. The proposed controller significantly enhances performance and adaptive capabilities while remaining stable over multiple execution cycles relative to baseline non-FOI adaptive methods.

Keywords: Adaptive control, Optimization, Sampled-data control
Abstract: Extremum seeking control (ESC) constitutes a powerful technique for online optimization with theoretical guarantees for convergence to the neighborhood of the optimizer under well-understood conditions. However, ESC requires a nonconstant perturbation signal to provide persistent excitation to the target system to yield convergent results, which usually results in steady state oscillations. While certain techniques have been proposed to eliminate perturbations once the neighborhood of the minimizer is reached, system modifications and environmental perturbations can suddenly change the minimizer and nonconstant perturbations would once more be required to convergence to the new minimizer. Hence, this paper develops a retrospective cost-based ESC (RC/ESC) technique for online output minimization with a vanishing perturbation, that is, a perturbation that becomes zero as time increases independently from the state of the controller or the controlled system. The performance of the proposed algorithm is illustrated via numerical examples.

Keywords: Adaptive control, Predictive control for nonlinear systems, Optimization algorithms
Abstract: This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system using recursive least squares for online, closed-loop system identification with optimization over a receding horizon performed by quadratic programming (QP). In order to account for the input nonlinearity, the input matrix is defined to be control dependent, and the optimization is performed iteratively. This technique is applied to output stabilization of a chain of integrators with unknown dynamics under control saturation and deadzone input nonlinearity.

Keywords: Adaptive control, Robotics, Estimation
Abstract: This paper presents an integral concurrent learning (ICL)-based observer for a monocular camera to accurately estimate the Euclidean distance to features on a stationary object, under the restriction that state information is unavailable. Using distance estimates, an infinite horizon optimal regulation problem is solved, which aims to regulate the camera to a goal location while maximizing feature observability. Lyapunov-based stability analysis is used to guarantee exponential convergence of depth estimates and input-to-state stability of the goal location relative to the camera. The effectiveness of the proposed approach is verified in simulation, and a table illustrating improved observability through better conditioning of the regressor is provided.

Keywords: Adaptive control, Uncertain systems
Abstract: We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties. The method relies on adjusting individual adaptation gains to cancel the effects of unmatched parameters whose transients could otherwise destabilize the closed-loop system. The method guarantees the restoration of the adaptation gains to their nominal value and can readily incorporate direct adaptation laws for matched uncertainties. The proposed framework is general as it only requires stabilizability for all possible models.

Keywords: Maritime control, Cooperative control, Adaptive control
Abstract: This paper presents a novel approach to planning and controlling the hybrid formation motion of a fleet of underactuated autonomous underwater vehicles (AUVs). The leader AUV performs end-to-end motion planning and obstacle avoidance using deep reinforcement learning (DRL). The followers, on the other hand, are guided by a backstepping technique to maintain the desired formation behind the leader. Neuro-adaptive strategies are employed to estimate the followers' unknown nonlinear terms. Operating within a machine learning (ML) framework, the leader is trained to formulate a control policy that guarantees the safe movement of the entire group towards the target. Theoretical analysis using the Lyapunov stability theory demonstrates that the AUVs' formation control system ensures uniform ultimate boundedness (UUB). The effectiveness of the proposed methodology is evaluated across a range of simulation scenarios.

Keywords: Autonomous systems, Constrained control, Distributed control
Abstract: Safety is essential for autonomous systems, in particular for interconnected systems in which the interactions among subsystems are involved. Motivated by the recent interest in cyber-physical and interconnected autonomous systems, we address the safe stabilization problem of interconnected systems with time delays. We propose multiple control Lyapunov and barrier functionals for the stabilization and safety control problems, respectively. In order to investigate the safe stabilization control problem, the proposed multiple control functionals are combined together via two methods: the optimization-based method and the sliding mode based method. The resulting controllers can be of either explicit or implicit forms, both of which ensure the safe stabilization objective of the whole system. The derived results are illustrated via a reach-avoid problem of multi-robot systems.

Keywords: Autonomous systems, Constrained control, Fault detection
Abstract: We propose an interval extension of Signal Temporal Logic (STL) called Interval Signal Temporal Logic (I-STL). Given an STL formula, we consider an interval inclusion function for each of its predicates. Then, we use minimal inclusion functions for the min and max functions to recursively build an interval robustness that is a natural inclusion function for the robustness of the original STL formula. The resulting interval semantics accommodate, for example, uncertain signals modeled as a signal of intervals and uncertain predicates modeled with appropriate inclusion functions. In many cases, verification or synthesis algorithms developed for STL apply to I-STL with minimal theoretic and algorithmic changes, and existing code can be readily extended using interval arithmetic packages at negligible computational expense. To demonstrate I-STL, we present an example of offline monitoring from an uncertain signal trace obtained from a hardware experiment and an example of robust online control synthesis enforcing an STL formula with uncertain predicates.

Keywords: Autonomous systems, Control applications, Learning
Abstract: The typical control architecture for autonomous helicopter operations consists of an inner loop for attitude stabilization and an outer loop control for lateral/longitudinal dynamics. In this paper, we focus on the design of the outer loop controller for helicopter aerial refueling. In order to alleviate the drawbacks of solely model-based or pure data-driven controllers, a residual reinforcement learning framework is presented where a model-based controller is combined with a reinforcement learning (RL) controller. These two controllers work in tandem to leverage the advantages of both the model-based and data-driven components. However, this requires appropriately allocating control authority to each controller (the maximum/minimum control authority and/or controller parameters), which are typically determined through time-consuming heuristic methods. Recognizing the need for a systematic approach to achieve reasonable harmony between the two control strategies, here we present an analytical (model-based) approach that facilitates the selection of the outer loop control gains for the model-based (dynamic inversion) controller along with appropriate bounds for the (data-driven) RL control input. We explicitly incorporate uncertainty bounds on the drogue motion and the maximum allowable acceleration of the aircraft into the control design. Finally, we showcase the applicability of the methodology through a simulation study.

Keywords: Autonomous systems, Cooperative control, Machine learning
Abstract: This paper explores the use of a Twin-Delayed Deep Deterministic Policy Gradient (TD3) approach for the formation control of multiple Uncrewed Aerial Vehicles (multi-UAVs). A leader-follower configuration is adopted. The proposed TD3 algorithm integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with the double Q-learning technique, creating a continuous controller for the formation tracking of multi-UAVs. Unlike the DDPG, TD3 utilizes two Q-networks that explore the environment independently, selecting the smaller Q-value to compute targets and update the Q-functions. Additionally, the target action policy is constrained within a valid action range, and updates are deliberately delayed to prevent the exploitation of erroneous experiences. Simulation results validate the effectiveness of the proposed method.

Keywords: Autonomous systems, Markov processes, Feedback linearization
Abstract: The autonomy aircraft guidance problem has been an important and challenging issue that has received significant attention in recent years. In this paper, we propose a novel controller to plan the trajectory of an aircraft under uncertainty by providing optimal commands to reach the target while avoiding no-fly zones and optimizing various performance metrics (e.g., fuel consumption and travel distance). In particular, we introduce a two-layer controller, where a Partially Observable Markov Decision Process (POMDP) is formalized as the high-level controller (outer loop), and an inverse dynamics controller serves as the low-level controller (inner loop). The POMDP provides the best local reference values to the low-level controller, which then commands the aircraft actuators. By leveraging a linearized dynamic model obtained through dynamics inversion, the POMDP can efficiently compute optimal reference values. We tested this approach in a simulated scenario where the aircraft avoids no-fly zones to reach a target position.

Keywords: Autonomous systems, Networked control systems, Control of networks
Abstract: Multi-agent networks in applications such as formation control experience disturbances in the agent states, which may propagate through the network and lead to instability in the dynamics, deviations of node states from their desired values, and even collisions and other safety hazards. In this paper, we propose techniques for mitigating disturbances by partitioning an unstable network into stable sub-networks, which we denote as islands. Taking the input-output L2 gain as the main optimization criterion, we derive sufficient Diagonally Dominant Sum-of-Squares (DDSOS) conditions for each island to satisfy a given bound on the L2 gain, and map these diagonal dominance conditions to an objective function. In order to mitigate the combinatorial complexity of selecting a subset of edges to remove to create islands, we prove that our proposed objective function is equivalent to a monotone supermodular function, and that the islanding problem is equivalent to submodular maximization with a matroid basis constraint. This problem structure enables us to develop polynomial-time algorithms with constant-factor optimality bounds. We evaluate our approach through a numerical study of consensus-based formation control in a network of single integrators.

Keywords: Predictive control for linear systems, Constrained control, Uncertain systems
Abstract: Cyber-physical and autonomous systems are often equipped with mechanisms that provide predictions/projections of future disturbances, e.g., road curvatures, commonly referred to as preview or lookahead, but this preview information is typically not leveraged in the context of deriving control barrier functions (CBFs) for safety. This paper proposes a novel limited preview control barrier function (LPrev-CBF) that avoids both ends of the spectrum, where on one end, the standard CBF approach treats the (previewable) disturbances simply as worst-case adversarial signals and on the other end, a recent Prev-CBF approach assumes that the disturbances are previewable and known for the entire future. Moreover, our approach applies to input-delay systems and has recursive feasibility guarantees since we explicitly take input constraints/bounds into consideration. Thus, our approach provides strong safety guarantees in a less conservative manner than standard CBF approaches while considering a more realistic setting with limited preview and input delays.

Keywords: Predictive control for linear systems, Distributed control, Cooperative control
Abstract: This paper addresses the periodic source seeking problem, where a signal source exhibits periodic movement and a distributed system is harnessed to discern its motion law and track it. We present an improved Gaussian process-based model predictive control to fulfill this control objective. The proposed algorithm leverages Gaussian processes (GP) to model the signal field, and utilizes Bayesian optimization to forecast the position sequence of the periodic signal source. A distributed model predictive control (DMPC) is incorporated into the algorithm, enabling the derivation of periodic control inputs for the system. By judiciously selecting training datasets for GP, the proposed algorithm can solve the periodic source seeking problem with a guaranteed probability. Simulation results substantiate the efficacy of this method and demonstrate a reduction in computation time for optimization compared to existing GP-based MPC methods.

Keywords: Predictive control for linear systems, Identification for control, Constrained control
Abstract: We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced error bounds. These bounds, derived from multi-step predictors, are utilized in a homothetic tube formulation to mitigate conservatism. Lastly, a multi-rate formulation is adopted to handle the incompatibilities of multi-step predictors. We provide a theoretical analysis, guaranteeing robust recursive feasibility, constraint satisfaction, and (practical) stability of the desired setpoint. We use a simulation example to compare it to existing literature and demonstrate advantages in terms of conservatism and computational complexity.

Keywords: Predictive control for linear systems, Optimal control, Linear systems
Abstract: In this paper, we address the problem of constructing a stability-aware model predictive control (MPC) for continuous-time optimal control problem in the sampled-data setting, with which continuous-time constraints of input and state are satisfied. To this end, we first approximate the continuous-time problem in discrete time with fast sampling, from which we derive a sufficient condition on state and input of sampled-data systems for satisfaction on constraints in continuous time. The lifting technique is employed to represent the discrete-time system with the input-hold constraint as an augmented time-invariant model. This conversion allows us to suggest a specific form of the terminal cost and terminal set of the proposed MPC, with which stability and recursive feasibility of the proposed MPC are theoretically guaranteed. Furthermore, we derive sufficient conditions for existence of the terminal cost and for boundedness of the terminal set, by investigating some properties of the linear quadratic regulator for the lifted system.

Keywords: Predictive control for linear systems, Optimization, Multivehicle systems
Abstract: We consider the time-robust control problem for heterogeneous multi-agent systems under global temporal logic tasks. To enable more expressive task formulation, we introduce an extended capability temporal logic plus (ECaTL+), allowing for the specification of how many times an arbitrary signal temporal logic (STL) task needs to be satisfied. Time robustness for ECaTL+ is formally designed and transformed into mixed-integer linear constraints with only logarithmic integer variables with respect to task length being required. During runtime execution, we integrate a self-correction module based on event-triggered model predictive control (MPC) to predict and rectify potential task violations. Simulations are conducted to demonstrate the expressiveness of ECaTL+ and the efficiency of the proposed control synthesis approach.

Keywords: Predictive control for linear systems, Optimization algorithms
Abstract: Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems. To reduce the computational burden of the optimization algorithm, most solvers perform as many offline operations as possible, typically performing the computation and factorization of its expensive matrices offline and then storing them in the embedded system. This improves the efficiency of the solver, with the disadvantage that online changes on some of the ingredients of the MPC formulation require performing these expensive computations online. This article presents an efficient algorithm for the factorization of the key matrix used in several first-order optimization methods applied to linear MPC formulations, allowing its prediction model and cost function matrices to be updated online at the expense of a small computational cost. We show results comparing the proposed approach with other solvers from the literature applied to a linear time-varying system.

