Keywords: Predictive control for nonlinear systems, Stochastic optimal control, Autonomous systems
Abstract: In this work, we design a model predictive controller (MPC) for systems to satisfy Signal Temporal Logic (STL) specifications when the system dynamics are partially unknown, and only a nominal model and past runtime data are available. Our approach uses Gaussian process regression to learn a stochastic, data-driven model of the unknown dynamics, and manages uncertainty in the STL specification resulting from the stochastic model using Probabilistic Signal Temporal Logic (PrSTL). The learned model and PrSTL specification are then used to formulate a chance-constrained MPC. For systems with high control rates, we discuss a modification for improving the solution speed of the control optimization. In simulation case studies, our controller increases the frequency of satisfying the STL specification compared to controllers that use only the nominal dynamics model.

Keywords: Biomedical, Hybrid systems, Predictive control for linear systems
Abstract: This paper describes an optimized behavioral intervention Healthy Mom Zone (HMZ) for managing gestational weight gain featuring sequential decision-making using Hybrid Model Predictive Control (HMPC). Dynamical models incorporating both behavioral and physiological aspects of the problem are presented and estimated from HMZ participant data via constrained semi-physical modeling. Daily measurements are provided to a controller that ultimately makes judicious (though infrequent) augmentations on categorical dosages of healthy eating and physical activity intervention components. Consequently, an HMPC algorithm is required which must follow a logical sequence of control actions conforming to practical requirements. A case study shows the benefits relative to a conventional "IF-THEN" approach. The computational framework (both modeling and control) serves as the basis for the Healthy Mom Zone 2.0 intervention currently being evaluated in a randomized clinical trial (NIH R01DK134863, NCT05807594) at Penn State University.

Keywords: Control system architecture, Sampled-data control, Predictive control for nonlinear systems
Abstract: Inner-outer-loop control is widely used for controlling mechanical systems with time-scale separation. Model Predictive Control (MPC) is a popular technique for systems that require command following with state and control constraints. We present a conversion algorithm for MPC-based inner- and outer-loop control by accounting for timing intricacies. For uncertain systems, we apply inner-outer-loop control based on predictive cost adaptive control (PCAC) to flight-control examples, with and without the conversion algorithm. Numerical examples show that inner-outer-loop PCAC improves command following and constraint satisfaction when the conversion algorithm is used. The investigation in this paper is numerical, and thus the contribution is technological rather than theoretical.

Keywords: Optimal control, Optimization algorithms, Constrained control
Abstract: Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing constraints is achieved by limiting the number of iterations of the optimization algorithm used to compute control actions at each time step, resulting in so-called suboptimal MPC. This paper proposes a suboptimal MPC scheme based on the alternating direction method of multipliers (ADMM). With a focus on the linear quadratic regulator problem with state and input constraints, we show how ADMM can be used to split the MPC problem into iterative updates of an unconstrained optimal control problem (with an analytical solution), and a dynamics-free feasibility step. We show that using a warm-start approach combined with enough iterations per time-step, yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the system and maintains recursive feasibility.

Keywords: Predictive control for linear systems, Robust control, Aerospace
Abstract: This paper introduces a robust Model Predictive Control approach in which a shrinking prediction horizon and a system input parameterization are exploited to control a linear system with set-bounded disturbances while satisfying state and control constraints. By exploiting input parameterization, the number of decision variables in the optimal control problem and the computational time can be reduced. The simulated spacecraft rendezvous maneuver is used to highlight the potential of the proposed approach for practical applications.

Keywords: Predictive control for linear systems, Robust control, Optimal control
Abstract: Model Predictive Control (MPC) is a powerful control strategy; however, its reliance on online optimization poses significant challenges for implementation on systems with limited computational resources. One possible approach to address this issue is to shorten the prediction horizon and adjust the conventional MPC formulation to enlarge the region of attraction. However, these methods typically introduce additional computational load. Recently, steady-state-aware MPC has been introduced to ensure output tracking and convergence to a given desired steady-state configuration while maintaining constraint satisfaction at all times without adding extra computational load. Despite its promising performance, steady-state-aware MPC does not account for external disturbances, which can significantly limit its applicability to real-world systems. This paper aims to advance the method further by enhancing its robustness against external disturbances. To achieve this, we adopt the tube-based design framework, which decouples nominal trajectory optimization from robust control synthesis, thereby requiring no additional online computational resources. Theoretical guarantees of the proposed methodology are shown analytically, and its effectiveness is assessed through simulations and experimental studies on a Parrot Bebop 2 drone.

Keywords: Predictive control for nonlinear systems, Iterative learning control, Constrained control
Abstract: For nonlinear systems that can be written in pseudo-linear form, we use iterative model predictive control (IMPC) for receding-horizon optimization. Pseudo-linear models, which are written in terms of state-dependent-coefficients (SDC’s), are widely used with the state-dependent Riccati equation. IMPC iterates over the horizon by updating the future control and state sequences until the future control sequence converges. To facilitate convergence, this paper explores the effectiveness of four root-finding methods and compares their performance with fixed-point iteration. At each iteration, a quadratic programming problem is solved using the current SDC’s to obtain a full-state-feedback controller. To assess the effectiveness of the root-finding methods, each technique is used to stabilize a collection of benchmark nonlinear systems.

Keywords: Predictive control for nonlinear systems, Optimal control, Computational methods
Abstract: Nonlinear Model Predictive Control (NMPC) is a powerful tool that can be applied to various control objects. However, it is known that the behavior realized by the NMPC varies greatly depending on the results of tuning the weight parameters of the objective function. In recent years, some methods have been proposed to automatically tune the weight parameters by utilizing tools such as machine learning, but these methods require a large number of prior trials. We propose a method to tune time-varying stage cost weight parameters in the NMPC for reference tracking without prior trials so as to minimize the tracking error. We formulate the NMPC as a min-max problem with the weight parameters as the maximizer and the control input as the minimizer, in order to optimize the weight parameter and the control input simultaneously. Furthermore, treating this optimization problem as a nonlinear receding-horizon differential game, we develop a real-time optimization algorithm. The effectiveness of the proposed method was confirmed by numerical simulations. We confirmed that the proposed method improves the control performance over the NMPC using fixed weight parameters designed by Bayesian optimization.

Keywords: Predictive control for nonlinear systems, Optimal control, Numerical algorithms
Abstract: This paper presents a novel framework for the continuation method of model predictive control based on optimal control problem with a nonsmooth regularizer. Via the proximal operator, the first-order optimality inclusion relation is reformulated into an equation system, to which the continuation method is applicable. In addition, we present constraint qualifications that ensure the well-posedness of the proposed equation system. A numerical example is also presented that demonstrates the effectiveness of our approach.

Keywords: Iterative learning control, Process Control, Optimization
Abstract: This paper presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. The robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.

Keywords: Predictive control for nonlinear systems, Stability of hybrid systems, Robotics
Abstract: Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose a layered Model Predictive Control (MPC) architecture for robust stabilization of hybrid systems. A high level “hybrid” MPC is solved at a slow rate to produce a stabilizing hybrid trajectory, potentially sub-optimally, including a domain and guard sequence. This domain and guard sequence is passed to a low level “fixed mode” MPC which is a traditional, time-varying, state-constrained MPC that can be solved rapidly, e.g., using nonlinear programming (NLP) tools. A robust version of the fixed mode MPC is constructed by using tracking error tubes that are not guaranteed to have finite size for all time. Using these tubes, we demonstrate that the speed at which the fixed mode MPC is re-calculated is directly tied to the robustness of the system, thereby justifying the layered approach. Finally, simulation examples of a five link bipedal robot and a controlled nonlinear bouncing ball are used to illustrate the formal results.

Keywords: Autonomous robots, Sensor networks, Control applications
Abstract: This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multi-step prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets.

Keywords: Machine learning, Stochastic systems, Uncertain systems
Abstract: This study introduces a novel theoretical framework for analyzing heteroscedastic Gaussian processes (HGPs) that identify unknown systems in a data-driven manner. Although HGPs effectively address the heteroscedasticity of noise in complex training datasets, calculating the exact posterior distributions of the HGPs is challenging, as these distributions are no longer multivariate normal. This study derives the exact means, variances, and cumulative distributions of the posterior distributions. Furthermore, the derived theoretical findings are applied to a chance-constrained tracking controller. After an HGP identifies an unknown disturbance in a plant system, the controller can handle chance constraints regarding the system despite the presence of the disturbance.

Keywords: Machine learning, Traffic control
Abstract: Accurate pedestrian intention estimation is crucial for the safe navigation of autonomous vehicles (AVs) and hence attracts a lot of research attention. However, current models often fail to adequately consider dynamic traffic signals and contextual scene information, which are critical for realworld applications. This paper presents a Traffic-Aware SpatioTemporal Graph Convolutional Network (TA-STGCN) that integrates traffic signs and their states (Red, Yellow, Green) into pedestrian intention prediction. Our approach introduces the integration of dynamic traffic signal states and bounding box size as key features, allowing the model to capture both spatial and temporal dependencies in complex urban environments. The model surpasses existing methods in accuracy. Specifically, TA-STGCN achieves a 4.75% higher accuracy compared to the baseline model on the PIE dataset, demonstrating its effectiveness in improving pedestrian intention prediction.

Keywords: Reinforcement learning, Control applications, Neural networks
Abstract: The design of feedback controllers is undergoing a paradigm shift from modelic (i.e., model-driven) control to datatic (i.e., data-driven) control. Canonical form of state space model is an important concept in modelic control systems, exemplified by Jordan form, controllable form and observable form, whose purpose is to facilitate system analysis and controller synthesis. In the realm of datatic control, there is a notable absence in the standardization of data-based system representation.

