Keywords: Robotics
Abstract: Over the last decades industrial robots have been widely deployed in the manufacturing industry, relieving workers from repetitive, unhealthy or arduous jobs with their workspace being strictly separated from that of humans for safety. Collaborative robots is a new generation of industrial robots that can work alongside and with humans as co-workers or assistants. These robots are expected to provide flexibility by extending robot applications beyond the isolated structured work cells. Collaborative robots should provide assistance to humans by reducing their physical and/or cognitive load, while being safe during their co-existence and collaboration with humans. In this talk, we will present shared control applications for resolving the problem of intentional physical human-robot interaction. The presentation will focus on the use of shared control methods for facilitating the human towards the kinesthetic teaching of a task, avoiding forbidden regions, as well as co-manipulating objects. The control methods that will be presented ensure passivity of the velocity output under the exerted human forces.

Keywords: Aerial robotics, Planning, Robotics applications
Abstract: We investigate the problem of computing time-optimal and energy-optimal vertical descent trajectories for quadcopters, while avoiding the Vortex Ring State (VRS). The VRS is a region in the velocity space of the quadcopter in which disturbances on the thrust produced by the propellers result in a reduced control effectiveness and can eventually lead to a crash. A new model is proposed for the VRS dynamics for quadcopters, which is shown to be more complete in comparison with existing approaches. Based on this novel model, time-optimal and energy-optimal trajectories are computed using GPOPS II and compared in a numerical case study.

Keywords: Modeling, Sliding mode control, Robotics applications
Abstract: As surprising as it seems, drones and mobile robots in general experience motion sickness when put in a moving environment. This navigation problem has been little if ever explored in the literature. Therefore we propose a formulation of the problem in the simplest possible way as a starting point. The objective of simplifying the problem is to avoid using sophisticated control and measurement devices, such as cameras, and rely instead on control system strategies. In this paper, the moving environment to which is associated a non-inertial frame is considered to have translation motion with respect to the inertial reference frame. The goal is to make the drone track a desired trajectory inside the moving environment based only on the measurements obtained with respect to the non-inertial frame. First, a model representing the dynamics of the drone in the non-inertial frame is developed using the relative motion principles. The new model takes into account the accelerations of the moving environment where they are considered as bounded unknown inputs. Then, a Kalman Filter with Unknown Inputs (KF-UI) is used to estimate simultaneously the states of the drone and the accelerations of the non-inertial frame. Finally, a Sliding Mode controller is implemented. Two numerical simulations were conducted to illustrate the performance of the combined KF-UI and Sliding Mode controller: the first one represents an ideal case where the non-inertial frame's accelerations are constant. The second one illustrates flying a drone in an elevator. The obtained results form an encouraging foundation for follow-on experiments.

Keywords: Autonomous systems, Simulation, Ships and offshore vessels
Abstract: In this paper, we present a Robot Operating System (ROS) based simulation environment for obstacle avoidance in maritime operations, suitable for different planning algorithms. The chosen algorithm in the current implementation is the Timed Elastic Band (TEB) available in the "TEB_local_planner" package of ROS. It allows to plan paths that are consistent with the kinematic constraints of an Autonomous Surface Vehicle (ASV) and to optimize the planning according to several parameters such as the navigation time and the Cross Track Error. The proposed framework has been validated and its performance assessed via Monte Carlo simulations.

Keywords: Automotive applications, Localization
Abstract: To enable the concept of a cloud-aided proactive suspension control, a precise localization of the vehicle on the street is needed and the accuracy must be significantly better than what global navigation satellite systems can provide. In this paper, we present a method to localize the vehicle in longitudinal direction using the information of the vertical street profile. The method is based on the correlation of a reference street profile from the cloud server with the observed profile gained through the vehicle's sensor data to determine the position of the vehicle relative to the cloud profile. The suitability of the algorithm is demonstrated on a quarter vehicle test bench using real sensor data and street profiles recorded on real roads. Given an accurate reference profile, this setup achieves a very precise localization on a variety of different road types with a mean absolute position error of about 1 cm and the corresponding mean absolute time error of around 1 ms. We also tested the method for robustness and found that local disturbances in the street profile or noisy observer data did not impair the performance. However, the algorithm is rather sensitive to inaccuracies in the measured vehicle speed. Even though we developed the method with respect to the intended application in proactive suspension control, it might also be of interest in other areas like autonomous driving, where high-precision localization is required.