Keywords: Constrained control, Stability of nonlinear systems, Lyapunov methods
Abstract: In this paper, we investigate the problem of reference tracking for a class of linear systems subject to actuator saturation and propose a performance-based model recovery anti-windup strategy. We adopt the classic model recovery anti-windup framework and regard the states or one of the outputs of the anti-windup compensator as the tracking error, by which the difference between the unconstrained system and the saturated system is quantified. Then, instead of employing the commonly used L2 gain, we present the prescribed performance functions (PPFs) to characterize the real-time tracking error such that the system performance can be captured more specifically. In particular, to avoid singular issues arising from the occurrence of saturation, we modify the existing PPFs with an auxiliary system when saturation occurs. Based on these modified performance functions, we follow a modified prescribed performance control approach and design the remaining output of the anti-windup compensator. Such a design procedure is constructive, making it more promising for extension to nonlinear systems. Theoretical results establish the boundedness of all signals in the closed-loop system. Simulation results verify the effectiveness of our design strategy.

Keywords: Constrained control, Stability of nonlinear systems, Uncertain systems
Abstract: In this paper, we show that Prescribed Performance Control (PPC) is a model-free Control Barrier Function (CBF)-based control approach. Specifically, we establish that a function utilized in the PPC design is a Time-Varying Reciprocal Control Barrier Function (TVRCBF). We demonstrate that PPC satisfies the same gradient condition that is well-known in the CBF literature, ensuring forward invariance. As a result, the control inputs generated by the PPC law belong to the input set characterized by the TVRCBF. Apart from assuming a certain controllability property, no further knowledge on the system dynamics is required. Our theoretical findings improve the understanding of the relationship between PPC and other CBF-based controllers. The theoretical results are validated through numerical simulations.

Keywords: Constrained control, Stability of nonlinear systems
Abstract: This paper considers the global finite time stabilization of linear systems subject to actuator saturation. We show that a controllable SISO system whose eigenvalues are real and negative can be globally stabilized in finite time by a homogeneous feedback law. To obviate numerical issues, this homogeneous feedback law is implemented by discretizing a compact set. Numerical simulation validates the effectiveness of the proposed approach.

Keywords: Constrained control
Abstract: In this paper, we consider the problem of safe control synthesis for general controlled nonlinear systems in the presence of bounded additive disturbances. Towards this aim, we first construct a governing augmented state space model based on the equations of motion of the original system, an integral control law and a nonlinear disturbance observer. Next, we propose the concept of Disturbance Observer based Integral Control Barrier Functions (DO-ICBFs) which we utilize to synthesize safe control inputs. The characterization of the safe controller is obtained after modifying the governing integral control law with an additive auxiliary control input computed via the solution of a quadratic problem. In contrast to prior methods in the relevant literature which can be unnecessarily cautious due to their reliance on the worst case disturbance estimates, our DO-ICBF based controller uses the available control effort frugally by leveraging the disturbance estimates computed by the disturbance observer. By construction, the proposed DO-ICBF based controller can ensure state and input constraint satisfaction at all times. Further, we propose Higher Order DO-ICBFs that extend our proposed method to nonlinear systems with higher relative degree with respect to the auxiliary control input. Finally, numerical simulations are provided to validate our proposed approach.

Keywords: Chaotic systems, Constrained control
Abstract: This paper presents a systematic control design methodology for the synchronization of a class of chaotic systems configured as the drive and response systems. We achieve state synchronization with a predefined bound on the state errors by exploiting the idea of barrier Lyapunov function and considering parametric uncertainties in the response system. Under a mild assumption on the initial conditions, we prove the asymptotic convergence of state errors to the origin. We also obtain explicit bounds on the state errors and estimated uncertain parameters in the post-design analysis. Simulations are carried out for L"{u} chaotic system to illustrate the adequacy of the proposed controller and the uncertain parametric estimator.

Keywords: Constrained control, Linear systems, Uncertain systems
Abstract: Existing safety control methods for non-stochastic systems become undefined when the system operates outside the maximal robust controlled invariant set (RCIS), making those methods vulnerable to unexpected initial states or unmodeled disturbances. In this work, we propose a novel safety control framework that can work both inside and outside the maximal RCIS, by identifying a worst-case disturbance that can be handled at each state and constructing the control inputs robust to that worst-case disturbance model. We show that such disturbance models and control inputs can be jointly computed by considering an invariance problem for an auxiliary system. Finally, we demonstrate the efficacy of our method both in simulation and in a drone experiment.

Keywords: Predictive control for linear systems, Robust control
Abstract: A Model Predictive Control (MPC) formulation is presented for systems with time-varying disturbances corresponding to an unknown but bounded time-shift of known nominal disturbance trajectories. Specifically, at each time step the true system disturbance is assumed to lie within a bounded time window of the known nominal disturbance trajectory. Through a time-varying constraint tightening approach, the MPC formulation is designed to be robust to a combination of the time-shifted nominal disturbance and a more conventional bounded unknown additive disturbance. Assuming finite-time system operation, a shrinking horizon formulation is presented for when the controller always predicts to the end of the operation, and a receding horizon formulation is presented to enable the use of a shorter prediction horizon. Constraint satisfaction is guaranteed in the receding horizon formulation through the use of wayset-based terminal constraints, which are computed offline using constrained zonotopes to achieve computational efficiency and scalability. Numerical results demonstrate the robustness and low degree of conservatism of the approach along with the computational benefits of a receding horizon formulation using waysets.

Keywords: Learning, Hybrid systems, Aerospace
Abstract: With the increasing electric loads present onboard electric aircraft, electrical components now support safety-critical functions for which component failure may cause catastrophic consequences. Throughout a mission, safety must be considered for power and thermal constraints within the powertrain and for trajectory constraints in the vehicle dynamics. While enforcing comprehensive safety guarantees may be challenging for developmental systems, learning a safe set and using a terminal constraint can be used to guide the system towards known safe behaviors. To provide modularity in the learning rather than learning only a single trajectory, the basic behaviors of the system can be divided into primitives and learning can be conducted for each primitive. While existing learning techniques using a safe set require knowledge of feasible safe trajectories a priori, this paper presents a learning technique which iteratively improves performance through learning an unknown safe set for energy primitives. This paper also presents a reachability technique to ensure safe transitions between primitives which exhibit different dynamics. This technique exploits the structure of the dynamics to minimize branching among hybrid modes. For a simulated case study, the learning model predictive controller results in vehicle safety at all time steps while matching the performance of an unsafe controller. Additionally, the controller demonstrates iterative improvements in performance for a mission where multiple primitives are used.

Keywords: Optimal control, Autonomous systems, Robotics
Abstract: Hamilton-Jacobi reachability analysis is a useful tool for generating reachable sets and corresponding optimal control policies, but its use in high-dimensional systems is hindered by the ``curse of dimensionality." Self-contained subsystem decomposition is a proposed solution, but it can produce conservative or incorrect results due to the ``leaking corner issue." This issue arises from the inexact decomposition of the target set and inconsistencies across the computed control policies for each coupled subsystem. In this paper, we define and resolve this issue by introducing the notion of an admissible control set that enforces consistent control actions across the coupled subsystems. Our method efficiently computes exact reachable sets and the corresponding optimal control policy for self-contained subsystems with a decomposable goal (or failure) set. We also provide conservative under-approximations for goal (or failure) sets with inexact decomposition. In this conservative case, a local update method in the full dimensional space can be applied to recover exact results. We validate our approach on a 3D system and demonstrate its scalability on a 6D system.

Keywords: Neural networks, Machine learning, Stability of nonlinear systems
Abstract: We present a framework based on interval analysis and monotone systems theory to certify and search for forward invariant sets in nonlinear systems with neural network controllers. The framework (i) constructs localized first-order inclusion functions for the closed-loop system using Jacobian bounds and existing neural network verification tools; (ii) builds a dynamical embedding system where its evaluation along a single trajectory directly corresponds with a nested family of hyper-rectangles provably converging to an attractive set of the original system; (iii) utilizes linear transformations to build families of nested paralleletopes with the same properties. The framework is automated in Python using our interval analysis toolbox npinterval, in conjunction with the symbolic arithmetic toolbox sympy, demonstrated on an 8-dimensional leader-follower system.

Keywords: Computational methods, Formal verification/synthesis, Hybrid systems
Abstract: This paper introduces zonoLAB, a MATLAB-based toolbox for set-based control system analysis using the hybrid zonotope set representation. Hybrid zonotopes have proven to be an expressive set representation that can exactly represent the reachable sets of mixed-logical dynamical systems and tightly approximate the reachable sets of nonlinear dynamic systems. Moreover, hybrid zonotopes can exactly represent the continuous piecewise linear control laws associated with model predictive control and the input-output mappings of neural networks with piecewise linear activation functions. The hybrid zonotope set representation is also highly exploitable, where efficient methods developed for mixed-integer linear programming can be directly used for set operation and analysis. The zonoLAB toolbox is designed to make these capabilities accessible to the dynamic systems and controls community, with functionality spanning fundamental operations with hybrid zonotope, constrained zonotope, and zonotope set representations, powerful set analysis tools, and general-purpose algorithms for reachability analysis of open- and closed-loop systems.

Keywords: Computational methods, Large-scale systems, Model/Controller reduction
Abstract: This paper presents a scalable tensor-based approach to computing controllability and observability-type energy functions for nonlinear dynamical systems with polynomial drift and linear input and output maps. Using Kronecker product polynomial expansions, we convert the Hamilton-Jacobi-Bellman partial differential equations for the energy functions into a series of algebraic equations for the coefficients of the energy functions. We derive the specific tensor structure that arises from the Kronecker product representation and analyze the computational complexity to efficiently solve these equations. The convergence and scalability of the proposed energy function computation approach is demonstrated on a nonlinear reaction-diffusion model with cubic drift nonlinearity, for which we compute degree 3 energy function approximations in n=1023 dimensions and degree 4 energy function approximations in n=127 dimensions.

Keywords: Distributed parameter systems, Adaptive systems
Abstract: This paper is concerned with the problem of identification of both failure and repair rates of a multi-state reparable system. The mathematical model of such systems considered in this work is governed by coupled transport and integro- differential equations, which describe the probabilities of the system in good and failure modes. The failure and repair rates are two very important parameters associated with this type of systems, which are not always accessible, yet play critical roles in determining the system behaviors. The objective of this work is to utilize an adaptive observer to identify these parameters. Rigorous analysis on the convergence of the error dynamics is presented.

Keywords: Distributed parameter systems
Abstract: Motivated by control of a delivery unmanned aerial vehicle with a cable-suspended load, with the purpose of avoiding collision of the payload with the surrounding environment, we design safe control for a cascade system consisting of hyperbolic PDEs and ODEs. The considered class of plants is 2 times 2 hyperbolic PDEs sandwiched by a strict-feedback nonlinear ODE and a linear ODE. This is the first safe control design for hyperbolic PDEs, where we apply the backstepping method and introduce the concept of PDE control barrier function (CBF) whose non-negativity as well as the ODE CBF's non-negativity are ensured to achieve the safety goal. The designed controller guarantees: 1) the safety of the state furthermost from the control input; 2) the exponential regulation of the overall plant state to zero. The effectiveness of the proposed method is illustrated by numerical simulation.

Keywords: Flexible structures, Distributed parameter systems, Hybrid systems
Abstract: We consider a linear system of PDEs composed of two Schrödinger equations of arbitrary lengths connected by a point mass interface condition. With boundary control active at one end point, we find that the associated spaces of exact controllability may, or may not, possess an asymmetry in regularity on either side of the point mass. The precise way in which the asymmetry occurs is characterized for the case of rational lengths.

Keywords: Control applications, Model/Controller reduction, Optimal control
Abstract: The tokamak, a viable option for harnessing nuclear fusion energy, employs strong helical magnetic fields to confine a plasma (ionized gas) within a toroidal vacuum chamber. Optimal performance in tokamaks necessitates sophisticated control mechanisms to shape the spatial profiles of specific plasma properties. One such property is the safety factor q, which measures the pitch of the helical magnetic field lines. The dynamics of the q profile in tokamaks depends on the gradient of the poloidal magnetic flux, which is governed by a nonlinear partial differential equation referred to as the magnetic diffusion equation. In this work, model predictive control (MPC) is proposed to regulate the q profile in the EAST tokamak. The finite-horizon optimal control problem (FHOCP) associated with the MPC approach is defined with the goal of minimizing the tracking error between observed and target gradients of the poloidal magnetic flux while satisfying input and state constraints. To address the optimization problem in real time, a simplified model is derived from the magnetic diffusion equation. As a difference from previous efforts in this area, a self-triggered mechanism is implemented within the MPC algorithm to prevent redundant computations arising in fixed sampling-time MPC schemes. Simulation studies show that the proposed controller has the capability of regulating the q profile through the manipulation of the plasma current and the heating and current-drive powers. A comparison with regular fixed-sampling-time MPC methods demonstrates that the proposed self-triggered MPC strategy optimizes performance by avoiding redundant computations and saving computational time.