This paper for the first time introduces the concept of textit{canonical data form} for the purpose of achieving more effective design of datatic controllers. In a control system, the data sample in canonical form consists of a textit{transition} component and an textit{attribute} component. The former encapsulates the plant dynamics at the sampling time independently, which is a tuple containing three elements: a state, an action and their corresponding next state. The latter describes one or some artificial characteristics of the current sample, whose calculation must be performed in an online manner. The attribute of each sample must adhere to two requirements: (1) causality, ensuring independence from any future samples; and (2) locality, allowing dependence on historical samples but constrained to a finite neighboring set. The purpose of adding attribute is to offer some kinds of benefits for controller design in terms of effectiveness and efficiency. To provide a more close-up illustration, we present two canonical data forms: temporal form and spatial form, and demonstrate their advantages in reducing instability and enhancing training efficiency in two datatic control systems.

Keywords: Reinforcement learning, Machine learning, Statistical learning
Abstract: We consider a sequential multi-task problem, where each task is modeled as the stochastic multi-armed bandit with K arms. We assume the bandit tasks are adjacently similar in the sense that the difference between the mean rewards of the arms for any two consecutive tasks is bounded by a parameter. We propose two algorithms (one assumes the parameter is known while the other does not) based on UCB to transfer reward samples from preceding tasks to improve the overall regret across all tasks. Our analysis shows that transferring samples reduces the regret as compared to the case of no transfer. We provide empirical results for our algorithms, which show performance improvement over the standard UCB algorithm without transfer and a naive transfer algorithm

Keywords: Reinforcement learning, Machine learning, Neural networks
Abstract: Effective exploration is critical for reinforcement learning (RL) to understand the environment and achieve high performance, especially in sparse reward tasks. Existing methods often depend on task-specific prior knowledge for exploration guidance, which is unavailable in complex tasks, or rely on additional policy objectives to encourage exploration, which leads to sub-optimal solutions. This paper addresses these limitations by introducing a dual-policy guided exploration (DPE) mechanism, which learns an extra policy to promote the sample collection in unfamiliar state areas. In this mechanism, we define a curiosity signal using the random network distillation technique, which evaluates the state familiarity to the agent. Then, a separate courage policy is trained by maximizing the accumulated discounted curiosity signal, which aims to explore unfamiliar areas and discover potential high-value behaviors. By incorporating this mechanism into the distributional RL framework, we propose a Distributional Soft Actor-Critic algorithm for Sparse reward tasks (DSAC-S), which comprises three modules: dual-policy guided exploration, curiosity signal learning, and actor-critic training. We validate its effectiveness in several sparse reward tasks, including MountainCar, Sparse HalfCheetah, and IDSim. The results demonstrate that DSAC-S successfully conquers these tasks with its enhanced exploration ability, while the two baselines either fail to solve these problems or exhibit poor performance.

Keywords: Autonomous systems, Machine learning, Aerospace
Abstract: Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables quick updates and redeployment of the policy when necessary. Then, a modified A* path planning algorithm is proposed to generate paths on the tiered terrain maps. This algorithm takes into account the classified terrain tiers, allowing the rover to dynamically adjust its path based on real-time assessments. By integrating few-shot learning with the modified A* algorithm, the rover is equipped to make real-time intelligent decisions, enhancing its ability to navigate complex terrains effectively. Simulation results demonstrate the rover’s enhanced capability to navigate complex terrains, illustrating the effectiveness and flexibility of this integrated approach.

Keywords: Optimization algorithms, Optimization, Distributed parameter systems
Abstract: This paper addresses the challenge of online learning in contexts where agents accumulate disparate data and use different local learning algorithms due to resource constraints. We introduce the Switched Online Learning Algorithm (SOLA), designed to solve the heterogeneous online learning problem by fusing updates from diverse agents through a dynamic switching mechanism contingent upon their respective performance and available resources. We theoretically analyze the design of the selecting mechanism to ensure that the regret of SOLA is bounded. Our findings show that the number of changes in selection needs to be bounded by a parameter dependent on the performance of the different local algorithms. Additionally, two numerical experiments are presented to emphasize the effectiveness of SOLA, first on an online linear regression problem and then on an online classification problem with the MNIST dataset.

Keywords: Machine learning, Reinforcement learning, Algebraic/geometric methods
Abstract: We consider the continuous-time temporal difference (TD) learning dynamics with nonlinear value function approximations, where there is a slim understanding of the convergence properties in irreversible regimes. Motivated by Krener's linearization idea ala Lie-brackets, we obtain conditions on the approximating value function and irreversibility coefficients under which the TD dynamics behaves close to a gradient flow. We show that our conditions lead to a set of partial differential equations, and study the existence of solutions using the algebraic invertibility of differential operators. Whenever a solution exists, using a perturbation analysis, we provide a stability result for nonlinear TD dynamics. As a by-product, we state the implications of the results for the classical case of linear approximations, where our conditions are algebraic, and easily verifiable.

Keywords: Reinforcement learning, Reduced order modeling, Distributed parameter systems
Abstract: Dimensionality reduction is crucial for controlling nonlinear partial differential equations (PDE) through a ``reduce-then-design'' strategy, which identifies a reduced-order model and then implements model-based control solutions. However, inaccuracies in the reduced-order modeling can substantially degrade controller performance, especially in PDEs with chaotic behavior. To address this issue, we augment the reduce-then-design procedure with a policy optimization (PO) step. The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction. This augmentation shifts the overall strategy into reduce-then-design-then-adapt, where the model-based controller serves as a warm start for PO. Specifically, we study the state-feedback tracking control of PDEs that aims to align the PDE state with a specific constant target subject to a linear-quadratic cost. Through extensive experiments, we show that a few iterations of PO can significantly improve the model-based controller performance. Our approach offers a cost-effective alternative to PDE control using end-to-end reinforcement learning.

Keywords: Network analysis and control, Agents-based systems, Fault tolerant systems
Abstract: We study the problem of resilient leader-follower consensus in multi-agent systems (MASs) where some of the agents may malfunction. The objective is for nonfaulty agents to reach consensus on a reference value broadcast by leader agents despite possible misbehaviors of adversarial agents. To this end, we utilize the multi-hop weighted mean subsequence reduced (MW-MSR) algorithm to achieve the goal in multi-agent networks with directed topologies. We characterize a necessary and sufficient condition on graph structures for our algorithm to succeed, which is expressed in a novel notion of robust following graphs. With one-hop communication, our condition is tighter than the ones in the related resilient leader-follower consensus works. With multi-hop communication, we can have an even more relaxed graph condition for our algorithm to succeed. Lastly, we present numerical examples to verify the effectiveness of our algorithm.

Keywords: Optimal control, Reinforcement learning, Cooperative control
Abstract: Avoiding collisions between unmanned aerial vehicles (UAVs) during formation is a challenge in leaderfollower formation control. This paper presents a two-stage optimal formation control scheme. Firstly, an end-to-end motion planning method is designed for collision-free motion during the formation process, utilizing deep reinforcement learning. Specifically, an actor-critic network with two hidden layers is constructed, incorporating an obstacle avoidance module based on Bounding Volume Hierarchy (BVH) trees. A novel composite reward function, consisting of target penalty and safety penalty, is proposed to guide the training of the actor-critic network. Furthermore, a low-complexity consensus protocol is developed to synchronize the state of the followers and leaders, and the system is rigorously proven to be asymptotically stable. Finally, the effectiveness of the proposed method is validated through simulations involving a set of UAVs.

Keywords: Optimization, Optimization algorithms, Cooperative control
Abstract: This paper investigates distributed zeroth-order feedback optimization in multi-agent systems with coupled constraints, where each agent operates its local action vector and observes only zeroth-order information to minimize a global cost function subject to constraints in which the local actions are coupled. Specifically, we employ two-point zeroth-order gradient estimation with delayed information to construct stochastic gradients, and leverage the constraint extrapolation technique and the averaging consensus framework to effectively handle the coupled constraints. We also provide convergence rate and oracle complexity results for our algorithm, characterizing its computational efficiency and scalability by rigorous theoretical analysis. Numerical experiments are conducted to validate the algorithm's effectiveness.

Keywords: Agents-based systems, Game theory, Distributed control
Abstract: In multi-agent systems with coupled objectives and/or constraints, agents may misreport information to achieve individual gains. This issue is exacerbated when agents possess local decision-making power, such as in multi-agent trajectory planning, where the increased autonomy amplifies individual benefits at the expense of a higher social cost. To overcome this problem, we leverage the Vickrey-Clarke-Grove (VCG) framework and propose a strategyproof, two-stage mechanism. We further extend this mechanism to prioritized planning and prevent agents from manipulating their priority.

Keywords: Optimal control, Estimation, Autonomous systems
Abstract: This paper considers the problem of localizing a set of nodes in a wireless sensor network where both the node positions and communication model parameters are unknown. We assume that a multi-agent system moves in formation through the environment, taking measurements of the Received Signal Strength, and seek a controller that optimizes a performance metric based on the Fisher Information Matrix. We propose a two-stage formation-based receding horizon approach that alternates between estimating the parameters and determining where to move and how to scale the formation to maximally inform the estimation problem. We apply a Dynamic Programming approach to solve the multi-stage look ahead control problem of the first stage, followed by a Particle Swarm Optimization algorithm to determine the best formation configuration in the second stage. We demonstrate our approach using different formation structures and compare it against multiple baselines.