Keywords: Robotics applications, Planning, Control applications
Abstract: Robotic path planning is a crucial step in industrial manufacturing processes on 3D objects. The industry trend of growing product diversity and individual customization requires fully automatic and flexible production systems. In this work, an automatic workflow to execute a drawing process on a 3D object from a user-provided 2D input pattern is presented. The workflow involves a mesh segmentation, a flattening algorithm using least-squares conformal mappings and a projection using barycentric coordinates. Based on the resulting 3D path, a robot trajectory is automatically generated. Two different control concepts, i.e. motion control and hybrid force/motion control, are evaluated experimentally in view of replication accuracy and contact forces.

Keywords: Predictive control, Autonomous systems, Robotics applications
Abstract: Autonomous maneuvering of truck-trailer systems is a challenging task, especially if collisions with obstacles must be avoided. Due to the complexity, global path planners typically focus on the efficient computation of feasible but suboptimal solutions. In this context, this paper presents a model predictive path-following controller for local optimization of the globally planned paths. This allows to eliminate redundant path elements to increase obstacle clearance and to compensate initial path deviations. Furthermore, it is proposed to constrain the terminal state to the reference path and to formulate the terminal costs in terms of a precomputed reference trajectory, both of which improve the numerical robustness of the optimization. Simulation results show the benefits of the optimized solution in a difficult planning scenario.

Keywords: Process control, Control Technology, Control applications
Abstract: This paper aims to review and compare some methods used to control overhead cranes in 2D space. The properties of the controllers are categorized based on their structure, feedback type (no feedback, collocated, and noncollocated feedback), and their stability (local, global, regulation and tracking). Subsequently, a new pendulum-like model has been proposed to describe the system's behavior more accurately. The model consists of a large number of links attached to a cart, allowing to study global nonlinearities as well as cables' flexibility and vibration, simultaneously. The controllers are then studied by extensive numerical simulations under the regulation and tracking scenarios.

Keywords: Robotics, Control architectures, Robotics applications
Abstract: This paper presents a practical implementation and comparison of state-of-the-art methods to achieve stable wireless telerobotics. The commonly used Time-Domain-Passivity-Approach (TDPA) is implemented in 6 DOF between two robots with significantly different inertias. The performances of two TDPA schemes are compared. The main difference between these schemes is the computation of the energy flow. In a first scheme, the external measured force is considered for the computation, while in the other scheme, the control forces are fed back. We clarify the pros and cons of both schemes in two practical case studies. Thus, the selection of the appropriate scheme can be done based on available hardware and requirements of the specific application.

Keywords: Control applications, Reduced order modeling, Nonlinear systems
Abstract: A Lyapunov-based adaptive control law is applied to a reduced-order model for a fluid flow dynamic system, which contains parametric uncertainty in both the plant dynamic model and the actuator model. The reduced-order model is derived using a proper orthogonal decomposition (POD) technique. To generate a control-oriented reduced-order model for the actuated flow dynamics, the POD decomposition is performed using both actuated and unactuated modes. This results in a reduced-order flow dynamic model that is in a non-standard mathematical form. This challenge is mitigated through innovative algebraic manipulation in the regulation error system development along with a Lyapunov-based adaptive control law. To the best of the authors’ knowledge, this is the first result to apply a nonlinear, Lyapunov-based adaptive control law to the complete actuated POD-based reduced-order flow dynamics to formally compensate for input-multiplicative parametric uncertainty. To achieve the result, a rigorous error system development is presented along with a Lyapunov-based stability analysis. To complement the theoretical development, detailed numerical simulation results are also provided, which show the control design trade-off between the adaptive control law and a standard non-adaptive control law.