Keywords: Optimization, Automotive control
Abstract: Vapor compression systems are commonly implemented air conditioning systems in automobiles that must be optimized in order to improve vehicle efficiency and reduce greenhouse gas emissions. This work uses control co-design techniques to simultaneously optimize the plant and control parameters of the system, as opposed to conventional techniques that sequentially optimize these parameters. Control co-design is applied to a dynamic, first-principles-based model of a vapor compression system cooling a car cabin that is controlled by three proportional-integral controllers. A multi-objective optimization problem is formulated to simultaneously optimize the sizing and performance of the system by minimizing component volumes and reducing power consumption. A Pareto curve is provided to demonstrate the trade-offs between these two objectives. Additionally, dynamic simulation results show the optimal designs meet the constraints while improving upon the performance of a design provided by a conventional optimization approach. Control co-design is shown to improve the sizing and performance of the system when compared to a conventional optimization approach.

Keywords: Energy systems, Optimization, Simulation
Abstract: Focusing on a renewable energy application, this work presents a control co-design (CCD) framework to optimize a ducted hydrokinetic turbine. The optimization aims to improve the techno-economic performance of a baseline design with real flow data. A Kriging-based surrogate model is introduced to capture the hydrodynamics of the ducted turbine concept. A reliable assessment of the deployment site requires year-long simulation, which poses a significant computational challenge. A computationally efficient CCD formulation is presented with failure rate and generator efficiency considerations. This formulation incorporates indirect control variables to reduce computational effort. The findings provide evidence of plant-controller coupling within the system, and highlights the conditions associated with it. A case study is undertaken to substantiate and validate the assumptions made in the formulation. The system optimization results in a net 8% techno-economic performance improvement when compared to a sequentially optimized design.

Keywords: Optimization, Uncertain systems, Robust control
Abstract: Control Co-Design (CCD) considers the coupled effects of both plant and control parameters to optimize a system's closed-loop performance during the design stage. This paper presents a new method for CCD with guarantees on robustness to nondeterministic disturbances for all initial conditions within a specified region of operation. This is accomplished by calculating the reachable sets of a candidate closed-loop system directly within the optimization problem. Using this approach, the plant and control parameters are simultaneously chosen to shape these reachable sets to be robustly positive invariant and thus safe for all time. Compared to conventional approaches that perform the optimization for a single initial condition and a given sequence of disturbances, the proposed set-based method avoids sensitivity to variations in the assumed design scenario. As a representative example, the proposed method is applied to an active suspension system.

Keywords: Energy systems, Optimization
Abstract: Design of wave energy converter farms entails multiple domains that are coupled, and thus, their concurrent representation and consideration in early-stage design optimization has the potential to offer new insights and promising solutions with improved performance. Concurrent optimization of physical attributes (e.g., plant) and the control system design is often known as control co-design or CCD. To further improve performance, the layout of the farm must be carefully optimized in order to ensure that constructive effects from hydrodynamic interactions are leveraged, while destructive effects are avoided. The variations in the joint probability distribution of waves, stemming from distinct site locations, affect the farm's performance and can potentially influence decisions regarding optimal plant selection, control strategies, and layout configurations. Therefore, this paper undertakes a concurrent exploration of control co-design and layout optimization for a farm comprising five devices, modeled as heaving cylinders in the frequency domain, situated across four distinct site locations: Pacific Islands, West Coast, East Coast, and Alaskan Coasts. The challenge of efficiently and accurately estimating hydrodynamic coefficients within the optimization loop was mitigated through the application of surrogate modeling and many-body expansion principles. Results indicate the optimized solutions exhibit variations in plant, control, and layout for each candidate site, signifying the importance of system-level design with environmental considerations from the early stages of the design process.

Keywords: Energy systems, Optimization, Power electronics
Abstract: Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, efficiency, and flexibility of the grid. Nevertheless, limited studies have attempted to perform energy storage sizing while including design and operations (i.e., control co-design) for offshore wind farms. As a result, the present work develops a control co-design optimization formulation to optimize multiple objectives and identify Pareto optimal solutions. The graph-based optimization framework is proposed to address the complexity of the system, allowing the optimization problem to be decomposed for large power systems. The IEEE-9 bus system is treated as an onshore AC grid with two offshore wind farms connected via a multi-terminal DC grid for our use case. The developed methodology successfully identifies the Pareto front during the control co-design optimization, enabling decision-makers to select the best compromise solution for multiple objectives.

Keywords: Distributed control, Networked control systems, Network analysis and control
Abstract: This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following. The information flow is directional, so that the underlying communication network topology is represented by a time-varying digraph. We assume that this digraph is connected in an integral sense. This is a much more general assumption than the one currently used in the literature. Under this assumption, it is shown that a distributed coordination controller ensures exponential convergence of the coordination error vector to a neighborhood of zero. The efficacy of the algorithm is confirmed with simulation results.

Keywords: Distributed control, Optimal control
Abstract: The optimal controller design problem for systems equipped with sensors that measure only relative, rather than absolute, quantities is considered. This relative measurement structure is formulated as a design constraint; it is demonstrated that the resulting constrained controller design problem can be written as a convex program. Certain additional network structural constraints can be incorporated into this formulation, making it especially useful in distributed or networked settings. An illustrative example highlights the advantage of the proposed methodology over the standard formulation of the output feedback controller design problem. A numerical example is provided.

Keywords: Distributed control, Power systems, Aerospace
Abstract: To reduce CO2 emissions and tackle increasing fuel costs, the aviation industry is swiftly moving towards the electrification of aircraft. From the viewpoint of systems and control, a key challenge brought by this transition corresponds to the management and safe operation of the propulsion system's onboard electrical power distribution network. In this work, for a series-hybrid-electric propulsion system, we propose a distributed adaptive controller for regulating the voltage of a DC bus that energizes the electricity-based propulsion system. The proposed controller---whose design is based on principles of back-stepping, adaptive, and passivity-based control techniques---also enables the proportional sharing of the electric load among multiple converter-interfaced sources, which reduces the likelihood of over-stressing individual sources. Compared to existing control strategies, our method ensures stable, convergent, and accurate voltage regulation and load sharing, even if the effects of power lines of unknown resistances are considered. The performance of the proposed control scheme is illustrated via numerical simulations of an exemplary propulsion architecture.

Keywords: Distributed control, Predictive control for linear systems, Networked control systems
Abstract: This work is primarily concerned about the distributed control of networked linear time-invariant (LTI) systems with time-varying well-conditioned costs. In particular, we propose a truncated predictive control algorithm based on κ-hop neighbourhoods of the agents in the network. We establish stability and regret bounds for the proposed algorithm, showing that the regret decays exponentially when the temporal prediction horizon k and spatial radius κ increase.

Keywords: Network analysis and control, Distributed control
Abstract: This paper examines the distributed neighbor selection problem for second-order semi-autonomous multi-agent networks. By inheriting the leader-to-follower reachability property encoded in the eigenvector associated with the smallest eigenvalue of perturbed graph Laplacian, this paper shows that the convergence rate of a second-order semi-autonomous network can be enhanced on the reduced network constructed by this eigenvector. Moreover, a quantitative connection between the relative rate of change in velocity of neighboring agents and the corresponding entries in this eigenvector is also established, enabling a distributed neighbor selection algorithm for second-order multi-agent networks. The main results in this paper extend our previous work of distributed neighbor selection algorithm design to multi-agent networks with more complicated agent-level dynamics.

Keywords: Linear systems, Stability of linear systems, Distributed control
Abstract: Given a linear time-invariant (LTI) continuous-time system, we propose strongly stabilizing linear-quadratic regulator (LQR) output feedback designs in this paper. A well-developed literature exists on output feedback strong stabilization. On the one hand, built upon such literature and inspired by the notion of structured sparsity that aims at reducing the information exchange, the structured strongly stabilizing (S-SS) output feedback designs have been developed. On the other hand, based on LQR theory, the structured LQR (S-LQR) output feedback designs have similarly been proposed to preserve the structured sparsity while permitting a reasonable LQR performance degradation. However, to the best of the authors' knowledge, the strongly stabilizing LQR (SS-LQR) output feedback design mixture and its structured variant, i.e., the (S-SS-LQR) output feedback design problem have not been investigated yet. That motivates us to propose SS-LQR output feedback designs via parametric and non-parametric procedures depending on the type of imposed strong stability (in the strict and wide senses, respectively). Furthermore, the non-parametric procedure facilitates proposing the S-SS-LQR output feedback design subject to an imposed sparsity structure. To assess the efficacy of our proposed SS-LQR output feedback designs, we run numerical experiments on various benchmark examples adopted from the literature.

Keywords: Compartmental and Positive systems, Lyapunov methods, Stability of nonlinear systems
Abstract: In this letter, we consider stabilizing control design for a class of positive bilinear systems. Positivity allows us to employ a max-separable function for Lyapunov analysis, enabling to estimate a region of attraction as a box, which is in particular useful, for example, for a heat exchanger. We take the summation of max-separable functions with respect to the state and input as a Lyapunov candidate, yielding a dynamic stabilizing controller. Moreover, employing a quadratic function with respect to the input and integral action of a performance output, we propose another dynamic stabilizing controller which can additionally regulate the output.

Keywords: Fuzzy systems, Stability of nonlinear systems, LMIs
Abstract: This paper addresses the problem of local stability analysis and estimation of the region of attraction for discrete-time Takagi-Sugeno fuzzy systems. A fuzzy-modeling approach is used to obtain a polytopic representation of the membership functions in terms of the premise variables. Unlike other methods available in the literature, this representation eliminates the need for information - in general given in terms of bounds - about the variation of the membership functions. Consequently, the estimation of the domain of attraction becomes a single-parameter minimization problem subject to linear matrix inequality constraints. To demonstrate the effectiveness of the approach, the paper provides numerical examples including a comparison with another method from the literature.

Keywords: LMIs, Lyapunov methods, Stability of nonlinear systems
Abstract: This paper deals with the investigation of an important property that characterizes the detectability of nonlinear systems, namely the incremental Exponential Input/Output-to-State Stability (i-EIOSS). While such a property is easy to check for linear systems, however, for nonlinear systems it is a hard task. On the other hand, the i-EIOSS property is essential and necessary for the development of robust estimators. In this paper, we propose a novel numerical design procedure ensuring the computation of the i-EIOSS-related parameters which are necessary to tune the parameters of the robust estimators. We first introduce a general simple but useful Lyapunov-based method, then we develop new Linear Matrix Inequality (LMI) conditions guaranteeing the computation of the i-EIOSS coefficients. The proposed design method is easily tractable by numerical software and may be used for several real-world applications. Compared to the existing literature, the proposed method is simpler, provides a finite number of LMIs to be solved, and does not need to convert the system into a new one with linear outputs leading to LMI conditions.

Keywords: Nonlinear output feedback, Lyapunov methods, Stability of nonlinear systems
Abstract: This paper addresses the problem of output feedback stabilization for a class of uncertain planar systems with unknown nonlinear structures. The underlying philosophy behind the proposed approach is primarily to revamp/advance the classical lead compensator with an arctangent function-based mechanism that assures bounded control magnitudes, leading to a new design methodology not only conquering the obstruction in constructing state observers but also directing the design of a bounded output feedback stabilizer. Inheriting and leveraging the stability-increasing capability offered by lead compensators, the resultant controller is capable of dealing with systems even suffering from unknown nonlinear structures and measurements concurrently. The strategy presented is further expanded to formulate a bounded output feedback output consensus protocol for uncertain planar two-agent systems with unknown nonlinear heterogeneous dynamics.

Keywords: Network analysis and control, Stability of nonlinear systems, Control of networks
Abstract: We analyze the solutions of networked heterogeneous nonlinear systems under diffusive consensus control. We assume that the individual systems are state strictly semi-passive and the closed-loop interconnected systems form a network with an underlying connected directed graph that contains a directed spanning tree. For these systems, we establish global uniform ultimate boundedness of the solutions. We provide an illustrative example involving a network of Stuart-Landau oscillators.

Keywords: Uncertain systems, Constrained control, Predictive control for linear systems
Abstract: A fully data-driven safe predictive control framework is presented for linear time-invariant (LTI) control systems. While model predictive control (MPC) is widely recognized for its ability to handle operational constraints, ensuring safety through maintaining the system within an invariant set is still an open challenge. In this paper, safety assurance is achieved through the integration of control barrier certificates (CBCs) in MPC. The behavioral systems theory is applied to obviate the system dynamics and consequently represent the MPC-CBC optimization using only input-state measurements. Furthermore, a data-driven maximal safe terminal set is constructed using the sum of squares (SOS) programming, surpassing the conventional sublevel sets of Lyapunov functions. This expansion of the terminal set leads to a significantly enlarged domain of attraction (DoA) for the MPC. The exponential stability and recursive feasibility of the proposed approach are rigorously proved by properly designing the terminal cost and the terminal set constraint. Finally, a numerical example is provided to illustrate the efficacy of the proposed MPC approach.