Keywords: Robotics, Reduced order modeling, Emerging control applications
Abstract: A major challenge in soft robotics is model-based control. Many common models for soft robots suffer a loss of mechanical structure when model reduction is performed. This is a significant problem for control, since many control methods, e.g. energy-based control, rely heavily on this structure. In this paper, we present an alternative finite dimensional reduction method that preserves the mechanical structure of the model of a soft robot. Using a form of finite element Galerkin projection, our approach incorporates this reduction process into a variational framework that is suitable for control applications. As a proof-of-principle, we present the model of a planar soft robot with preserved mechanical structure and a simple control paradigm. With this method, soft robots can be described in a form that is suitable for control strategies that depend on their mechanical structure, such as energy-based control.

Keywords: Robotics, Control applications, Lyapunov methods
Abstract: We present LQR-CBF-RRT*, an incremental sampling-based algorithm for offline motion planning. Our framework leverages the strength of Control Barrier Functions (CBFs) and Linear Quadratic Regulators (LQR) to generate safety-critical and optimal trajectories for general affine control systems. This work uses CBF for safety guarantees and LQRs for optimal control synthesis during edge extensions. Traditional CBF methods involve Quadratic Programs (QPs), which add computational overhead and can sometimes be infeasible. Conversely, LQR-based controllers typically employ first-order Taylor approximations for nonlinear systems, necessitating consistent recalculations. To enhance motion planning efficiency, our framework directly verifies CBF constraints during the planning process, thereby eliminating the need for QP solutions. Additionally, we cache optimal LQR gain matrices in a hash table to bypass re-computation during local linearizations in the rewiring phase. To further boost sampling efficiency, we integrate the Cross-Entropy Method. Our results demonstrate that the proposed planner outperforms existing algorithms in computational efficiency and exhibits robust performance in real-world experiments.

Keywords: Robotics, Optimization, Energy systems
Abstract: The paper considers energy-oriented trajectory optimization of a two-link robot inspired by thermodynamics,particularly on the second law. A formulation of thermodynamic principles is developed within the framework of dynamical systems, extending these principles for use in multi-domain systems. The approach involves periodic motions, with the cyclic-averaged subsystem energies replacing temperature in an extended definition of entropy generation. In a representative problem, the robot arm moves between two arbitrary points under the influence of an external force and performs work by repeatedly elevating loads. The trajectories to be optimized were parameterized with a Fourier decomposition. Simulation results indicate that the proposed cost function effectively minimizes energy consumption with some advantages over direct minimization of the supplied energy. Specifically, only partial knowledge of the dissipation characteristics is needed, and the proposed cost function exhibits improved convexity properties near the optimal point in comparison to energy supply.

Keywords: Mechanical systems/robotics, Autonomous robots, Robotics
Abstract: Recent advancements in applying machine learning to bipedal robots have demonstrated significant potential. However, the need for more comprehensive Artificial Intelligence (AI) testing environments has become increasingly evident, as current evaluations are constrained by limited datasets, making real-world testing essential. This paper presents Stilt-bot, an AI test bed specifically designed to support skill transfer across various robotic platforms, regardless of changes in size, shape, or actuator power. Inspired by how humans adapt their gait throughout growth, Stilt-bot employs a simple yet versatile 6 degrees-of-freedom (DOF) design and a prismatic sliding mechanism that enhance its agility and reduce weight. This configuration allows easy modifications to its height, mass, and power, providing a flexible and intuitive platform for evaluating AI-based walking control strategies. Both simulation and experimental results confirm Stilt-bot’s capability for stable flat-ground walking, demonstrating its effectiveness as a test bed for developing robust AI-driven bipedal locomotion.

Keywords: Electrical machine control, Optimization, Estimation
Abstract: Servo systems, one of the backbones of modern manufacturing, are supposed to move as fast as possible for high productivity. Due to the inaccurate information on torque capacity, conventional trajectory generation methods are either overly conservative, compromising yield, or violate dynamical feasibility, compromising quality. This work proposes a method to address these shortcomings. Stable adaptive estimation of the servomotor model parameters is first performed, then torque capacity constraints are established as analytical functions of the motor speed based on parameter estimates, and finally, a computationally efficient algorithm is developed to reshape an aggressive (dynamically infeasible) trajectory into a feasible one. Theoretical analysis and numerical simulation validate the effectiveness of the proposed method.

Keywords: Optimization algorithms, Computational methods, Aerospace
Abstract: This paper investigates and compares the convergence performance of two widely used Nonlinear Programming (NLP) solvers—MATLAB’s texttt{fmincon} and IPOPT— for solving multi-impulse cislunar trajectory optimization problems. The problem is formulated as a minimum-fuel (or minimum-Delta v) trajectory optimization for a transfer between two Distant Retrograde Orbits (DROs) in the Circular Restricted Three-Body Problem (CR3BP). Derivatives of the objective and constraints, required by the solvers, are computed using three methods: a) solver’s built-in finite-difference (FD) method, b) the complex-step based (CX) method, and c) an analytical method. The results demonstrate that while analytical derivatives provide the fastest convergence, the CX method achieves nearly the same performance, despite being significantly easier to compute than the analytical derivatives. The CX method outperforms the FD method in terms of derivative accuracy and its impact on the convergence performance of the solvers (i.e., total number of iterations and function evaluations). Results indicate that IPOPT exhibits faster convergence compared to texttt{fmincon}, when CX and analytical derivatives are used.

Keywords: Aerospace, Hierarchical control, Lyapunov methods
Abstract: Space robots, i.e., spacecraft with robotic manipulators, are essential for in-orbit servicing and debris removal. The high actuation redundancy of these systems allows for the imposition of multiple tasks with different priority levels. This work presents a singularity-free task-priority design for trajectory tracking in space robots, effectively removing the stringent assumption that the manipulator operates within the feasible workspace (free of kinematic singularities). The design, based on a modular hierarchical architecture, ensures end-effector tracking takes priority over tasks like maintaining a safe distance and a proper attitude of the spacecraft base. The proposed control law uses a 6D matrix representation of rigid motion that unifies attitude and position, offering invariance to inertial frame changes, making it ideal for space robotics.

Keywords: Constrained control, Flight control, Control applications
Abstract: This paper considers the problem of trajectory tracking for quadrotors operating in wind conditions that result in propeller thrust saturation. To address this problem, an antiwindup compensator (AWC) is developed to reduce the tracking performance degradation and destabilizing effects from thrust saturation. Relationships are derived showing how the tracking error and AWC states are influenced by the wind disturbance and saturation, and how the influences depend on the controller and AWC gains. As a result, these gains can be tuned to achieve desired performance levels. Simulation results are presented to validate the effectiveness of the proposed method.

Keywords: Statistical learning, Algebraic/geometric methods, Uncertain systems
Abstract: This paper introduces Manifold-Guided Stabilizing Control (MGSC), a novel approach to synthesizing stabilizing controllers for nonlinear dynamical systems using diffusion models. Our method formulates control synthesis as a search for the closest asymptotically stable vector field within a learned manifold of stable dynamics. We train a diffusion model on a dataset of asymptotically stable vector fields and employ Tweedie’s estimate to iteratively adjust control parameters, ensuring convergence to a stabilizing controller. This formulation enables zero-shot stabilization for previously unseen systems with significantly reduced computational cost. Our numerical experiments demonstrate that MGSC achieves stabilization in just 16 seconds, compared to 2 minutes in prior work, while generalizing effectively across different nonlinear control problems. These results highlight the potential of diffusion models as a powerful tool for fast, data-driven control synthesis.

Keywords: Stochastic optimal control, Statistical learning, Stochastic systems
Abstract: This paper addresses the numerical solution of backward stochastic differential equations (BSDEs) arising in stochastic optimal control. Specifically, we investigate two BSDEs: one derived from the Hamilton-Jacobi-Bellman equation and the other from the stochastic maximum principle. For both formulations, we analyze and compare two numerical methods. The first utilizes the least-squares Monte-Carlo (LSMC) approach for approximating conditional expectations, while the second leverages a time-reversal (TR) of diffusion processes. Although both methods extend to nonlinear settings, our focus is on the linear-quadratic case, where analytical solutions provide a benchmark. Numerical results demonstrate the superior accuracy and efficiency of the TR approach across both BSDE representations, highlighting its potential for broader applications in stochastic control

Keywords: Network analysis and control, Control of networks
Abstract: This paper considers the problem of designing non-pharmaceutical intervention (NPI) strategies, such as masking and social distancing, to slow the spread of a viral epidemic. We formulate the problem of jointly minimizing the infection probabilities of a population and the cost of NPIs based on a Susceptible-Infected-Susceptible (SIS) propagation model. To mitigate the complexity of the problem, we consider a steady-state approximation based on the quasi-stationary (endemic) distribution of the epidemic, and prove that the problem of selecting a minimum-cost strategy to satisfy a given bound on the quasi-stationary infection probabilities can be cast as a submodular optimization problem, which can be solved in polynomial time using the greedy algorithm. We carry out experiments to examine effects of implementing our NPI strategy on propagation and control of epidemics on a Watts-Strogatz small-world graph network. We find the NPI strategy reduces the steady state of infection probabilities of members of the population below a desired threshold value.