Keywords: Control applications, Nonlinear robust control, Sliding mode control
Abstract: A barrier function-based sliding mode control method is developed for fluid flow dynamic systems, which formally compensates for the model parameter variations resulting from actuator perturbations that are inherent in closed-loop active flow control applications. In an effort to reduce chattering, the control law incorporates adaptive sliding gain terms based on barrier functions. To the best of the authors’ knowledge, this is the first barrier function-based nonlinear closed-loop active flow control result to prove finite-time real sliding of a reduced-order flow dynamic system, which formally incorporates input-multiplicative time-varying parametric uncertainty. An innovative error system development is provided along with a rigorous Lyapunov-based stability analysis to prove the theoretical results. A detailed comparative numerical study is also provided, which shows the significant reduction in mean squared error that are achieved using the barrier function-based control law over a standard sliding mode controller.

Keywords: Sensors, Control applications, Real-time systems
Abstract: This paper reports early success in using systems theoretic approaches to develop a real-time interpreter for honeybee communication. Foraging Western honeybees share location information of prime food sources through a particular dance. In this work we develop algorithms that translate time-series data of the dancing bees’ locations into parameter estimates of the relevant food sources. The resulting system becomes a component in a future ecological sensor, WaggleChat, for deeper research into bee communication, demonstration of social insect communication to a broad audience, and a first step toward a new, closed-loop approach to pollination control for both agriculture and broader ecological management. Code, models, and more details are available at: https://gitlab.com/idealabs/hb-comm-interpreter.

Keywords: Ships and offshore vessels, Cooperative control, Autonomous systems
Abstract: This paper proposes a novel dynamic coordination control scheme for a physically connected multi-vessel towing system to transport an offshore platform. The transportation process is executed by four tugboats, and each of them has a leading or following role. To render the transportation faster, the roles of the tugboats can be switched in the towing process. The dynamic coordination decision mechanism is designed to allocate in real-time a combination of roles to the tugs by comparing the position and heading of the offshore platform to the next waypoint position. A control allocation strategy is developed to optimally control the position and heading of the tugboats considering multiple constraints. The reference trajectory of the tugboats is dynamically calculated based on the assigned role of each tugboat. A simulation experiment indicates that the proposed control scheme can enhance the maneuverability of the physically connected multi-vessel towing system and increase the efficiency of offshore platform transportation.

Keywords: Fault detection/accomodation, Learning
Abstract: Quality control represents a challenge in the industrial textile finishing field. The general unavailability of labelled data from real production plants and the imbalanced nature of the problem suggest tackling it with a semi-supervised approach that monitors the behaviour of the system and identifies whether shifts from the nominal conditions arise. In particular, we utilize techniques from Elastic Shape Analysis to study the time-series data collected from sensors installed in real production plants and to extract features used to define distance metrics that quantify the data variability. The results of this exploratory study support the efficacy of the proposed strategy.

Keywords: Fault detection/accomodation, Observers
Abstract: Sensor fault detection and reconstruction are essential for efficient performances of systems and for increased reliability in safety-critical processes. A novel fault estimation scheme is proposed for uncertain systems in conjunction with sliding mode observer used for its state estimation. A system is defined using the observer outputs and known inputs to formulate the sensor fault as its unmeasured state. For this system, three observers are designed to estimate the sensor fault; a reduced-order linear observer, a robust sliding mode observer, and a novel norm estimator. The norm observer is an algorithm to estimate the norm bound of the fault. These three observers are validated through simulation exercises.