Keywords: Uncertain systems, Direct adaptive control, Maritime control
Abstract: This work deals with the design and implementation of a novel output feedback position tracking controller for marine vessels subject to uncertainties in their dynamical parameters and periodic external disturbance. Specifically, an adaptive controller that does not make use of velocity measurements has been presented which can compensate for uncertainties in the systems dynamical parameters and periodic external disturbance. A filtered based velocity surrogate formulation in conjunction with a periodic noise estimator and a desired model compensation based adaptive parameter estimator have been utilized to tackle the problem. Boundedness of the closed loop system and convergence of the position tracking error to the origin are proven via Lyapunov–type arguments. Comparative numerical simulations are presented to illustrate the effectiveness of the proposed controller.

Keywords: Uncertain systems, Distributed control, Adaptive control
Abstract: In this paper, we design a novel distributed adaptive controller that utilizes the hedging-based reference model and command governor mechanism to provide stability guarantees and improved transient response for the overall uncertain multi-agent system in the presence of actuator dynamics and unknown actuation capabilities. Specifically, the hedging-based reference models are used for each agent that enables correct adaptation unaffected by actuator dynamics; this alters the trajectories of the ideal reference model. In addition, the command governor mechanisms are used, which are available only to leader agent(s) that modify a given command’s trajectory to capture the desired overall multi-agent dynamical system behavior in transient time. We investigate closed-loop system stability conditions using Lyapunov Theory and matrix mathematics and calculate stability boundaries regarding the actuation bandwidth using Linear Matrix Inequalities solutions. Moreover, we prove the asymptotic stability of the tracking error, which involves the difference between the states of the uncertain agent system and the hedged-based reference model. We then prove the command governor signal converges to zero. Finally, the performance of the proposed control algorithm is shown with a numerical example on a line graph with a leader-follower setting, and comparison results of the transient response with and without the command governor mechanism are given.

Keywords: Uncertain systems, Human-in-the-loop control, Robust control
Abstract: Generating safe behaviors for autonomous systems is important as they continue to be deployed in the real world, especially around people. In this work, we focus on developing a novel safe controller for systems where there are multiple sources of uncertainty. We formulate a novel multimodal safe control method, called the Multimodal Safe Set Algorithm (MMSSA) for the case where the agent has uncertainty over which discrete mode the system is in, and each mode itself contains additional uncertainty. To our knowledge, this is the first energy-function-based safe control method applied to systems with multimodal uncertainty. We apply our controller to a simulated human-robot interaction where the robot is uncertain of the human's true intention and each potential intention has its own additional uncertainty associated with it, since the human is not a perfectly rational actor. We compare our proposed safe controller to existing safe control methods and find that it does not impede the system performance (i.e. efficiency) while also improving the safety of the system.

Keywords: Uncertain systems, Linear systems, Modeling
Abstract: We present a semi-analytical method for exact computation of the boundary of the reach set of a single-input controllable linear time invariant (LTI) system with given bounds on its input range. In doing so, we deduce a parametric formula for the boundary of the reach set of an integrator linear system with time-varying bounded input. This formula generalizes recent results on the geometry of an integrator reach set with time-invariant bounded input. We show that the same ideas allow for computing the volume of the LTI reach set.

Keywords: Intelligent systems, Iterative learning control, Game theory
Abstract: This paper presents a model-free reinforcement learning approach to obtain the optimal capture strategy in the pursuit-evasion (PE) game. We present the necessary condition for successful capture and converge to the optimal solution to achieve Nash equilibrium in the game through online policy iteration, without the prior knowledge of pursuers’ system dynamics. The multiplayer situation is considered and employ a bipartite graph framework to describe the game performance index. A maximum matching algorithm is utilized to minimize associated cost. Through simulation experiments, we demonstrate the effectiveness of the designed control strategy.

Keywords: Autonomous robots
Abstract: We revisit the Safety Velocity Cones (SVCs) obstacle avoidance approach for real-time autonomous navigation in an unknown n-dimensional environment. We propose a locally Lipschitz continuous implementation of the SVC controller using the distance-to-the-obstacle function and its gradient. We then show that the proposed implementation guarantees safe navigation in generic environments and almost globally asymptotic stability (AGAS) of the desired destination when the workspace contains strongly convex obstacles. The proposed computationally efficient control algorithm can be implemented onboard vehicles equipped with limited range sensors (e.g., LiDAR, depth camera), allowing the controller to be locally evaluated without requiring prior knowledge of the environment.

Keywords: Sensor fusion, Sensor networks, Robotics
Abstract: Autonomous localization of Wheeled Mobile Robots (WMRs) in challenging extraterrestrial environments greatly depends on wheel slip estimation. In this paper, we propose a novel sequential track-to-track fusion for multi-sensor networks of unscented Kalman filters that has immediate application to wheel slip estimation of WMRs. Compared to current fusion techniques, the algorithm exhibits enhanced consistency through the propagation and utilization of cross-correlations, as well as improved computational efficiency via the implementation of a sequential scheme for fusing local estimates. As a case study, the slip estimation in a six-wheel planetary WMR with purely proprioceptive sensors is considered, where the steerable wheel sets form a network of sensors. We compare the novel slip estimator’s performance with rival methods in a high-fidelity software-in-the-loop simulation and demonstrate its ability to achieve a balance between consistency, accuracy, and speed in real-time applications.

Keywords: Quantized systems, Estimation, Sensor fusion
Abstract: We propose a novel method for achieving the average consensus in a distributed manner while dealing with communication compression. While it is widely recognized that distributed consensus algorithms with compression can falter due to compression-error-induced divergences, our approach integrates an error correction step to guarantee convergence towards an approximate average consensus across any bounded compression function. Significantly, with our error correction mechanism, we can achieve convergence to a solution with arbitrarily high accuracy, regardless of how crude the compression is in a fully distributed setting. Additionally, we quantify the convergence rate and provide upper bounds for the estimation error based on the spectral properties of the underlying communication network. Simulation results validate the scalability and efficacy of our proposed algorithm.

Keywords: Randomized algorithms, Kalman filtering, Estimation
Abstract: In this work, we address the problem of sensor selection for state estimation via Kalman filtering. We consider a linear time-invariant (LTI) dynamical system subject to process and measurement noise, where the sensors we use to perform state estimation are randomly drawn according to a sampling with replacement policy. Since our selection of sensors is randomly chosen, the estimation error covariance of the Kalman filter is also a stochastic quantity. Fortunately, concentration inequalities (CIs) for the estimation error covariance allow us to gauge the estimation performance we intend to achieve when our sensors are randomly drawn with replacement. To obtain a non-trivial improvement on existing CIs for the estimation error covariance, we first present novel matrix CIs for a sum of independent and identically-distributed (i.i.d.) and positive semi-definite (p.s.d.) random matrices, whose support is finite. Next, we show that our guarantees generalize an existing matrix CI. Also, we show that our generalized guarantees require significantly fewer number of sampled sensors to be applicable. Lastly, we show through a numerical study that our guarantees significantly outperform existing ones in terms of their ability to bound (in the semi-definite sense) the steady-state estimation error covariance of the Kalman filter.

Keywords: Control over communications, Networked control systems, Communication networks
Abstract: Many modern controllers are composed of different components that communicate in real-time over some network with limited resources. In this work, we are interested in designing a controller that can be implemented with a minimum number of sensor-to-actuator messages, while satisfying safety constraints over a finite horizon. For finite horizon problems, a linear time-varying controller with memory can be represented as a block-lower-triangular matrix. We show that the rank of this matrix exactly captures the minimum number of messages needed to be sent from the sensors to actuators to implement such a controller. Moreover, we introduce a novel matrix factorization called causal factorization that gives the required implementation. Finally, we show that the rank of the controller is the same as the rank of the Youla parameter, enabling the Youla parametrization (or analogous parametrizations) to be used to design the controller, which reduces the overall design problem into a rank minimization one over a convex set. Finally, convex relaxations for rank are used to demonstrate that our approach leads to 20-50% less messages on a simulation than a benchmark method.

Keywords: Reduced order modeling, Nonlinear systems identification
Abstract: This paper presents a timescale separation method for realization-preserving reduced-order modeling of dynamic systems. While classical singular perturbation theory can be used to separate fast and slow states of multi-timescale systems in a standardized form, many real-world systems do not follow this form. Alternatively, geometric singular perturbation theory admits a more general nonstandard form, however it mainly focuses on analyzing the system dynamics in a transformed state space, which is not realization-preserving. Furthermore, existing methods typically assume that the locations and values of small parameters used to form the perturbed system are known, however for complex systems this may not be the case. The proposed approach integrates a data-driven method with singular perturbation theory to achieve timescale separation of multi-timescale systems without assuming prior knowledge of the small parameters. Furthermore, a sparsity-promoting data-driven approach allows the relative timescale of each state to be characterized, facilitating separation of systems with more than two timescales. Numerical examples illustrate the efficacy and computational efficiency of the proposed approach.

Keywords: Reduced order modeling, Statistical learning
Abstract: We consider the problem of estimating the subset of test conditions under which a simplified model---or set of simplified models---accurately approximates the behavior of a true system. We approach the problem by proposing a compact set of possible test conditions, and an unknown but samplable continous validity function over that set that quantifies the accuracy of the model under each possible condition. We propose a novel Bayes estimator that optimally directs function sampling to greedily minimize the expected posterior misclassification rate of the valid set, which we call minimum posterior misclassification sampling (GP-MPM), and we show that the the method can be extended to approximate the valid sets of a partially ordered set of models, with sample complexity growing sublinearly with the number of models. In testing against a safety-focused model, we show that the algorithm's estimated valid set approaches the true valid set much more quickly than undirected sampling, even with small sample sizes.

Keywords: Simulation, Markov processes, Optimization algorithms
Abstract: In Metropolis Hastings methods, gradient-based proposals, such as Metropolis-Adjusted Langevin Algorithm (MALA), are widely used due to the fast convergence in distribution compared with non-gradient-based sampling methods like random walk proposals. On the other hand, the application of MALA is constrained by the accessibility of gradients. To extend the application scenario of MALA, we propose to use approximated gradients in MALA and name the algorithm MALA-SPSA. We prove the mixing time of MALA-SPSA to prove its efficiency in theory. Numerical experiments are conducted to verify the performance of MALA-SPSA.

Keywords: Reduced order modeling, Linear systems, Numerical algorithms
Abstract: We consider the Adaptive Antoulas-Anderson (AAA) rational interpolation algorithm recently developed by Trefethen and co-authors, which is a type of moment- matching technique for SISO system realization and model reduction. We consider variations on this algorithm that are suitable for MIMO systems. In particular, we develop state- space formulas and keep track of the state-space dimension at every step of the adaptive block-AAA algorithm, showing an unfavorable increase of the state dimension. We propose a new low-rank adaptive interpolation algorithm that addresses this shortcoming. Comparative computational results are included for the algorithms above, together with comparisons to balanced truncation.

Keywords: Subspace methods, Linear systems, Numerical algorithms
Abstract: It is well known that zeros and poles of a single-input, single-output system in the transfer function form are the roots of the transfer function’s numerator and the denominator polynomial, respectively. However, in the state-space form, where the poles are a subset of the eigenvalue of the dynamics matrix and thus can be computed by solving an eigenvalue problem, the computation of zeros is a non-trivial problem. This paper presents a realization of a linear system that allows the computation of invariant zeros by solving a simple eigenvalue problem. The result is valid for square multi-input, multi-output (MIMO) systems, is unaffected by lack of observability or controllability, and is easily extended to wide MIMO systems. Finally, the paper illuminates the connection between the zero-subspace form and the normal form to conclude that zeros are the poles of the system’s zero dynamics.

Keywords: Agents-based systems, Estimation, Optimization
Abstract: Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign

Keywords: Agents-based systems, Game theory, Optimal control
Abstract: This paper investigates a partial-information pursuit evasion game in which the Pursuer has a limited-range sensor to detect the Evader. Given a fixed final time, we derive the optimal evasion strategy for the Evader to maximize its distance from the pursuer at the end. Our analysis reveals that in certain parametric regimes, the optimal Evasion strategy involves a ‘risky’ maneuver, where the Evader’s trajectory comes extremely close to the pursuer’s sensing boundary before moving behind the Pursuer. Additionally, we explore a special case in which the Pursuer can choose the final time. In this scenario, we determine a (Nash) equilibrium pair for both the final time and the evasion strategy.

Keywords: Agents-based systems, Kalman filtering, Uncertain systems
Abstract: This paper presents a probabilistic model-based approach to centralized multi-agent trajectory planning. This approach allows for incorporating uncertainty of the state and dynamics of the agents directly in the model. Probabilistic inference is then efficiently automated using message passing. The recently introduced normal-with-unknown-variance (NUV) priors are used to prevent collisions between agents and obstacles. Furthermore, a new expectation-maximization inference scheme is derived for box and half-space NUV priors, which takes state uncertainty into account when avoiding collisions.