Keywords: Networked control systems, Biological systems, Time-varying systems
Abstract: Numerous complex systems, such as those arisen in ecological networks, genomic contact networks, and social networks, exhibit higher-order and time-varying characteristics, which can be effectively modeled using temporal hypergraphs. However, analyzing and controlling temporal hypergraphs poses significant challenges due to their inherent time-varying and nonlinear nature, while most existing methods predominantly target static hypergraphs. In this article, we generalize the notions of controllability and observability to temporal hypergraphs by leveraging tensor and nonlinear systems theory. Specifically, we establish tensor-based rank conditions to determine the weak controllability and observability of directed, weighted temporal hypergraphs. The proposed framework is further demonstrated with synthetic and real-world examples.

Keywords: Networked control systems, Decentralized control, Cooperative control
Abstract: Hierarchical cyclic pursuit has garnered significant attention among researchers in recent years. Along similar lines, this work aims to study ring digraphs, with a hierarchical "necklace" structure, involving antagonistic interactions. In the first part, identical antagonistic interactions, represented by negative edge weights, within macro vertices are considered. A study on the effect of such negative interactions on the consensus of agents is presented and bounds on negative gain, for consensus, are derived. Subsequently, heterogeneous antagonistic interactions are considered and the effect of these interactions on the consensus of the agents is analyzed. The set of points where consensus is achievable, when the edge weights are varied within the derived permissible bounds, is also investigated. Finally, numerical examples and corresponding simulations are presented to validate the analytical results.

Keywords: Mechatronics, Stability of nonlinear systems, Control applications
Abstract: This paper presents a novel design for finite-time position control of quadrotor Unmanned Aerial Vehicles (UAVs). A robust, finite-time, nonlinear feedback controller is introduced to reject bounded disturbances in tracking tasks. The proposed control framework differs conceptually from conventional controllers that utilize Euler angle parameterization for attitude and adhere to the traditional hierarchical inner-outer loop design. In standard approaches, the translational controller and the corresponding desired attitude are computed first, followed by the design of the attitude controller based on time-scale separation between fast attitude and slow translational dynamics. In contrast, the proposed control scheme is quaternion-based and utilizes a transit feed-forward term in the attitude dynamics that anticipates the slower translational subsystem. Robustness is achieved through the use of continuously differentiable sliding manifolds. The proposed approach guarantees semi-global finite-time stability, without requiring time-scale separation. Finally, numerical simulation results are provided to demonstrate the effectiveness of the proposed controller.

Keywords: Biomedical, Modeling, Human-in-the-loop control
Abstract: We introduce a novel dynamic modeling paradigm to represent fatigue and recovery processes in muscles by dynamic extension of the classical 3-element Hill model. A memristor and a capacitor are used in a series circuit arrangement, with a voltage source representing muscle activation. This model is shown to capture the fundamental features of fatigue accumulation and recovery in response to arbitrary motion, load and activation profiles, in contrast with other work assuming specific input shapes such as constants or periodic functions. Further, the basic model is shown to capture the gradual increase in activation spectral amplitudes with fatigue progression, which is an experimental fact. The paper shows how the fatigue modeling element is integrated into larger musculoskeletal dynamic models. Possible modifications and extensions aimed at more flexibility are also suggested.

Keywords: Emerging control applications, Predictive control for linear systems, Identification for control
Abstract: Control systems engineering has contributed to a paradigm shift in behavioral science and medicine. Among the applications of control systems engineering in behavioral medicine includes understanding, on an individual level, the dynamics of behavior change and leveraging this knowledge to deliver optimized, personalized interventions. These principles are the foundation of the control optimization trial (COT) framework that aims to facilitate the dissemination of data-driven, control-oriented behavioral interventions and consequently improve individual and public health. YourMove (ClinicalTrials.gov ID NCT05598996), a first-of-its-kind COT study, is an intervention to increase physical activity in sedentary adults and the culmination of years of research into the effectiveness of system identification and model predictive control (MPC) design in behavior change interventions. This paper summarizes the methods utilized in YourMove and provides promising preliminary results for illustrative participants in the ongoing study. The results presented are consistent with scenarios simulated in prior work and validate the COT framework as an effective tool for delivering personalized closed-loop interventions. In particular, results from this study demonstrate the performance and robustness of a three-degree-of-freedom Kalman filter-based hybrid model predictive control (3DoF-KF HMPC) algorithm in real-world settings.

Keywords: Machine learning, Fault detection, Genetic regulatory systems
Abstract: A mRNA-protein gene regulatory network is a differential equation model for gene expression that incorporates the dynamics of both mRNA and protein expressions. This paper addresses the challenges of using High-Dimensional Low-Sample-Size (HDLSS) data set for early disease detection and re-stabilization in mRNA-protein gene regulatory networks. For the first time, we demonstrate that detecting the pre-disease stage of mRNA-protein gene regulatory networks is possible using only the HDLSS data of either mRNA or protein. After detecting the pre-disease stage, it is crucial to prevent disease progression at that point. From a control engineering perspective, this prevention can be achieved by enhancing the system's stability, a process called as re-stabilization. We demonstrate that the key nodes for re-stabilization in the mRNA-protein gene regulatory network manifest as mRNA-protein duals. The intervention strategy, whether suppression or promotion, is identical for the mRNA and protein components of each key dual. Furthermore, we present a Lyapunov equation-based system identification method to estimate the system matrix using the HDLSS data set. From the estimated system matrix, the key nodes for re-stabilization can be identified. The effectiveness of the proposed method has been validated via numerical examples.

Keywords: Biomedical, Predictive control for linear systems, PID control
Abstract: In this paper we propose a technique to enhance the performance of a Proportional-Integral-Derivative (PID)-based control structure for Depth-of-Hypnosis control in total intravenous anesthesia when set-point changes are required during the maintenance phase. In particular, the PID controller, tuned for disturbance rejection, is integrated with a feedforward action based on Model Predictive Control (MPC). A tuning parameter determines the aggressiveness of the controller, thus allowing the anesthesiologist to select the most appropriate transient response depending on the kind of patient and of surgery. Simulation results show that the method is useful in providing an effective tool for the anesthesiologist to interact with the control system.

Keywords: Human-in-the-loop control, Automotive control, Automotive systems
Abstract: The introduction of drive-by-wire systems and advances in vehicle automation have enabled new possibilities of what a vehicle can do. Research has pushed vehicle automation to a level that only skilled drivers can achieve in racing and drifting, and decoupled systems can act as a guardian, protecting the driver in extreme situations. Introducing these systems has the potential to improve safety of the car and driver combined system, but could also lead to deskilling and over-reliance by the driver. This manuscript presents an approach to address this through leveraging task decomposition to teach advanced driving skills, namely drifting a circular donut. Using Model Predictive Control, task decomposition is achieved by allowing drivers to learn steering and throttle control independently, enabling the driver to isolate learning individual skills to enhance skill acquisition. A user study was conducted with 11 participants to experimentally validate the approach. Results reflect that the group subject to task decomposition demonstrated enhanced mastery of the skill.

Keywords: Differential-algebraic systems, Estimation, Control applications
Abstract: An observer is developed for the nonlinear descriptor system dynamics of parallel connected lithium-ion battery packs. The observer estimates the states of the individual cells in the pack with stability and existence guarantees provided through linear matrix inequalities. When evaluated on the urban dynamometer driving schedule, the proposed observer performed well, with root mean squared errors (RMSEs) of 0.0072 and 0.0054 in the state-of-charges as well as 0.3A and 0.28A in the currents for cells 1 and 2, respectively. The results also demonstrated the value of incorporating contact resistances in the model. In particular, with the inclusion of a contact resistance of 0.02 Ohm, the mismatch in currents grew by 9.69% for cell 1 and by 8.55% for cell 2. These results highlight the potential of implementing cell-level state estimation and control in large battery packs accounting for cell-to-cell variability and contact resistances.

Keywords: Energy systems, Hybrid systems
Abstract: Blue economy industries like aquaculture are expanding further offshore to leverage the ocean's vast scale. However, this shift demands reliable, regular power independent of land-based grids. This study introduces a bi-level optimization framework for the design and operation of a hybrid OTEC-diesel system equipped with battery energy storage to power offshore fish farms. The upper-level optimization aims to minimize the levelized cost of energy (LCOE) and ensure continuous operation by optimizing the battery size within the constraints of energy storage. The objective of the lower-level optimization is to minimize energy waste while also addressing an environmental goal, which is to decrease the unnecessary mixing of cold and warm water in the Rankine cycle of the ocean thermal energy conversion (OTEC) system. In our study, we investigated two system configurations: first, a traditional setup using only a diesel generator; second, an OTEC/Diesel/BESS hybrid configuration was evaluated under two scenarios for different fish farm sizes. The results show that scaling up the fish farm and hybrid OTEC-diesel system with energy storage significantly lowers the LCOE. Furthermore, regulating the working fluid’s mass flow rate cuts energy waste by nearly 20% compared to single-level optimization, which reduces environmental impact, and positions this hybrid system as a sustainable and cost-effective alternative to diesel-powered fish farms.

Keywords: Energy systems, Power systems, Optimization
Abstract: The increasing integration of distributed energy resources (DERs) and the rising frequency of extreme weather events present significant challenges to the resilience of distribution systems. Virtual Power Plants (VPPs) offer a promising solution by providing the grid with flexible demand or generation support. This flexibility can be quantified by Dynamic Operating Envelopes (DOEs), which define the upper and lower power bounds of distributed assets. This paper investigates the impact of heat and cold waves on DOEs in distribution systems that incorporate VPPs. VPPs are modeled using high-fidelity, weather-dependent simulations of load and DERs to capture how extreme temperatures affect both demand and generation. Realistic weather data is used to explore how these conditions change the system conditions and then propagate to DOE results. This paper employs a modified IEEE 123-bus system to simulate heat and cold waves in King County, Washington State. Results indicate that extreme weather events decrease the DOEs and limit system flexibility. However, VPP integration enhances operational flexibility and improves resilience, although the benefits are not uniformly distributed across all nodes in the system.

Keywords: Distributed control, Reinforcement learning, Game theory
Abstract: Learning in games provides a powerful framework to design control policies for self-interested agents that may be coupled through their dynamics, costs, or constraints. We consider the case where the dynamics of the coupled system can be modeled as a Markov potential game. In this case, distributed learning ensures agents' control policies converge to a Nash equilibrium. However, standard algorithms like natural policy gradient require global state and action knowledge, which does not scale well with more agents. We show that by limiting information flow to local neighborhoods, we can still converge to near-optimal policies. If a game’s global cost function can be decomposed into local costs that align with agent policies at equilibrium, this approach benefits team coordination. We demonstrate this with a sensor coverage problem.

Keywords: Mean field games, Game theory, Control applications
Abstract: We model a Stackelberg game in a power market with rational consumers and a benevolent regulator as a mean-field game. The Stackelberg leader, who is a government regulator, sets the grid distribution fees so as to maximize the total welfare of the consumers, while also ensuring the solvency of the electricity producers and while satisfying renewable production targets. The Stackelberg followers, who are rational prosumers of electricity, maximize their personal utilities by choosing their individual Photovoltaics investments that provides an alternative to buying electricity from the grid, and hence can also produce electricity. With the representative prosumer’s demand evolving as an Ornstein-Uhlenbeck process, we find a closed form mean-field game approximation to prosumer’s optimal strategy, and use that to calculate the optimal fees set by the regulator. Using these we numerically investigate and explain the influence of various market conditions on the optimal distribution fees.

Keywords: Modeling, Game theory
Abstract: We explore the evolution of trust and trustworthiness in bounded rational agents via the Logit dynamics. We focus on the N-player trust game, where multiple investors and trustees interact and make decisions based on their level of trust and trustworthiness, respectively. We characterize the equilibria of the dynamics under different parameter regimes and establish their stability properties. We show how parameters associated with rationality, incentives, and synergy influence the emergence of trustworthy behavior. Additionally, we show when investors’ contributions are synergistic, then the dynamics may exhibit a limit cycle emerging from a Hopf bifurcation. We illustrate our theoretical results and provide additional insights into these dynamics through numerical simulations.

Keywords: Evolutionary computing, Network analysis and control, Game theory
Abstract: Cooperation plays a fundamental role is societal and biological evolution, and the population structure can significantly influence the dynamics of cooperative behaviors. While mathematical frameworks have existed for studying evolutionary games on graphs with pairwise interactions, exploration of strategy evolution on hypergraphs is relatively limited despite the widespread occurrence of multi-group (higher-order) interactions. Here, we propose the higher-order strategy update pattern based on two-stage selection of a imitating object, governing the propagation of cooperation on hypergraphs. Aiming at two specific update mechanisms, we establish mathematically rigorous conditions for cooperation in public goods games on any weighted hypergraph, achieved by adapting the higher-order random walk into the coalescence theory. These analytical conditions are in good agreement with the experimental consequences of Monte Carlo simulations, regarding both the random hypergraphs and the empirical higher-order networks. Furthermore, we surprisingly identify that one of the higher-order rule of strategy updates – selecting a high-performing group and then selecting a random member in it for imitation – can profoundly promotes the emergence of cooperation. These findings underscore a crucial role of higher-order interactions in modeling and controlling social and biological systems, favoring collective cooperation.

Keywords: Decentralized control, Agents-based systems, Game theory
Abstract: In this paper, we propose a multilateral negotiation protocol for multi-agent task allocation problems. The protocol extends the Monotonic Concession Protocol (MCP), which was originally limited to two-player settings. For more than two players settings, we generalize several key concepts of MCP, including concession, agreement, and risk. Specifically, by introducing a notion of generalized risk, agents can systematically rank among various task allocations. The generalized risk metric is then employed to form a Nash strategy, leading the negotiation towards a task partition that maximizes the Nash product of the agents' utilities. The proposed mechanism ensures that the negotiation process is fully distributed, with agents acting in a completely multilateral manner, meaning their actions, roles, state, and information are symmetric. We demonstrate the performance and features of the proposed protocol through a three-player task negotiation example, which shows that the negotiation monotonically converges towards a task allocation that maximizes the Nash product as the negotiation round progresses.

Keywords: Optimal control, Distributed control, Numerical algorithms
Abstract: Mean-field control (MFC) problems aim to find the optimal policy to control massive populations of interacting agents. These problems are crucial in areas such as economics, physics, and biology. We consider the non-local setting, where the interactions between agents are governed by a suitable kernel. For N agents, the interaction cost has O(N^2) complexity, which can be prohibitively slow to evaluate and differentiate when N is large. To this end, we propose an efficient primal-dual algorithm that utilizes basis expansions of the kernels. The basis expansions reduce the cost of computing the interactions, while the primal-dual methodology decouples the agents at the expense of solving for a moderate number of dual variables. We also demonstrate that our approach can further be structured in a multi-resolution manner, where we estimate optimal dual variables using a moderate N and solve decoupled trajectory optimization problems for large N. We illustrate the effectiveness of our method on an optimal control of 5000 interacting quadrotors.

Keywords: Automata, Discrete event systems, Fault detection
Abstract: This paper extends the results on repeated fault diagnosability of monolithic discrete-event systems to systems with decentralized structure. To this end, the definition of K-codiagnosability, which basically consists of ensuring that at least one local diagnoser is able to accurately determine whether a fault event has occurred at least K times is introduced. Moreover, a necessary and sufficient condition for K-codiagnosability of regular languages is presented and an algorithm with polynomial-time complexity is proposed for its verification. A numerical examples illustrates the efficiency of the proposed method.

Keywords: Multivehicle systems, Predictive control for nonlinear systems
Abstract: Large-spacing truck platooning strikes a balance between maintaining the benefits of platooning—such as the fuel efficiency and emissions reduction—while addressing safety, flexibility, and operational challenges of close-spacing systems. To improve the performance of large-spacing truck platooning under windy conditions, a nonlinear model predictive controller (NMPC) is developed. This controller ensures platooning safety while optimizing fuel efficiency and reducing tailpipe nitrogen oxides (NOx) emissions. Simulations of a two-truck platoon with a 3-sec time gap under windy conditions were conducted, using models validated with on-road experimental data. The results demonstrate that the proposed NMPC effectively keeps spacing errors within the preset safety buffer. Moreover, the follower truck achieves a fuel saving of 5.4% in the Alberta Highway 2 driving cycle with headwinds, and tailpipe NOx emissions are reduced by up to 56.8% by suppressing rapid engine torque changes. This study offers valuable insights into the feasibility of deploying large-spacing platoons in windy environments.

Keywords: Automotive control, Constrained control, Human-in-the-loop control
Abstract: This manuscript presents a control barrier function based approach to shared control for preventing a vehicle from entering the part of the state space where it is unrecoverable. The maximal phase recoverable ellipse is presented as a safe set in the sideslip angle-yaw rate phase plane where the vehicle's state can be maintained. An exponential control barrier function is then defined on the maximal phase recoverable ellipse to promote safety. Simulations demonstrate that this approach enables safe drifting,that is, driving at the handling limit without spinning out. Results are then validated for shared control drifting with an experimental vehicle in a closed course. The results show the ability of this shared control formulation to maintain the vehicle's state within a safe domain in a computationally efficient manner, even in extreme drifting maneuvers.

Keywords: Multivehicle systems, Formal verification/synthesis, Automotive control
Abstract: We consider the coordination of a fleet of tractor trucks to manage trailers in a large warehouse complex and propose an approach that leverages Metric Temporal Logic (MTL) to describe missions to be executed. Each mission includes multiple tasks, such as reaching a trailer, connecting to it, moving it to a sequence of specific warehouse regions, such as loading docks, internal holding areas, and departure parking lots, and eventually disconnecting from it. The electric-powered tractor trucks must also be recharged by visiting charging stations. The MTL formulation avoids an operator manually designing a mission specification, which can quickly become unfeasible with many requests and possible assignments of tractor trucks. MTL specifications and motion dynamics are formulated as a mixed integer linear programming (MILP) approach, where the cost function includes performance objectives such as minimizing the trailer motions and energy-efficient usage. Since missions are added and removed during operation and to also reduce the computation time, we modify the method to allow for a receding horizon approach that allows for partial satisfaction of the MTL specification and uses the cost function to favor the progress towards completion of partially satisfied specifications. We compare different MILP formulations in simulations.

Keywords: Automotive control, Robust control, Machine learning
Abstract: Designing controllers for obtaining optimal performance from a multi-fuel compression ignition engine is a challenging problem. Multiple actuators are used to achieve desired combustion phasing across various operating conditions. Traditionally, feedforward maps are used, which are built by performing experiments at different operating conditions in steady-state. In recent years, the use of data-driven models to construct FF maps has gained traction. However, performance using FF maps while moving between steady-state points and in the presence of disturbance is not guaranteed. This paper explores the design of feedback controllers for reducing the transients observed due to uncertain actuator dynamics while moving between steady-state points and in the presence of disturbance. Robust control framework was used for controller design, and simulations representing scenarios that can be seen during the engine operation were used to demonstrate the effectiveness of the developed controllers.