Keywords: Agents-based systems, Stochastic systems
Abstract: Opinion dynamics models study how the opinions of individuals evolve in social settings. An important aspect often of this is social pressure, in which an individual feels pressure to conform her expressed opinions to the opinions of those around her, even against her true beliefs. This work studies an interacting Polya urn model for opinion dynamics under social pressure, originally proposed in Jadbabaie et al. In this paper, we consider the behavior of this model on random graphs. Previous work has shown conditions for when the agents on the network approach consensus, in which all the agents asymptotically express the same opinion over time, even if this opinion is contrary to some of their true beliefs; however these conditions are not interpreted as explicit graph properties or characteristics. In this work, we bridge this gap by examining what kinds of basic network properties determine whether the network approaches consensus. We show that when the agents’ network structure is a random graph, homophily, the tendency for agents to be connected to those more similar to themselves, diminishes the likelihood of consensus to occur. This result gives insight on how network characteristics affect the possibility of consensus.

Keywords: Agents-based systems, Observers for nonlinear systems, Uncertain systems
Abstract: In this paper, we investigate the design of a resilient consensus observer that yields correct and continuous state estimates of a nonlinear control system whose state observations are subject to attacks. The observations are assumed to be obtained from multiple sensors and/or communication channels employed to provide redundancy. It is shown that, in an attack-free setting, individual globally convergent observers can be designed for each sensor/channel using the corresponding state-dependent Jacobian system. To counter attacks, a resilient consensus observer is then designed by merging the observers estimates to produce a smooth consensus estimate. Resilient convergence of the consensus estimate to the true state is ensured under the condition that the sparsity of the attacks being less than a half of the number of redundant channels. As an illustrative example, the proposed scheme is successfully applied to the chaotic circuit synchronization problem in which their synchronization has been attacked.

Keywords: Iterative learning control
Abstract: Application of learning to collaborative tracking control of multi-agent systems has addressed a wealth of problems across transportation, manufacturing, rescue, aerospace and medical care areas. Iterative learning control algorithms have been proposed to address synergistic objectives in general optimization problems, achieving a transparent balance between convergence speed, tracking error and robustness. This paper builds on this framework by formulating a point-to-point strategy that allows each subsystem to track only a portion of the trajectory, thereby providing a more flexible design framework with broad utility. Moreover, a channel tracking strategy is developed to ensure that the total output during untracked intervals is limited to an a specified range. The practicality of this novel control framework is illustrated through derivation, simulation and evaluation of three new iterative learning laws: inverse, gradient and norm-optimal. Convergence analysis for the proposed framework is also given.

Keywords: Game theory
Abstract: In this paper, we address the challenge of Nash equilibrium (NE) seeking in non-cooperative convex games with partial-decision information. We propose a distributed algorithm, where each agent refines its strategy through projected-gradient steps and an averaging procedure. Each agent uses estimates of competitors' actions obtained solely from local neighbor interactions, in a directed communication network. Unlike previous approaches that rely on (strong) monotonicity assumptions, this work establishes the convergence towards a NE under a diagonal dominance property of the pseudo-gradient mapping, that can be checked locally by the agents. Further, this condition is physically interpretable and of relevance for many applications, as it suggests that an agent's objective function is primarily influenced by its individual strategic decisions, rather than by the actions of its competitors. In virtue of a novel block-infinity norm convergence argument, we provide explicit bounds for constant step-size that are independent of the communication structure, and can be computed in a totally decentralized way. Numerical simulations on an optical network's power control problem validate the algorithm's effectiveness.

Keywords: Agents-based systems, Estimation
Abstract: A common feature of interactions and opinion exchanges on social networks, both real and digital, is the presence of social pressure, in which agents alter their expressed opinions in order to fit in with the expressed opinions of those around them. In such systems, each agent has a true and unchanging inherent belief but broadcasts a declared opinion at each time step, influenced by both her inherent belief and the declared opinions of her neighbors. An important question in this setting is parameter estimation: how to disentangle the effects of social pressure and estimate the underlying true beliefs of the agents from their declared opinions. To address this question, Jadbabaie et al. formulated the interacting Polya urn model of opinions under social pressure and studied parameter estimation using an aggregate estimator when the social network is a complete graph, which asymptotically estimates the true beliefs unless majority pressure causes the network to approach consensus over time. In this work, we show that the maximum likelihood estimator on the interacting Polya urn model on arbitrary networks always asymptotically estimates the true beliefs - including the degree to which that belief is held - even when consensus is approached.

Keywords: Algebraic/geometric methods, Game theory, Networked control systems
Abstract: Preferences, fundamental in all forms of strategic behavior and collective decision-making, in their raw form, are an abstract ordering on a set of alternatives. Agents, we assume, revise their preferences as they gain more information about other agents. Exploiting the ordered algebraic structure of preferences, we introduce a message-passing algorithm for heterogeneous agents distributed over a network to update their preferences based on aggregations of the preferences of their neighbors in a graph. We demonstrate the existence of equilibrium points of the resulting global dynamical system of local preference updates and provide a sufficient condition for trajectories to converge to equilibria: stable preferences. Finally, we present numerical simulations demonstrating our preliminary results.

Keywords: Cooperative control, Distributed control, Agents-based systems
Abstract: The paper proposes a heterogeneous push-sum based subgradient algorithm for multi-agent distributed convex optimization, in which each agent can arbitrarily switch between subgradient-push and push-subgradient at any time. It is shown that the heterogeneous algorithm converges to an optimal point at an optimal rate over time-varying directed graphs. The switching process within the heterogeneous algorithm can help prevent the leakage of agents' subgradient information.

Keywords: Estimation, Optimization, Distributed parameter systems
Abstract: We consider the problem of enabling a network of agents to estimate the state of a discrete-time nonlinear dynamical system. At each time step, each agent in the network receives a measurement characterized by a nonlinear function of the system state and exchanges information with its neighbors in the network. We propose an optimization-based estimator where agents collaboratively solve a distributed optimization problem while satisfying a communication constraint in the form of a fixed number of distributed optimization iterations at each estimation time step. Subject to the assumptions that the system is collectively observable, and the communication network is time-varying and strongly connected, we show that for any given lambda which satisfies 0

Keywords: Networked control systems, Communication networks, Distributed control
Abstract: In this paper, we consider a multi-agent system where agents aim to achieve a consensus in spite of interactions with malicious agents that communicate misleading information. Physical channels supporting communication in cyberphysical systems offer attractive opportunities to detect malicious agents: however, trustworthiness indications coming from the channel are subject to uncertainty and need to be treated with this in mind. We propose a resilient consensus protocol that incorporates trust observations from the channel and weighs them with a parameter that accounts for how confident an agent is regarding its understanding of the legitimacy of other agents in the network, with no need for the initial observation window T0 that has been utilized in previous works. Analytical and numerical results show that (i) our protocol achieves a resilient consensus in the presence of malicious agents and (ii) the steady- state deviation from nominal consensus can be minimized by a suitable choice of the confidence parameter that depends on the statistics of trust observations.

Keywords: Robotics, Biologically-inspired methods, Hybrid systems
Abstract: Drawing inspiration from human multi-domain walking, this work presents a novel reduced-order model based framework for realizing multi-domain robotic walking. At the core of our approach is the viewpoint that human walking can be represented by a hybrid dynamical system, with continuous phases that are fully-actuated, under-actuated, and over-actuated and discrete changes in actuation type occurring with changes in contact. Leveraging this perspective, we synthesize a multi-domain linear inverted pendulum (MLIP) model of locomotion. Utilizing the step-to-step dynamics of the MLIP model, we successfully demonstrate multi-domain walking behaviors on the bipedal robot Cassie---a high degree of freedom 3D bipedal robot. Thus, we show the ability to bridge the gap between multi-domain reduced order models and full-order multi-contact locomotion. Additionally, our results showcase the ability of the proposed method to achieve versatile speed-tracking performance and robust push recovery behaviors.

Keywords: Robotics, Biologically-inspired methods, Simulation
Abstract: Inspired by Chukars wing-assisted incline running (WAIR), in this work, we employ a high-fidelity model of our Husky Carbon quadrupedal-legged robot to walk over steep slopes of up to 45 degrees. Chukars use the aerodynamic forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and even overhangs. By exploiting the thrusters on Husky, we employed a collocation approach to rapidly resolving the joint and thruster actions. Our approach uses a polynomial approximation of the reduced-order dynamics of Husky, called HROM, to quickly and efficiently find optimal control actions that permit high-slope walking without violating friction cone conditions.

Keywords: Robotics, Lyapunov methods, Stability of nonlinear systems
Abstract: The locomotion dynamics of a snake robot are greatly influenced by the way it interacts with its surroundings, particularly in terms of friction. Given the variable frictional characteristics of the environments in which holonomic snake robots locomote, this paper introduces a novel friction-adaptive body-shape controller that dynamically adjusts the robot’s locomotion parameters in response to changes in friction. One of this paper’s contributions is based on deriving a novel linear regression-based representation of the ground friction forces acting on the snake robot, which results in coupled dynamics between the gait tracking and friction parameter estimation errors. Using these coupled dynamics and our proposed friction adaptation law, we prove practical stability of the gait tracking and friction parameter estimation errors in the general case, as well as asymptotic convergence of the gait tracking errors and boundedness of the friction parameter estimation errors in a special case. Simulation results show that the proposed controller is stable and that the gait tracking error and friction parameter estimation errors are converging and bounded.

Keywords: Robotics, Mechanical systems/robotics, Feedback linearization
Abstract: This paper considers the motion control of a multirotor slung load system (SLS) which is capable of tracking time-varying payload reference position trajectories. The method applies a quasi-static feedback (QSF) linearization to obtain linear tracking error dynamics for the outer-loop. QSF has the practical benefit of simple static dependence on state and reference input. Further, LTI error dynamics simplify gain tuning and the stability proof. Accurate software-in-the-loop (SITL) simulation and flight tests validate tracking performance. Open-source software and hardware are used in the experiments, and source code is available.

Keywords: Robotics, Mechanical systems/robotics, Machine learning
Abstract: This paper presents a Reinforcement Learning-based path-planning algorithm with included obstacle avoidance that is used for a stationary three-degree-of-freedom robot. Therefore, the actor-critic algorithms Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) are combined with Prioritized Experience Replay (PER) and tested in simulation. Further, investigations regarding different exploration strategies and network shapes are conducted. The results show that especially the combination of TD3 with PER offers a solid approach for complex path-planning in the continuous domain of the spatial three-degree-of-freedom robot. The performance could then be boosted, additionally, by enlarging the utilised feedforward-neural networks and more sophisticated exploration strategies.

Keywords: Robotics, Optimization algorithms, Machine learning
Abstract: In this work, we present to the best of our knowledge, the first Deep Reinforcement Learning (DRL) based 2D pose-graph optimization (PGO). We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Markov Decision Process. The proposed agent outperforms state-of-the-art solver g2o on challenging instances where traditional nonlinear least-squares techniques may fail or converge to unsatisfactory solutions. Experimental results indicate that iterative-based solvers bootstrapped with the proposed approach allow for significantly higher quality estimations.

Keywords: Nonlinear systems identification, Estimation, Learning
Abstract: This paper derives a new condition to ensure that a selected reproducing kernel Hilbert space is invariant under the action of the Koopman operator associated with a generally nonlinear system in discrete time. If the function that determines the dynamics is continuous over an invariant set supporting the dynamics, and the reproducing kernel that defines the space of observables is uniformly bounded above and below by positive constants over the invariant set, then the native space that contains the observables is invariant under the Koopman operator. This condition is used to derive error bounds for approximations of the Koopman operator in the strong operator topology. These bounds are explicit in the number of snapshots and reduced dimension of the subspace used to construct estimates. Numerical examples illustrate the qualitative behavior of the error bounds.

Keywords: Nonlinear systems identification, Identification
Abstract: Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.

Keywords: Nonlinear systems identification, Machine learning, Robust adaptive control
Abstract: A deep neural network (DNN)-based adaptive controller with a real-time and concurrent learning (CL)-based adaptive update law is developed for a class of uncertain, nonlinear dynamic systems. The DNN in the control law is used to approximate the uncertain nonlinear dynamic model. The inner-layer weights of the DNN are updated offline using data collected in real-time; whereas, the output-layer DNN weights are updated online (i.e., in real-time) using the Lyapunov- and CL-based adaptation law. Specifically, the inner-layer weights of the DNN are trained offline (concurrent to real-time execution) after a sufficient amount of data is collected in real-time to improve the performance of the system, and after training is completed the inner-layer DNN weights are updated in batch-updates. The key development in this work is that the output-layer DNN update law is augmented with CL-based terms to ensure that the output-layer DNN weight estimates converge to within a ball of their optimal values. A Lyapunov-based stability analysis is performed to ensure semi-global exponential convergence to an ultimate bound for the trajectory tracking errors and the output-layer DNN weight estimation errors.