Keywords: Nonholonomic systems, Modeling, Mechanical systems/robotics
Abstract: Trajectory tracking with an autonomous unicycle is considered in three-dimensional space. It is shown that with the appropriate choice of pseudo-velocities the lateral and longitudinal dynamics and control can be decoupled at the linear level. Linear state feedback controllers are designed separately for lateral and longitudinal subsystems and these controllers are tested simultaneously for the nonlinear model via numerical simulations.

Keywords: Optimization algorithms, Optimization, Cooperative control
Abstract: We revisit the so-called distributed two-time-scale stochastic gradient method for solving a strongly convex optimization problem over a network of agents in a bandwidth-limited regime. In this setting, the agents can only exchange the quantized values of their local variables using a limited number of communication bits. Due to quantization errors, the existing best known convergence results of this method can only achieve a suboptimal rate mathcal{O}(1/sqrt{k}), while the optimal rate is mathcal{O}(1/k) under no quantization, where k is the time iteration. The main contribution of this paper is to address this theoretical gap, where we study a sufficient condition and develop an innovative analysis and step-size selection to achieve the optimal convergence rate mathcal{O}(1/k) for the distributed gradient methods given any number of quantization bits. We provide numerical simulations to illustrate the effectiveness of our theoretical results.

Keywords: Optimization algorithms, Optimization
Abstract: Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradient descent update followed by a retraction operation on the Stiefel manifold, which can be computationally expensive. Recently, an infeasible retraction-free approach, termed the landing algorithm, was proposed as an efficient alternative. Motivated by the common occurrence of orthogonality constraints in tasks such as principle component analysis and training of deep neural networks, this paper studies the landing algorithm and establishes a novel linear convergence rate for smooth non-convex functions using only a local Riemannian PŁ condition. Numerical experiments demonstrate that the landing algorithm performs on par with the state-of-the-art retraction-based methods with substantially reduced computational overhead.

Keywords: Spacecraft control, Optimal control
Abstract: This paper presents an optimal control solution for an aerocapture vehicle with two control inputs, bank angle and angle of attack, referred to as augmented bank angle modulation (ABAM). We derive the optimal control profiles using Pontryagin’s Minimum Principle, validate the result numerically using the Gauss pseudospectral method (implemented in GPOPS), and introduce a novel guidance algorithm, ABAMGuid, for in-flight decision making. High-fidelity Monte Carlo simulations of a Uranus aerocapture mission demonstrate that ABAMGuid can greatly improve capture success rates and reduce the propellant needed for orbital correction following the atmospheric pass.

Keywords: Spacecraft control, Adaptive control, Lyapunov methods
Abstract: The stability and accuracy of satellite missions in space are significantly reliant on satellite attitude control. This paper presents a comprehensive study of satellite attitude tracking using Coulombic actuators and back-stepping adaptive control using signal-chasing analysis. Mathematical models which describe the dynamics and kinematics of satellite motion, and the features of the Coulombic actuator are discussed. The Lyapunov function is used to show that the closed-loop system is stable and that it is converging toward the desired equilibrium point. To manage parameter uncertainty, the backstepping adaptive control approach is used, assuring robustness and stability. Finally, extensive simulation results are presented to evaluate the performance of the proposed approach.

Keywords: Optimal control, Spacecraft control, Constrained control
Abstract: In astrodynamics and space flight mechanics problems, it is known that the choice of coordinates (and/or elements) has a significant influence on the convergence performance of the Hamiltonian/indirect boundary-value problems (HBVPs). It is oftentimes required to enforce the orbit-departure (or orbit-insertion) boundary conditions in terms of the classical orbital elements (COEs), which are more intuitive compared to the modified equinoctial elements (MEEs). In trade studies, it is quite common to seek an answer to the following question: What is the best orbit geometry to which a spacecraft can be transferred? We propose an easy-to-implement approach to enforcing orbit-departure/insertion transversality conditions using the costate mapping theory. This approach allows practitioners to formulate HBVPs using the set of MEEs (which is advantageous from a computational performance point of view) while enforcing the boundary conditions using the more intuitive COEs. Application of the method is demonstrated for generating minimum-fuel low-thrust trajectories between geocentric elliptical and near-circular orbits.

Keywords: Adaptive control, Spacecraft control, Closed-loop identification
Abstract: We consider detumbling for a rigid spacecraft with 3, 2, or 1 torque actuators. The inertia matrix of the spacecraft is assumed to be unknown, and the axes of the torque actuators and angular-rate sensors are assumed to be mutually orthogonal but are otherwise unknown. Predictive cost adaptive control (PCAC) is applied to this system using online system identification and receding-horizon optimization. PCAC is applied to Euler's equation as a sampled-data controller. The goal of this numerical investigation is to assess the effectiveness of PCAC for fully and underactuated detumbling with high modeling uncertainty and hard constraints on the torque magnitude and torque rate.

Keywords: Process Control, Chemical process control, Biotechnology
Abstract: The 21st century society faces significant sustainability challenges, including climate change, resource depletion, and environmental degradation. Microalgae have emerged as a versatile and sustainable solution to mitigate these problems. The microalgae production process involves harnessing their ability to convert sunlight, carbon dioxide, and nutrients (contaminants in wastewater media or agriculture effluents) into valuable biomass through photosynthesis, thus providing a synergy between biomass production and environmental remediation. When microalgae are produced, the culture conditions, including temperature, light intensity, nutrient concentration, dissolved oxygen, and pH, are critical factors that must be carefully controlled to ensure optimal microalgae growth. The growth dynamics of microalgae is influenced by a multitude of interacting factors that, together with the biological nature of microalgae and the variability of weather conditions, pose a significant barrier to the implementation of effective control algorithms. This tutorial paper summarizes the results of more than 20 years of experience working in the scale-up and development of modeling, control, and optimization methods for microalgae production systems. Theoretical and experimental results at industrial scale will be presented to show the potential of microalgae production systems and how control engineering solutions can contribute to make them more competitive in the market for bioproduct generation and wastewater regeneration.

Keywords: Robust control, Mechatronics, Adaptive control
Abstract: To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework for these mechanisms. This framework is intended to be applicable across all actuator systems encompassing electrical, hydraulic, or pneumatic servomechanisms, while also functioning within complex interactions among dynamic components and adhering to control input constraints. In this respect, the state-space model of actuator systems is decomposed into smaller subsystems that incorporate the first principle equation of actuator motion dynamics and interactive energy conversion equations. This decomposition operates under the assumption that the comprehensive model of the servo-driven actuator system and energy conversion, uncertainties, load disturbances, and their bounds are unknown. Then, the GRC employs subsystem-based adaptive control strategies for each state-variant subsystem separately. Despite control input constraints and the unknown interactive system model, the GRC-applied actuator mechanism ensures uniform exponential stability and robustness in tracking desired motions. It features straightforward implementation, experimentally evaluated by applying it to two industrial applications.

Keywords: Robust control, Time-varying systems, Output regulation
Abstract: We prove finite-time input-to-state stability estimates for discrete-time time-varying linear systems in closed loop with sample-data control laws. The upper bounds for the norms of the states in the estimates are suprema of the uncertainties over time intervals of constant finite lengths. We cover output feedback stabilization and input delays. We combine our results with a trajectory based approach, to prove novel global exponential input-to-state stability estimates for nonlinear systems with state delays. We illustrate our work using a dynamics containing an unknown nonlinearity and an unknown state delay.

Keywords: Stochastic systems, Optimal control, Uncertain systems
Abstract: To address deviations from expected performance in stochastic systems, we propose a risk-sensitive control synthesis method to minimize certain risk measures over the limiting stationary distribution. Specifically, we extend Worst-case Conditional Value-at-Risk (W-CVaR) optimization for Linear Time-invariant (LTI) systems to handle nonzero-mean noise and affine controllers, using only the first and second moments of noise, which enhances robustness against model uncertainty. Highlighting the strong coupling between the linear and bias terms of the controller, we reformulate the synthesis problem as a Bilinear Matrix Inequality (BMI), and propose an alternating optimization algorithm with guaranteed convergence. Finally, we demonstrate the numerical performance of our approach in two representative settings, which shows that the proposed algorithm successfully synthesizes risk-sensitive controllers that outperform the naive LQR baseline.

Keywords: Fault tolerant systems, Linear systems, Constrained control
Abstract: Cyber-physical systems are prone to sensor attacks that can compromise safety. A common approach to synthesizing controllers robust to sensor attacks is secure state reconstruction (SSR)---but this is computationally expensive, hindering real-time control. In this paper, we take a safety-critical perspective on mitigating severe sensor attacks, leading to a computationally efficient solution. Namely, we design feedback controllers that ensure system safety by directly computing control actions from past input-output data. Instead of fully solving the SSR problem, we use conservative bounds on a control barrier function (CBF) condition, which we obtain by extending the recent eigendecomposition-based SSR approach to severe sensor attack settings. Additionally, we present an extended approach that solves a smaller-scale subproblem of the SSR problem, taking on some computational burden to mitigate the conservatism in the main approach. Numerical comparisons confirm that the traditional SSR approaches suffer from combinatorial issues, while our approach achieves safety guarantees with greater computational efficiency.

Keywords: Stochastic optimal control, Iterative learning control, Reinforcement learning
Abstract: Risk-sensitive control balances performance with resilience to unlikely events in uncertain systems. This paper introduces ergodic-risk criteria, which capture long-term cumulative risks through probabilistic limit theorems. By ensuring the dynamics exhibit strong ergodicity, we demonstrate that the time-correlated terms in these limiting criteria converge even with potentially heavy-tailed process noises as long as the noise has a finite fourth moment. Building upon this, we proposed the ergodic-risk constrained policy optimization which incorporates an ergodic-risk constraint to the classical Linear Quadratic Regulation (LQR) framework. We then propose a primal-dual policy optimization method that optimizes the average performance while satisfying the ergodic-risk constraints. Numerical results demonstrate that the new risk-constrained LQR not only optimizes average performance but also limits the asymptotic variance associated with the ergodic-risk criterion, making the closed-loop system more robust against sporadic large fluctuations in process noise.

Keywords: Autonomous systems, Constrained control, Learning
Abstract: We present an interactive motion planner that integrates online learning of human driver preferences with parametric control barrier functions. Using stochastic models with Gaussian disturbances to capture human-driven vehicle behavior uncertainty, we update parameters in real-time parameter by Kalman filtering while ensuring safety by control barrier functions. A case study on highway lane-changing tasks demonstrates improved traffic flow, reduced disruptions, and lighter actuation effort compared to non-adaptive algorithms.

Keywords: Distributed parameter systems, Cooperative control, Traffic control
Abstract: This paper investigates the consensus problem of hyperbolic multi-agent systems (MASs) under Markov switching topologies. Firstly, we propose a boundary feedback consensus protocol for the hyperbolic MASs of conservation laws, in which the communication topology of the agents is subject to a Markov chain. Secondly, by employing the Lyapunov approach, we provide the consensus analysis under Markov switching topologies, obtaining sufficient conditions w.r.t. the boundary control matrices, Laplacian matrices and generator of Markov process for ensuring the exponential mean-square consensus. To simplify the inequality conditions, we further combine spectral decomposition techniques with the Lyapunov approach to establish sufficient conditions w.r.t. Laplacian eigenvalues, which are more tractable. Finally, we apply the proposed boundary consensus protocol to synchronize a multi-lane road traffic flow system under Markov switching topologies, and present numerical simulation results to illustrate the effectiveness of the boundary consensus protocol.

Keywords: Distributed parameter systems, Adaptive control, Numerical algorithms
Abstract: This paper proposes a closed-form adaptive tracking control approach for linear heat equations with unknown parameters to achieve full temperature profile tracking by leveraging Fourier regularization and bi-orthogonal series. A state predictor which copies the plant with unknown parameters replaced by their estimates is built and an adaptive law is designed to estimate the unknown parameters. The state predictor is decomposed into two subsystems for tracking control synthesis: the first subsystem involves terms from the original heat equation, while the second subsystem is simpler and can be reformulated as a standard heat equation. Specifically, the first subsystem is regarded as an unforced PDE whose terminal states always follow the desired temperature profile such that its initial condition can be calculated by solving the backward heat equation at every time step. To address the blow-up issue in backward calculation, a Fourier regularization scheme is explored to cut off the higher-order Fourier modes and an appropriate tradeoff between approximation accuracy and robustness is achieved. Given the solutions from the first subsystem, the initial condition for the second subsystem can be subsequently calculated. We propose a numerical algorithm to calculate a set of bi-orthogonal series offline and employ them to compute the boundary control function that drives the second subsystem to zero at every time step. Combining these two subsystems, it guarantees that the overall system follows the desired temperature profile. We demonstrate that the proposed closed-form adaptive tracking control algorithm achieves full temperature profile tracking with <2% error.

Keywords: Distributed parameter systems, Linear systems, Chemical process control
Abstract: This work addresses the design of an output regulator for a three-dimensional transport-reaction model in cylindrical coordinates described by a first-order hyperbolic partial differential equation. By utilizing the Laplace transform in space and time a closed-form semigroup operator is derived to capture the system’s evolution in time, and the Lyapunov equation is solved to address the stability analysis. An output regulator is designed to track periodic signals generated by a finite-dimensional exosystem. This is achieved by solving the continuous-time Sylvester output regulation equation. Numerical simulations demonstrate the controller’s efficiency in properly tracking the desired signal.

Keywords: PID control, Mechatronics, Process Control
Abstract: It is not uncommon for graduate students on the mechatronics side of the control world to treat the Proportional plus Integral plus Derivative (PID) controller with a certain amount of disdain. This is not surprising since most control texts from this end of the control world treat PIDs as simple, basic structures, to be quickly replaced by more advanced methods. To that end, these texts devote only a handful of pages to the subject. It seems that – at least in the mechatronics world – PIDs are considered too simple for much interest in academia while practicing engineers do not seem to care why they were working.

This is a far cry from the treatment of PIDs in the chemical and bio-process control worlds (CPC and BPC, respectively). At this end of the control spectrum, PID controllers are studied in far more depth obtaining entire books or book series. Despite this volumetric expansion of material, it seems that in the latter worlds, many of the issues and concerns one sees in the mechatronic world are treated as obscure corner cases. Depending upon the teaching text, issues of sampling and digital representation may have been completely omitted. There were other surprises. While PIDs were almost universal and standard, they were almost never unified or standardized. Furthermore, what seemed to limit performance was not the structure of the controller itself, but the lack of accurate system/process models based on repeated physical system measurements.

However, the mechatronic and process PID goals and foibles were not that different once one considered the different system, time constant, and measurement constraints. We will discuss these issues with the goal of getting a more unified view of PIDs across our application domains. We will provide a handful of common PID forms and show how they are related, so that we can approach any PID structure with the same analytical approach. We will finally look forward to how PIDs can be used, not only as a fundamental teaching tool for explaining control outside of our research circles, but as a critical component for advanced control methods.

Keywords: Estimation, Filtering, Kalman filtering
Abstract: This letter introduces a Kalman Filter framework for systems with process noise and measurements characterized by state-dependent, nonlinear conditional means and covariances. Estimating such general nonlinear models is challenging because traditional methods, such as the Extended Kalman Filter, linearize only functions – not noise – and require state-independent covariances. These limitations often necessitate Bayesian approaches that rely on specific distribution assumptions. To address these challenges, we propose a framework that employs a recursive least squares method that relies solely on conditional means and covariances, eliminating the need for explicit probability distributions. By applying first-order linearizations and incorporating targeted modifications to manage state dependence, the filter simplifies implementation, reduces computational demands, and provides a practical solution for systems that deviate from the assumptions underlying traditional Kalman filters. Simulation results on a compartmental model demonstrate performance comparable to sequential Monte Carlo methods while significantly lowering computational costs, effectively addressing real-world challenges of scalability and precision.

Keywords: Statistical learning, Behavioural systems, Identification for control
Abstract: In this paper, we study the noise sensitivity of the semidefinite program (SDP) used in the direct data-driven infinite horizon linear quadratic regulator (LQR) problem for discrete-time linear time-invariant systems. While this SDP is shown to find the true LQR controller in the noise-free setting, we show that it leads to a trivial solution when data is corrupted by noise, even when the noise is arbitrarily small. Hence, a “certainty equivalence” approach that uses the original SDP with noisy data is not appropriate.

Keywords: Cooperative control, Robust adaptive control, Output regulation
Abstract: Cooperative output regulation (COR) for multi-agent systems (MAS) has garnered significant attention due to its broad applications. This paper offers a fresh perspective on the COR problem for a class of heterogeneous, uncertain, linear SISO MAS facing two major challenges simultaneously: (1) the agents are highly uncertain and heterogeneous, and (2) communication is restricted to a directed spanning tree with only output information exchanged among agents. We propose a novel plug-and-play cooperative feedforward disturbance compensator that requires little prior knowledge of follower agents' dynamics. Unlike traditional methods, our compensator is fully distributed, adaptive, and highly robust to agent heterogeneity. It eliminates the need for system identification and handles large uncertainties without relying on typical assumptions such as minimum phase, identical dimensionality, or uniform relative degree across agents. Moreover, it is designed for scalability, allowing the seamless addition or removal of agents without the need for controller redesign, provided the network maintains a spanning tree. Theoretical analysis and simulations demonstrate the compensator's effectiveness in solving the COR problem across various scenarios.

Keywords: Distributed control, Cooperative control, Autonomous robots
Abstract: This paper presents a distributed coverage control law for heterogeneous agents to achieve higher-order Voronoi coverage. Unlike most existing works, which assume that an event occurring at a certain location within the domain requires only a single homogeneous agent’s response, we are motivated by applications where multiple agents of different types are required to respond to the event. In this paper, we introduce a partitioning method designed for heterogeneous agents in higher-order Voronoi coverage. Additionally, we present various forms of coverage cost functions, each tailored to meet the diverse needs of different applications. Based on these, we develop a controller that enables heterogeneous multi-agent systems to achieve equilibrium in a distributed manner. The convergence of the control law is proved and the performance is evaluated through simulations.

Keywords: Large-scale systems, Stochastic systems, Autonomous systems
Abstract: Quantifying long-term risk in large-scale multi-agent systems is critical for ensuring safe operation. However, the high dimensionality of these systems and the rarity of risk events can make the required computations prohibitively expensive. To overcome this challenge, we introduce a graph-based representation and efficient risk quantification techniques tailored for stochastic multi-agent systems. A key technical innovation is a systematic approach to decompose the estimation problem of system-wide safety probabilities into smaller, lower-dimensional sub-systems with sub-safe sets. This decomposition leverages the graph Fourier basis of the agent interaction network, providing a natural and scalable representation. The safety probabilities for these sub-systems are derived as solutions to a set of low-dimensional partial differential equations (PDEs). The proposed decomposition enables existing risk quantification approaches but does so without an exponential increase in computational complexity with respect to the number of agents. The proposed PDE characterization allows physics-informed learning to be used to estimate long-term risk probability using short-term samples or without sufficient risk events.