Keywords: Closed-loop identification, Identification, Nonlinear systems identification
Abstract: This paper considers output-only identification for discrete-time hysteretic Lur’e (DTHL) systems, which are Lur’e systems with hysteretic feedback. The discrete-time model used to identify DTHL systems consists of asymptotically stable linear dynamics, a time delay, a washout filter, and a hysteretic nonlinear feedback mapping. A nonlinear least-squares identification algorithm is developed for DTHL systems as an extension of an existing technique for identifying Lur’e systems with memoryless nonlinearity feedback. Numerical examples are given to show the effectiveness of this technique for identifying DTHL systems.

Keywords: Nonlinear systems identification, Machine learning
Abstract: Recent advances in learning or identification of nonlinear dynamics focus on learning a suitable model within a pre-specified model class. However, a key difficulty that remains is the choice of the model class from which the dynamics will be learned. The fundamental challenge is trading the richness of the model class with the learnability within the model class. Toward addressing the so-called model selection problem, we introduce a novel notion of Structural Risk Minimization (SRM) for learning nonlinear dynamics. Inspired by classical SRM for classification, we minimize a bound on the true prediction error over hierarchies of model classes. The class selected by our SRM scheme is shown to achieve a nearly optimal learning guarantee among all model classes contained in the hierarchy. Employing the proposed scheme along with computable model class complexity bounds, we derive explicit SRM schemes for learning nonlinear dynamics under hierarchies of: i) norm-constrained Reproducing Kernel Hilbert Spaces, and ii) norm-constrained Neural Network classes. We empirically show that even though too loose to be used as absolute estimates of the true prediction error, our SRM bounds are able to track its relative behavior across different model classes of the hierarchy.

Keywords: Manufacturing systems, Iterative learning control, Process Control
Abstract: Aiming at the problem of unstable control and low precision of key variables in the repeated operation of Czochralski silicon single crystal (Cz-SSC), this paper proposes a data-driven active disturbance rejection learning control (ADRLC) method based on iterative extended state observer (ESO). Firstly, the iterative dynamic linearization method transform the Cz-SSC growth system into an affine form, and the system uncertainty including disturbance is merged into a total term. Then, by designing ESO for iterative estimation of the nonlinear uncertainty. Finally, based on the ADRC strategy, an ADRLC controller with iterative parameter updating is designed and the convergence of tracking control error is proved theoretically. The entire learning control scheme does not require additional model information, except for the input and output data of the system. In addition, the effectiveness of the method is verified by the batch control results of crystal diameter. Compared with the traditional iterative learning control method, the proposed ADRLC method can estimate the uncertainty of the system along the iterative axis, and overcome the disturbance through the ADRLC controller to obtain accurate crystal diameter variable batch control results.

Keywords: Optimization, Uncertain systems, Optimization algorithms
Abstract: This paper introduces a novel approach to concurrently design dynamic controllers and correlated differential privacy noise in dynamic control systems. An increase in privacy noise increases the system's privacy but adversely affects the system's performance. Our approach optimizes the noise distribution while shaping closed-loop system dynamics such that the privacy noise has the least impact on system performance and the most effect on system privacy. We further add privacy noise to both control input and system output to privatize the system's state for an adversary with access to both communication channels and direct output measurements. The study also suggests tailored privacy bounds for different states, providing a comprehensive framework for jointly optimizing system performance and privacy in the context of differential privacy.

Keywords: Optimization, Optimization algorithms
Abstract: Trading off exploration and exploitation is a crucial task in global (or black-box) optimization, to balance the search for better local optimizers with the refinement of already-found ones. Often, such a trade-off is not easily controlled by the user, as it depends non-trivially on the tuning parameters of the selected algorithm. A new concept is proposed here, where the share of exploitation moves over the total number of iterations is regulated by a feedback control law, to achieve a user-defined set-point. This concept is applied to the recently proposed Set Membership Global Optimization (SMGO) technique, resulting in a modified algorithm. Additional computational improvements are presented as well, and the resulting approach is extensively tested and compared with other methods. The statistical tests indicate that the new algorithm has better iteration-based optimization performance than the original one, at the same time shortening the computational times by around one order of magnitude.

Keywords: Optimization, Output regulation, Optimal control
Abstract: This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem. To solve this regulation task, we design a state feedback controller based on a continuous approximation of the projected gradient flow. We first show that the equilibria of the interconnection between the plant and the proposed controller correspond to critical points of the constrained optimization problem. We then derive sufficient conditions to ensure that, for the closed-loop system, isolated locally optimal solutions of the optimization problem are locally exponentially stable and show that input constraints are satisfied at all times by identifying an appropriate forward-invariant set. Simulations illustrate our results.

Keywords: Optimization, Power systems, Smart grid
Abstract: In this work, we develop a novel data-driven Bayesian nonparametric Wasserstein distributionally robust optimization (BNWDRO) framework for decision-making under uncertainty. The proposed framework unifies a Bayesian nonparametric method and the Wasserstein metric to decipher the global-local features of uncertainty data and encode these features into a novel data-driven ambiguity set. By establishing the theoretical connection between this data-driven ambiguity set and the conventional Wasserstein ambiguity set, we prove that the proposed framework enjoys the finite sample guarantee and asymptotic consistency. To efficiently solve the resulting distributionally robust optimization problem under the BNWDRO framework, we derive for this optimization problem an equivalent reformulation, which is kept tractable for many practical scenarios. Numerical experiments on a unit commitment problem verify the effectiveness of the proposed BNWDRO framework compared with existing methods.

Keywords: Optimization, Time-varying systems
Abstract: This paper introduces extremum seeking (ES) algorithms designed to achieve perfect tracking of arbitrary time-varying extremum. In contrast to classical ES approaches that employ constant frequencies and controller gains, our algorithms leverage time-varying parameters, growing either asymptotically or exponentially, to achieve desired convergence behaviors. Our stability analysis involves state transformation, time-dilation transformation, and Lie-bracket averaging. The state transformation is based on the multiplication of the input state by asymptotic or exponential growth functions. The time transformation enables tracking of the extremum as it gradually converges to a constant value when viewed in the dilated time domain. Finally, Lie-bracket averaging is applied to the transformed system, ensuring practical uniform stability in the dilated time domain as well as asymptotic or exponential stability of the original system in the original time domain. We validate the feasibility of these designs through numerical simulations.

Keywords: Power electronics, Energy systems, Control applications
Abstract: This study introduces a control structure designed to enhance the reliability and scalability of parallel-operated solar-battery inverter systems. While parallel-operated inverters offer numerous benefits, they encounter challenges and limitations in practical settings, including power sharing inaccuracies, complex synchronization mechanisms, and the need for transformers. In response to these challenges, this research presents a control scheme that integrates three independently modulated controllers tailored for a double-staged theta−converter topology. This topology not only eliminates the need for transformers but also reduces leakage currents and component count. The control approaches are optimized for three main objectives: (1) maximizing solar power delivery while reducing harmonics; (2) ensuring DC bus stability while minimizing oscillations; and (3) employing a self-synchronized universal droop control for accurate power sharing. This control methodology is equipped with black-start and self-synchronization capabilities while maintaining tight voltage and frequency regulations. Experimental field tests were conducted on two portable solar-battery platforms, validating off-grid operation for parallel units and highlighting its suitability for the increasing demands of solar energy applications.

Keywords: Power electronics, Observers for Linear systems, Robust control
Abstract: In LCL-type grid-tied inverters, the lumped disturbance, including the parameter uncertainties, unmodeled dynamics, the power grid, etc., may deteriorate the performance of the current injected into the grid. In this paper, a resonant internal model (RIM) molding scheme based on the extended state observer (ESO), the major part of the active disturbance rejection control (ADRC), is proposed. The proposed strategy utilizes the ESO to obtain estimated complete state and disturbance, then compensates for the disturbance and remolds the plant with estimated variables into a new form containing an expected internal model, which provides the disturbance attenuation capability and satisfactory tracking performance for the reference. The mathematical model of passive damping LCL filter with damping resistor and capacitor in series is established, and the design process of RIM scheme is introduced in detail. The effectiveness of the proposed scheme has been tested with a 2-kW experimental prototype.

Keywords: Power electronics, Smart grid, Power systems
Abstract: This article introduces two control frameworks: one for Grid-Following (GFL) inverters aiding Grid-Forming (GFM) inverters in voltage regulation during large contingency events and optimizing power transactions under normal conditions; and another for seamless transitions between grid-tied and grid-isolated setups, managing voltage transient characteristics. In microgrids, GFM inverters regulate voltage, while GFL inverters handle power transactions. The proposed GFL control detects abrupt load/generation changes, adjusting power transactions using local storage to support GFM inverters during contingencies. Additionally, a transition control ensures smooth GFL-GFM shifts, reducing power and voltage fluctuations. Simulation results validate improved voltage regulation during contingencies and enhanced power tracking during slow changes, alongside minimized transient overshoot.

Keywords: Power systems, Energy systems
Abstract: This paper studies the long-term distributed solar panel investment equilibrium driven by the solar panels investors' expected payoffs derived from participating in different short-term solar energy markets. To this end, we consider three different short-term solar market mechanisms, namely, (a) single-product real-time energy market, (b) product-differentiated real-time energy market, and (c) contract-based panel market. We derive expressions for the equilibrium price and expected return of solar panel investors under all these three markets. We then connect these short-term market equilibria with the long-term panel investment game where individual investors determine whether to invest in solar panels by trading off the capacity cost with the expected payoff from the panel investment. Interestingly, we establish that the single-product real-time energy market consistently leads to under-investment compared to product-differentiated real-time energy market, where the latter is shown to support social welfare. We also prove that the contract-based market leads to over-investment in a limiting parameter region where the extra valuations of users are small.

Keywords: Variable-structure/sliding-mode control, Algebraic/geometric methods, Power electronics
Abstract: In this article, we present a general approach to control switched Hamiltonian systems via a sliding motion defined on a manifold of the system’s phase space. The geometric features of classical Hamiltonian systems are exploited in the formal definition of sliding regimes taking place in the phase space of the system and the establishment of necessary and sufficient conditions for the existence of a local sliding regime on a pre-specified manifold which generates a desired closed loop dynamics. An example is presented which illustrates the approach.

Keywords: Feedback linearization, Power electronics, Robust control
Abstract: This article addresses the control design problem for multivariable nonlinear systems in the particular case of dynamically feedback linearizable multiple input multiple output (MIMO) systems. The differential flatness property is used to establish the possibility of static feedback linearization on a suitably dynamically extended system, where linear ADRC loops are easily implemented on independent perturbed chains of integrations, modulo a Relative Gain Array treatment of the resulting input coupling matrix. The proposed approach is tested on an experimental platform, a Cascade Buck-Buck DC-DC power converter.

Keywords: Automotive systems, Automotive control, Modeling
Abstract: This study proposes a parametric model for personalized braking of electric vehicles (EVs) in a full-stop scenario, with the goal of achieving more comfortable One-Pedal Driving. The proposed model is designed to capture two operations: coasting and active braking. Firstly, coasting behavior is analyzed using real-world driving data from three vehicles to determine a threshold distance where active braking initiates. Secondly, a modified skewed sine (MSS) function-based model is introduced to capture human behavior during active braking periods. With the proposed MSS and a second-order polynomial approximation of the coasting period threshold, braking during full-stop scenarios can be reasonably well captured. On average, the medians of the root mean squared error in vehicle speed, the median of minimum acceleration error, and the minimum/maximum jerk errors between the model and the data are 0.76~m/s, 0.18~m/s^2, and 0.02/-0.54~m/s^3, respectively. Furthermore, a parametric study was conducted to investigate the influence of the model parameter and the duration of coasting periods on energy recuperation for a specific EV. The results reveal that when compared to a scenario where the model parameter is tuned with human driving data, energy recuperation can be increased by up to 11.75% through reductions in the model parameter value and coasting distance.

Keywords: Optimal control, Optimization, Automotive control
Abstract: Eco-driving of Connected and Automated Vehicles (CAVs), in particular with multiple on-board power sources, has the potential to significantly improve energy savings in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profiles between origin and destination, based upon route information available from connectivity and advanced mapping features. In this work, the eco-driving problem is solved for a fuel cell electric truck over a selected real-world route within the Texas Triangle Region. A study on the effect of availability of look-ahead information, such as grade, is brought up and the results are expressed in terms of hydrogen consumption and travel time. The problem is formulated as an optimal control problem (OCP), and solved as a hierarchical model predictive control (MPC) using Approximate Dynamic Programming (ADP). Different levels of driver aggressiveness have been considered in the virtual simulations performed in SIMULINK and the results have been compared against a heuristic-based baseline controller. The improvements coming from the optimized strategy without look-ahead grade information are approximately 3% to 9% depending on the driver aggressiveness, while a substantial and consistent 8% improvement is provided when leveraging look-ahead data.