Keywords: Modeling
Abstract: Twisted string actuators (TSAs) have shown strong promise in emerging applications, such as soft robotics and assistive robotics. To construct compact and lightweight TSAs, it is often inevitable to use low-torque and low-speed motors. An accurate TSA model can facilitate the appropriate design of motor-string components and enable TSA’s reliable operation. However, existing models often neglect the twisted strings’ friction and the exerted opposing torque on the motor, which would result in significant discrepancies in predicting the dynamic behaviors of TSAs with low-torque and low-speed motors. This work presents an enhanced model to accurately capture TSA’s dynamics by accounting for the aforementioned phenomena. The theory of torsional closed-wrapped helical springs is used to capture the friction between the twisted strings. The total elastic potential energy of the strings considering string curvature is used to derive the total opposing torque exerted by the twisted strings. The proposed model is experimentally identified, validated, and compared with an existing TSA model to confirm its superior accuracy.

Keywords: Robotics, Modeling, Control applications
Abstract: Twisted and coiled actuators (TCAs) have found extensive application in robotic devices due to their inherent compliance, large force generation, and lightweight characteristics. These attributes endow the TCA-driven robotic arm with significant potential in human-robot interaction. This paper presents a TCA-actuated robotic arm system, where one module emulates the motion of the human upper arm with an elbow joint and the other mimics the wrist, resulting in a highly anthropomorphic structural design. For such a robotic arm system, this paper develops both a motion kinematics model and a Lagrangian dynamics model. Additionally, a nonlinear model predictive controller (NMPC) is designed for trajectory tracking while ensuring the safe TCA actuation of the dynamic system. Experiments are systematically conducted, the results of which demonstrate that the robotic arm system offers a wide range of motion, capable of tracking three-dimensional reference movements with relatively high accuracy.

Keywords: Sensor networks, Optimal control, Automotive systems
Abstract: Frequency-modulated continuous wave (FMCW) radars are increasingly installed on car doors to enhance collision avoidance. Determining the optimal placement of these radars to effectively cover the movements of both doors, while accounting for their opening and closing dynamics, presents a significant challenge. This work focuses on developing a deployment strategy for FMCW radars that addresses three worst-case scenarios: both front and rear doors closed, one door open while the other remains closed, and vice versa. For each scenario, corresponding costs are defined to evaluate the radar’s coverage performance. These costs are incorporated into an integrated cost function that assesses overall coverage efficacy across multiple car door scenarios. The cost function is then maximized using the Luus-Jaakola algorithm to derive an optimized deployment solution. The effectiveness of this optimized deployment is validated through simulations, with comparisons to commonly used strategies, demonstrating the improvements achieved by the proposed approach.

Keywords: Estimation, Biomedical, Biological systems
Abstract: Background: Effective management of body fluid volumes and precise ultrafiltration (UF) prescription are critical challenges in treating Chronic Kidney Disease (CKD) patients undergoing hemodialysis (HD). Current fluid estimation techniques rely on fluid infusion or restricted UF protocols, which are difficult to implement consistently in daily clinical practice. Objective: This work aims to evaluate whether current blood concentration measurement techniques can identify fluid and absolute blood volumes during regular HD treatments with standard ultrafiltration (UF) profiles (constant rates). Methods: The proposed method is independent of any specific hematocrit sensor, UF rate, or volume infusion protocol. It utilizes modeling and prediction algorithms to quantify errors in fluid volume estimations. Results: The method was tested on model-generated data from two patients under constant UF profiles. Extracellular (plasma and interstitial) fluid and absolute blood volumes were accurately estimated. In one case, specific blood volume dropped from 65 mL/kg to 61 mL/kg, while in the other, it remained above the critical threshold of 65 mL/kg. Conclusion: This estimation algorithm can be easily integrated into existing HD machines, potentially improving treatment outcomes for CKD patients.

Keywords: Autonomous robots, Hybrid systems
Abstract: We propose a hybrid feedback control scheme for the autonomous robot navigation problem in three-dimensional environments with arbitrarily-shaped convex obstacles. The proposed hybrid control strategy, which consists in switching between the move-to-target mode and the obstacle-avoidance mode, guarantees global asymptotic stability of the target location in the obstacle-free workspace. We also provide a procedure for the implementation of the proposed hybrid controller in a priori unknown environments and validate its effectiveness through simulation results.

Keywords: Optimization, Robotics, Control applications
Abstract: This letter aims to generate a continuous-time trajectory consisting of piecewise Bézier curves that satisfy signal temporal logic (STL) specifications with piecewise time-varying robustness. The time-varying robustness is less conservative than the real-valued robustness, which enables more effective tracking in practical applications. Specifically, the continuous-time trajectories account for dynamic feasibility, leading to smaller tracking errors and ensuring that the STL specifications can be met by the tracking trajectory. Comparative experiments demonstrate the efficiency and effectiveness of the proposed approach.

Keywords: Nonlinear output feedback, Observers for nonlinear systems, Constrained control
Abstract: In this work, we introduce a low-complexity output-feedback control scheme imposing prescribed performance characteristics for unknown high-order nonlinear systems. We design a novel robust observer with adaptive gains in order to mitigate undesirable high steady-state gains. The controller incorporates an adaptive mechanism to ensure closed-loop signal boundedness, particularly during transient. Furthermore, we provide a nonlinear separation principle to demonstrate the recovery of closed-loop performance under state-feedback. Simulation results validate the theoretical findings and demonstrate the efficacy of the proposed controller.

Keywords: Autonomous vehicles, Optimal control
Abstract: Energy-efficient driving is a key advancement in the deployment of automated vehicles once safety concerns are addressed. This paper formulates the energy-efficient driving problem with constraints and explores various solution methods for common driving scenarios. The findings, rooted in theory of optimal control and Pontryagin's Minimum Principle (PMP), offer fundamental insights into energy-efficient driving strategies in every-day driving scenarios. Analytical insights from PMP coupled with fast analytical solution of respective boundary value problem, enabled implementation in a real-time control system and near-optimal energy savings. The proposed approach was validated through real vehicle testing on the track, with results demonstrating that automated eco-driving can achieve significant energy savings over human drivers in basic daily driving scenarios. This study not only highlights the effectiveness of the proposed approach but also provides practical guidance for integrating energy-efficient driving strategies into real-world automated driving and advanced driver assistance systems.

Keywords: Automotive control, Automotive systems, Machine learning
Abstract: Linear Parameter-Varying Model Predictive Control has been shown to provide an effective design approach for developing an Energy Management System for Hybrid Electric Vehicles. However, despite the good performance achieved, modern data-driven Artificial Intelligence methods can improve the performance due to the approximations involved in generating the models. An approach is described for reducing the sub-optimality due to the modelling problem in predictive control using an AI algorithm belonging to the class of data-driven Machine Learning techniques. This provides more effective vehicle speed and driver torque demand predictions that are used within the predictive controller. The proposed combined policy is compared with a baseline control design developed using the well-known Equivalent Consumption Minimization Strategy and an MPC neglecting the use of AI predictors.

Keywords: Automotive control, Automotive systems, Optimal control
Abstract: Connected and automated vehicles (CAVs) represent the future of transportation, utilizing detailed traffic information to enhance control and decision-making. Eco-driving of CAVs has the potential to significantly improve energy efficiency, and the benefits are maximized when both vehicle speed and powertrain operation are optimized. In this paper, we studied the co-optimization of vehicle speed and powertrain management for energy savings in a dual-motor electric vehicle. Control-oriented vehicle dynamics and electric powertrain models were developed to transform the problem into an optimal control problem specifically designed to facilitate real-time computation. Simulation validation was conducted using real-world data calibrated traffic simulation scenarios in Chattanooga, TN. Evaluation results demonstrated a 12.80-24.52% reduction in the vehicle's power consumption under ideal predicted traffic conditions. Energy benefits are maintained with various prediction uncertainties, such as Gaussian process uncertainties on acceleration and time-shift effects on predicted speed. The energy savings of the proposed eco-driving strategy are achieved through effective speed control and optimized torque allocation. The proposed model can be extended to various CAV and electric vehicle applications, with potential adaptability to diverse traffic scenarios.

Keywords: Automotive control, Automotive systems
Abstract: Electrified connected and automated vehicles (CAVs) offer advanced prediction capabilities to revolutionize battery thermal management, thereby reducing energy consumption and battery degradation. The main challenges, however, lie in the complexities of coupled multi-objective optimization and multi-timescale dynamics, including both fast vehicle dynamics and slow battery thermal dynamics. In this study, we introduce an integrated power and thermal management strategy designed to enhance energy efficiency while minimizing battery degradation, all while ensuring thermal and traffic safety for CAVs. Leveraging the multi-horizon model predictive control framework, the objective function is reformulated based on the degradation loss term to better address these challenges. Our findings suggest that an effective management strategy requires reducing peak power and scheduling battery cooling when traction power is low to balance the trade-off between cooling energy efficiency and battery degradation loss. Simulation results show the proposed approach achieves a 5.60% reduction in cooling energy, a 3.02% reduction in traction energy, and more than 12% reduction in degradation loss, ensuring optimal energy efficiency and battery longevity across various driving conditions.