Keywords: Optimization, Multivehicle systems, Transportation networks
Abstract: This paper examines the challenges and requirements for transitioning logistic distribution networks to electric fleets. To maintain their current operations, fleet operators need a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations and fleet schedules. In this context, this paper presents a modeling framework to optimize the charging infrastructure and charging schedules for a logistic distribution network in a joint fashion. Specifically, we cast the joint infrastructure design and operational scheduling problem as a mixed-integer linear program that can be solved with off-the-shelf optimization algorithms providing global optimality guarantees. For a case study in the Netherlands, we assess the impact of different parameters in our optimization problem, specifically, the allowed deviation from existing operations with conventional diesel trucks and the cost factor for daily peak energy usage. We examine the effects on infrastructure design and power requirements, comparing our co-design algorithm with planned infrastructure solutions. The results indicate that current charging and electric machine technologies for trucks can perform the itineraries of conventional trucks for our case study, but to maintain critical time requirements and navigate grid congestion co-design can have a significant impact in reducing total cost of ownership (average 3.51% decrease in total costs compared to rule-based design solutions).

Keywords: Automotive control, Automotive systems, Optimization algorithms
Abstract: As the technology of battery electric vehicles (BEVs) becomes more mature, the number of BEVs in the commercial market has been rapidly growing. Despite significant improvements in BEV driving ranges over the past decade, longer charging times and a limited public charging infrastructure still result in extended travel times for BEV users compared to users of gasoline vehicles. To address this challenge, the fixed route vehicle charging problem (FRVCP) has been extensively studied to reduce travel time by strategically planning the visit to charging stations and optimizing the charging duration. However, the solution of FRVCP primarily focuses on the charging schedule, without considering driver’s social activities during the trip (e.g., dining, lodging, and visits to places of attraction). Therefore, this paper develops a driver-centric schedule optimizer by integrating the driver’s social activities into FRVCP. The driver’s needs of dining and lodging are measured by a temporal first-order human energy model, and the problem is formulated into a mixed-integer programming problem. The proposed optimizer is evaluated through Monte Carlo simulation across 6 routes, demonstrating up to 17.19% savings on travel time compared with a human-like heuristic optimizer. It also shows better capability of enhancing driver’s comfort.

Keywords: Automotive control, Automotive systems, Optimization algorithms
Abstract: With an increase in direct-to-consumer deliveries, growing fuel costs and environmental impact have become major concerns for delivery fleets. Fleet electrification is a promising solution to reduce operating costs and carbon footprint. The major barriers to fleet electrification are the limited range of electric vehicles (EVs) and limited charging infrastructure. To address these issues, this paper proposed a new concept of using one delivery EV together with a towable mobile charging trailer (MCT) for delivery. In addition, this study proposed a two-echelon genetic algorithm-variable neighborhood search (GA-VNS) to solve the continuous location routing problem (LRP). Simulation results showed that, by optimally locating an MCT and routing, the delivery EV was able to cover the same service area with reduced EV range and thus battery size. Compared to the baseline case (i.e., return-to-the-base charging), the proposed operational concept and algorithm can help significantly reduce the total travel distance and the total energy consumption by up to 57.57% and 49.72%, respectively. The proposed operational concept and algorithm can potentially help fleet owners reduce operating costs and the number of vehicles in various delivery tasks.

Keywords: Process Control, Building and facility automation, Machine learning
Abstract: Modern communities are one of the most important energy consumers in the city energy system, offering substantial potential for active participation in demand response services. In this paper, we propose an energy scheduling and control framework for grid-interactive communities, integrating a physically consistent deep learning (PCDL) model for thermal dynamic approximation. The construction of the PCDL model relies on open-loop simulation data, with its physical consistency assured through parameter constraints and a specialized two-layer model structure. Subsequently, the PCDL model is utilized as the prediction model in both the scheduling and control systems. The energy dispatch plan is derived through a day-ahead scheduling process and then integrated into the model predictive control (MPC) system to facilitate real-time energy management and thermal comfort regulation. A simulation case is presented in this paper to verify the performance of the proposed modeling and control methods. According to the simulation results, the PCDL model exhibits superior control-oriented generalization ability compared to the other data-driven models. Furthermore, the PCDL-based scheduling and control framework outperforms other controllers in maintaining indoor thermal comfort and achieves at least a 29.5% reduction in energy expenditure compared to the baseline controller.

Keywords: Process Control, Distributed parameter systems, Optimal control
Abstract: This work designs a Model Predictive Control (MPC) for a two-dimensional transport-reaction model described by a first-order hyperbolic system. The model predictive controller design requires a discrete in-time modeling setting that is obtained by an exact time discretization utilizing the semigroup, without any model reduction or approximation, usually present in controller designs of DPS. Numerical simulations demonstrate the controller’s efficiency in achieving faster convergence and both input and output constraint satisfaction.

Keywords: Process Control, Predictive control for nonlinear systems, Optimal control
Abstract: In this work, we address the problem of economic operation of wastewater treatment plants by proposing a data-driven economic predictive control approach. First, we propose a deep input-output Koopman modeling framework, which is able to predict the overall economic operational cost for the water treatment process based on input data and partial state measurements. Subsequently, based on the learned model, a convex economic model predictive control (EMPC) strategy is developed. This control strategy improves the overall operational performance in a computationally efficient manner. The simulation results validate the effectiveness of our proposed approach and demonstrate its superiority over a benchmark EMPC method.

Keywords: Biotechnology, Process Control, Differential-algebraic systems
Abstract: This article describes a technique for solving optimal control problems by transformation into a system of differential-algebraic equations (DAEs). The optimal control vector can be obtained via simulation of the resulting DAE system with the selected DAE solver, eliminating the need for an optimization solver. This simulation-based (DAE-based) technique is demonstrated and benchmarked against various optimization-based approaches via two case studies associated with optimization and control of a Stefan problem. Results show that the simulation-based approach is faster than every optimization-based method by more than an order of magnitude while giving accurate solutions in all cases. The proposed framework offers a promising alternative to the traditional techniques in optimal control-related applications where speed is crucial, e.g., real-time online model predictive control.

Keywords: Control applications, Process Control, PID control
Abstract: Multivariable poorly damped processes can be easily destabilized using poorly designed controllers. In this case, controllers with increased robustness and flexibility are generally preferred. At the same time, model-based controllers are prone to modeling errors. Occasionally, autotuning methods can be used instead. A solution that meets all these requirements consists in the generalization of the PID, the fractional order controller. Autotuning methods for fractional order controllers are scarce, even so for multivariable poorly damped ones. No experimental validation of such methods has been reported. In this manuscript, an autotuning method is used to design fractional order controllers for a two rotor aerodynamical system. A novel approach using a sequential relay test and fractional order PIDs is used to extract required process information. Comparative closed loop results with standard PID controllers are included to demonstrate the advantages and disadvantages of the approach.

Keywords: Fault detection, Fault accomodation, Process Control
Abstract: The robustness of automatic process control systems to faults occurring in the control system components has become increasingly important, especially in sampled-data systems with measurements obtained at discrete intervals. This paper addresses the problem of estimation and accommodation of multiple simultaneous sensor faults in nonlinear processes with discretely-sampled measurements. We propose an integrated approach that leverages a neural-network-based classification scheme for fault detection and estimation, together with a model-based feedback controller design for fault accommodation. Specifically, a multi-output feed-forward neural network is trained to classify the magnitudes of each sensor reading as a discrete value, allowing for the approximation of the fault magnitude. The model-based feedback controller adjusts the location of the closed-loop poles to ensure stability during faulty operation. An illustrative chemical process example is used to demonstrate the efficacy of the proposed approach and assess its robustness with respect to possible fault estimation errors.

Keywords: Networked control systems, Cooperative control, Distributed control
Abstract: In this paper, the cluster consensus control of rigid body attitude for a multi-agent system on networks represented by directed graphs with weighted cooperative and competitive interactions is studied. Using an absolute Laplacian feedback scheme, Lyapunov-based stability guarantees are obtained for the cases of strongly connected interactively balanced and interactively sub-balanced communication graphs. Both rotation matrices on SO(3)^N and exponential coordinates are used, while the proposed controllers are at the kinematic level.

Keywords: Autonomous systems, Spacecraft control, Adaptive systems
Abstract: There have been numerous different proposed path planning algorithms capable of computing obstacle avoidance paths for autonomous vehicles. However, methods often fall short for path planning in six degrees of freedom (6dof) and dynamic environments such as those encountered by spacecraft in orbit. This article proposes the novel Enumerated Vectors for Autonomy in Dynamic Environments (EVADE) method -- strongly influenced by the Vector Field Histogram (VFH) algorithm -- which generates a desired path in 6dof environments for autonomous obstacle or adversary avoidance for space vehicles. EVADE's method of state representation converts large point cloud data sets obtained from LiDAR sensors into a series of Gaussian distributions which are stored in a 3D polar grid. EVADE's representation allows for a seamless analysis of the surrounding state in 3 dimensions, as well as propagation of obstacle states in environments with dynamic obstacles. EVADE is also performant in scenarios with intelligent dynamic obstacles that intentionally and continuously interfere with the planned path. In result, EVADE is capable of providing complex 3D spline paths to inform a space vehicle's guidance, navigation and control system while using minimal compute to enable edge-based avoidance maneuvers.

Keywords: Filtering, Sensor fusion, Sensor networks
Abstract: The distributed filtering problem sequentially estimates a global state variable using observations from a network of local sensors with different measurement models. In this work, we introduce a novel methodology for distributed nonlinear filtering by combining techniques from transportation of measures, dimensionality reduction, and consensus algorithms. We illustrate our methodology on a satellite pose estimation problem from a network of direct and indirect observers. The numerical results serve as a proof of concept, offering new venues for theoretical and applied research in the domain of distributed filtering.

Keywords: Spacecraft control, Robotics, Robust adaptive control
Abstract: This paper presents a new control law that combines solar radiation pressure and atmospheric drag as a forms of actuation with thrusters to reduce the fuel necessary to perform formation reconfiguration and maintenance. Specifically, utilizing the novel combination of particle swarm optimization algorithm with the stabilization technique of Guardian Map theory, a control law that is robust to changes in orbital position and angle with respect to the sun and velocity vectors is proposed. A linear parameter varying model of the chaser spacecraft that considers the effects of a third-body, and the nonlinear model of solar radiation pressure and atmospheric drag is also proposed in this paper and used in the development of the control law. Results show a reduction in fuel-consumption during the transients of formation reconfiguration and maintenance.

Keywords: Aerospace, Spacecraft control
Abstract: This paper considers constrained spacecraft rendezvous and docking (RVD) in the setting of the Bicircular Restricted Four-Body Problem (BCR4BP), while accounting for attitude dynamics. We consider Line of Sight (LoS) cone constraints, thrust limits, thrust direction limits and approach velocity constraints during RVD missions in a near rectilinear halo orbit (NRHO) in the Sun-Earth-Moon system. To enforce the constraints, the Time Shift Governor (TSG), which uses a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft, is employed. The time shift is gradually reduced to zero so that the virtual target gradually evolves towards the Chief spacecraft as time goes by, and the RVD mission can be achieved. Numerical simulation results are provided to validate of the proposed control method.

Keywords: Stability of nonlinear systems, Spacecraft control, Output regulation
Abstract: Flow rate controllers for Liquid Propelled Rocket Engines (LPRE) were initially designed for specific operating points. However, the development of reusable launchers makes it necessary to design control laws that allow safe transitions between several operating points. The objective of this paper is to present a methodology to determine control laws fulfilling two objectives, i. e. ensuring transition between the operating points, and guaranteeing stability along the state trajectories. The approach is based on the use of contraction theory and extends previous work that tackled a simplified model of the engine to consider the complete non linear model of the LPRE. The design of the control law boils down to solving a set of LMIs. Simulation results illustrate the resulting performances of the controlled engine.

Keywords: Adaptive control, Visual servo control, Robotics
Abstract: Introducing a depth-independent interaction matrix is effective for robots to achieve uncalibrated adaptive image-based visual servoing (IBVS) if both the intrinsic and extrinsic parameters of the camera are unknown. Yet, existing relevant studies neglect parameter convergence that is beneficial to improving the overall performance and robustness of adaptive robot control systems. This paper proposes an adaptive IBVS method for robot visual regulation under an uncalibrated eye-to-hand monocular camera, where a composite learning mechanism is incorporated to improve parameter estimation so as to enhance pixel error convergence. Asymptotic convergence of pixel errors and parameter estimation errors is proven under a condition of interval excitation that significantly relaxes persistent excitation. Experiments on a collaborative robot with 7 degrees of freedom named Franka Emika Panda have verified the effectiveness and superiority of the proposed method with respect to parameter estimation and pixel error convergence.

Keywords: Adaptive control
Abstract: This paper investigates a novel dual-loop sliding mode control (SMC) scheme to achieve trajectory tracking for a class of nonlinear systems with unknown uncertainties. The inner loop is control loop, which consists of three controllers. An optimal feedback controller based on solving Hamilton-JacobiBellman equation is proposed, which optimizes system performance for the nominal tracking system. A cerebellar model articulation control (CMAC) neural network is introduced for approximating the unknown uncertainties, and is embedded in the so-called CMAC-based sliding-mode controller. An adaptive compensator is used to dispel the negative effect from the approximated error of the CMAC. The outer loop is learning loop, which consists of the CMAC-based approximator and the quasi-sliding-model learning law. We formulate the learning problem of CMAC into a robust control framework of discrete time nonlinear system. A new online learning law based on discrete-time quasi-SMC strategy is developed to ensure the global and fast convergence and eliminates the chattering. It can be shown that the explicit expressions of gain parameters of the dual-loop sliding-mode scheme are derived in terms of the established sufficient conditions. Our proposed techniques are applied to a salient permanent magnet synchronous motor with excellent performance.

Keywords: Adaptive systems, Adaptive control, Computational methods
Abstract: We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark subactuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural Engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances.

Keywords: Adaptive systems, Adaptive control, Quantum information and control
Abstract: An essential quantity in quantum information theory is the von Neumann entropy which depends entirely on the quantum density operator. Once known, the density operator reveals the statistics of observables in a quantum process, and the corresponding von Neumann Entropy yields the full information content. However, the state, or density operator, of a given system may be unknown. Quantum state observers have been proposed to infer the unknown state of a quantum system. In this note, we show (i) that the von Neumann entropy of the state estimate produced by our quantum state observer is exponentially convergent to that of the system’s true state, and (ii) the relative entropy between the system and observer’s state converges exponentially to zero as long as the system starts in a full-rank state.

Keywords: Adaptive systems, Adaptive control
Abstract: This paper presents novel model reference adaptive control (MRAC) systems for nonlinear plants in which the matched, nonparametric uncertainty is contained in a vector-valued reproducing kernel Hilbert space (RKHS). The first MRAC system leverages both the classical projection operator for MRAC and a convex projection operator on Hilbert spaces, and assures uniform boundedness of both the trajectory tracking error and the adaptive gains and uniform ultimate boundedness of the trajectory tracking error. The notion of projection operator on Hilbert spaces is presented in this paper for the first time. This MRAC system allows the user to design part of the control input, known as the compensator, provided that some minimal design criteria are met. The second MRAC system builds upon the previous one and assures both uniform boundedness of all parameters and uniform asymptotic convergence of the tracking error to zero. This result, which is the first of its kind within the MRAC literature in the presence of matched uncertainties in RKHSs, requires the user to estimate the largest admissible magnitude of the functional uncertainty. The last MRAC system leverages the previous one and does not require any constraints on the largest admissible magnitude of the functional uncertainty.

Keywords: Adaptive systems, Estimation, PID control
Abstract: Digital PID control requires a differencing operation to implement the D gain. In order to suppress the effects of noisy data, the traditional approach is to filter the data, where the frequency response of the filter is adjusted manually based on the characteristics of the sensor noise. The present paper considers the case where the characteristics of the sensor noise change over time in an unknown way. This problem is addressed by applying adaptive real-time numerical differentiation based on adaptive input and state estimation (AISE). The contribution of this paper is to extend AISE to include variable-rate forgetting with exponential resetting, which allows AISE to more rapidly respond to changing noise characteristics while enforcing the boundedness of the covariance matrix used in recursive least squares.

Keywords: Autonomous systems, Neural networks, Flight control
Abstract: A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural Engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks.

Keywords: Autonomous systems, Stochastic optimal control, Machine learning
Abstract: Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often require excessive sample sizes, which may be infeasible in real-world scenarios where only limited observations of the system are available. Furthermore, purely data-driven methods often neglect useful a priori knowledge, such as approximate models of the system dynamics. We present a method to incorporate such prior knowledge into data-driven control algorithms using kernel embeddings, a nonparametric machine learning technique based in the theory of reproducing kernel Hilbert spaces. Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem. We formulate the biased learning problem as a least-squares problem with a regularization term that is informed by the dynamics, that has an efficiently computable, closed-form solution. Through numerical experiments, we empirically demonstrate the improved sample efficiency and out-of-sample generalization of our approach over a purely data-driven baseline. We demonstrate an application of our method to control through a target tracking problem with nonholonomic dynamics, and on spring-mass-damper and F-16 aircraft state prediction tasks.

Keywords: Aerospace, Autonomous systems, Multivehicle systems
Abstract: This paper introduces an aircraft contingency landing framework that assures the safe execution of road-based emergency landings by coordinating traffic speed regulation through a vehicle-to-vehicle data link. Suitable roads within the reachable footprint are extracted from a geographical database and ranked based on road properties, live weather, and traffic data. A landing trajectory is planned for the road with the highest multi-objective utility, ensuring safe road landing by commanding ground traffic to stop and allocate space on the road using the data link. A use case in Long Island, New York, is presented in simulation with connected ground traffic and gliding fixed-wing aircraft. Results confirm analytically obtained safety constraints and illustrate the safe road landing sequence in dense traffic initially moving at normal speeds.

Keywords: Cooperative control, Autonomous systems, Adaptive control
Abstract: In this paper, the distributed event-triggered consensus control of uncertain multi-agent systems (MASs) under a directed graph is investigated, where the information transmission among agents is intermittent and asynchronous. Firstly, to eliminate the influence of the nonlinear uncertainties and reduce the communication burden among the agents, we employ the model reference adaptive consensus (MRACon) technique, where a reference model with the predicted relative state information initialized by intermittently collected state information of neighbors as input is introduced for each agent to track. An adaptive consensus algorithm is designed such that the uncertain MAS under a directed graph reaches consensus asymptotically. A novel graph-based Lyapunov functional is proposed to guarantee the correctness of theoretical analysis. Moreover, a lower bound of the minimum transmission interval is obtained for each agent, and thus the Zeno behavior is strictly ruled out. Finally, the effectiveness is verified by a simulation example.

Keywords: Nonholonomic systems, Autonomous systems, Mechanical systems/robotics
Abstract: This paper considers collision avoidance for vehicles with first-order nonholonomic constraints maintaining nonzero forward speeds, moving within dynamic environments. We leverage the concept of control barrier functions (CBFs) to synthesize control inputs that prioritize safety, where the safety criteria are derived from the velocity obstacle principle. Existing instantiations of CBFs for collision avoidance, e.g., based on maintaining a minimal distance, can result in control inputs that make the vehicle stop or even reverse. The proposed formulation effectively separates speed control from steering, allowing the vehicle to maintain a forward motion without compromising safety. This is beneficial for ensuring that the vehicle advances towards its desired destination, and it is moreover an underlying requirement for certain vehicles such as marine vessels and fixed-wing UAVs. Theoretical safety guarantees are provided, and numerical simulations demonstrate the efficiency of the strategy in environments containing moving obstacles.

Keywords: Network analysis and control, Autonomous systems
Abstract: Despite the simplicity of its protocol, the matrix-weighted multi-agent system naturally admits cluster consensus under a wide range of network parameters, with mechanisms that have yet to be fully understood. Here we examine the system on a negative circle and study its clustering effect with the edge set named as the Nontrivial Balancing Set (NBS). A single NBS, upon the negation of its edges, implies a bipartition of the agents both structurally and dynamically. When the cyclic network contains more than one NBS, clusters of the agents’ asymptotic values develop as a result of different bipartitions on the graph and in the Laplacian null space. We prove that the vectors associated with the NBSs span the null space of the Laplacian matrix for such networks. Furthermore, we show that agents partitioned into the same group by all of the NBSs achieve consensus, and that the clusters are separated dynamically under an appropriate initial value condition.

Keywords: Predictive control for linear systems, Reduced order modeling, Robotics
Abstract: Gait control of legged robotic walkers on dynamically moving surfaces (e.g., ships and vehicles) is challenging due to the limited balance control actuation and unknown surface motion. We present a contingent model predictive control (CMPC) for bipedal walker locomotion on moving surfaces with a linear inverted pendulum (LIP) model. The CMPC is a robust design that is built on regular MPC to incorporate the ``worst case'' predictive motion of the moving surface. Integrated with a LIP model and walking stability constraints, the CMPC framework generates a set of consistent control inputs considering to anticipated uncertainties of the surface motions. Simulation results and comparison with the regular MPC for bipedal walking are conducted and presented. The results confirm the feasibility and superior performance of the proposed CMPC design over the regular MPC under various motion profiles of moving surfaces.

Keywords: Predictive control for linear systems, Robotics, Aerospace
Abstract: In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). CBF is a tool that allows enforcement of forward invariance of a set using Lyapunov-like functions to ensure safety. The First control synthesis that employed CBF was based on Quadratic Program (QP) that modifies the existing controller to satisfy the safety requirements. However, the CBF-QP-based controllers leading to longer detours and undesirable transient performance. Recent contributions utilize the framework of MPC benefiting from the prediction capabilities and constraints imposed on the state and control inputs. Due to the intrinsic nonlinearities of the dynamics of robotics systems, all the existing MPC-CBF solutions rely on nonlinear MPC formulations or operate on less accurate linear models. In contrast, our novel solution unlocks the benefits of linear MPC-CBF while considering the full underactuated dynamics without any linear approximations. The cascaded scheme converts the problem of safe VTOL-UAV navigation to a Quadratic Constraint Quadratic Programming (QCQP) problem solved efficiently by off-the-shelf solvers. The closed-loop stability and recursive feasibility is proved along with numerical simulations showing the effective and robust solutions.

Keywords: Predictive control for linear systems, Subspace methods, Closed-loop identification
Abstract: Current data-driven predictive control (DDPC) methods heavily rely on data collected in open-loop operation with elaborate design of inputs. However, due to safety or economic concerns, systems may have to be under feedback control, where only closed-loop data are available. In this context, it remains challenging to implement DDPC using closed-loop data. In this paper, we propose a new DDPC method using closed-loop data by means of instrumental variables (IVs). We point out that the original DDPC fails to represent all admissible trajectories due to feedback control. By drawing from closed-loop subspace identification, the use of two forms of IVs is suggested to address this issue and the correlation between inputs and noise. Furthermore, a new DDPC formulation with a novel IV-inspired regularizer is proposed, where a balance between control cost minimization and weighted least-squares data fitting can be made for improvement of control performance. Numerical examples and application to a simulated industrial furnace showcase the improved performance of the proposed DDPC based on closed-loop data.

Keywords: Predictive control for linear systems, Uncertain systems, LMIs
Abstract: Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme to design state-feedback controllers from noisy data for unknown linear time-invariant (LTI) system. The considered min-max problem minimizes the worst-case cost over the set of system matrices consistent with the data. We show that the resulting optimization problem can be reformulated as a semidefinite program (SDP). By solving the SDP, we obtain a state-feedback control law that stabilizes the closed-loop system and guarantees input and state constraint satisfaction. A numerical example demonstrates the validity of our theoretical results.

Keywords: Predictive control for linear systems
Abstract: This paper proposes an analytical target modification for linear robust model predictive control strategies in order to deal with time-varying references defined by dynamic signal targets. The new approach can be directly integrated to linear robust model predictive control algorithms that achieve piecewise constant reference tracking if recursive feasibility is ensured for any set-point. The main contribution is to present a direct analytical approach that provides a potentially improved steady-state tracking error performance with the same computation complexity of the original MPC for tracking piecewise constant reference. A simulation case study based on the trajectory tracking control of a quadrotor is used to illustrate the usefulness of the new analytical target modification layer.

Keywords: Networked control systems, Predictive control for linear systems, Constrained control
Abstract: In this paper, a resilient model predictive control architecture is proposed for constrained cloud-based networked control systems subject to false data injections on both the controller-to-actuator and sensor-to-controller channels. The basic idea consists in exploiting the capability of the encryption process to hide the data structure, shared between the controller and plant sides, to any third-party. Then, by adequately coupling the latter with the resilient nature of the receding horizon control philosophy, an array of attack countermeasures is determined for the on-line operations. Besides this, in order to secure data packet transmissions, cloud computing operations are performed by adopting an additive homomorphic cryptosystem so that encrypted model predictive control sequences are obtained. Finally, a platoon of vehicles is used to validate the whole architecture in simulation.

Keywords: Supervisory control, Discrete event systems, Automata
Abstract: In this paper, we study a security problem of protecting secrets in distributed systems. Specifically, we employ discrete-event systems to describe the structure and behaviour of distributed systems, in which global secret information is separated into pieces and stored in local component agents. The goal is to prevent such secrets from being exposed to intruders by imposing appropriate protection measures. This problem is formulated as to ensure that at least one piece of every distributed global secret is secured by a required number of protections, while the overall cost to apply protections is minimum. We first characterize the solvability of this security problem by providing a necessary and sufficient condition, and then develop an algorithm to compute a solution based on the supervisory control theory of discrete-event systems. Finally, we illustrate the effectiveness of our solution with an example system comprising distributed databases.