Keywords: Biosystems, Biotechnology, Kalman filtering
Abstract: This paper develops and validates an extended Kalman filter for estimating the fluid volume retained in a patient’s or a lab animal’s peritoneal cavity during perfusion. Such estimation is potentially valuable for monitoring perfusion effectiveness and patient condition during medical interventions such as peritoneal dialysis. Monitoring intra-abdominal pressure and volume is particularly important for preventing undesirable or unsafe conditions such as intra-abdominal hypertension, abdominal compartment syndrome, etc. The literature already models the dynamics of peritoneal cavity pressure during perfusion. However, to the best of the authors’ knowledge, the use of such models for online perfused volume estimation is a novel contribution of this paper. Specifically, the paper presents a simple model of cavity pressure dynamics, parameterizes it from a perfusion experiment on a large laboratory animal (namely, a Yorkshire swine), and uses it to design an extended Kalman filter for online retained volume estimation. The paper validates its approach by comparing the perfused volume estimate to a benchmark estimate generated using canister fluid level sensors embedded in the perfusion setup. The results of this validation are very encouraging, with 97% correlation between the two volume estimates.

Keywords: Distributed parameter systems, Sensor networks, Localization
Abstract: In standard PPP GNSS, residual atmospheric delay (RAD) is estimated independently at each agent by filtering through time, resulting in estimation periods that are too long for vehicular applications. This paper explores cooperative estimation of local vertical RAD within a network of interacting agents. After formulating the problem, the paper formulates and compares: the individual solution when there is no inter-agent interactions, the centralized solution when all agents interact with a central node, and four distributed algo- rithms (i.e., information-weighted consensus (IWC), covariance intersection (CI), Inverse covariance intersection (ICI), and ellipsoidal intersection (EI)). The role of information shared between agents (i.e., cross-correlation) is specifically considered. The article shows that the distributed methods estimate the common RAD faster than independent agents. This increased accuracy enables faster and more accurate estimation of the unique portion of each agent’s state (e.g., horizontal position).

Keywords: Identification, Semidefinite programming, Kalman filtering
Abstract: This paper derives a BLUE (best linear unbiased estimate) for the estimation of noise covariance matrix in the framework of subspace identification. An estimation method of the noise covariance with CCA (canonical correlation analysis) weighting matrix was previously proposed by the authors. The newly derived BLUE is compared with the previously proposed method numerically and it is shown that the previously proposed method gives good performance.

Keywords: Observers, Kalman filtering, Identification
Abstract: Two Kalman Filter (KF) like algorithms for state estimation of linear systems from multilevel quantized outputs are introduced in this paper. The proposed algorithms are proved to be optimal in the sense that they minimize the a posteriori state estimation error covariance matrix. Monte Carlo simulations are carried out to illustrate their performance for different quantization steps and Signal-to-Noise Ratios. It is also shown how the algorithms can be employed for the parameter estimation of linear systems in linear regression form, from quantized outputs.

Keywords: Observers, Aerial robotics, Adaptive control
Abstract: This article deals with the problem of victim localization in avalanches by using controlled unmanned aerial vehicles (UAVs) equipped with an electromagnetic sensor (known as ARVA) typically adopted in these search and rescue scenarios. We show that the nominal ARVA measurement can be linearly related to a quantity that is sufficient to reconstruct the victim position. We explicitly deal with a robust scenario in which the measurement is actually perturbed by the noise that grows with the distance to the victim and propose an adaptive control scheme based on a least-square identifier and a trajectory generator whose role is both to guarantee the persistence of excitation for the identifier and to steer the ARVA receiver toward the victim. We prove that the controller ensures boundedness of trajectories and enables to localize the victim in a domain where the ARVA output is sufficiently informative. We illustrate its performance in a realistic simulation framework specifically developed with real data. The proposed approach could significantly reduce the searching time by providing an exploitable estimate before having reached the victim.

Keywords: Smart grid, Game theory, Energy Systems
Abstract: By utilizing tools from the game theory, we develop a novel multiperiod-multicompany demand response framework considering the interactions between companies (sellers of energy) and their consumers (buyers of energy). We model the interactions in terms of a Stackelberg game, where companies set their prices and consumers respond by choosing their demands. We show that the underlying game has a unique equilibrium at which the companies maximize their revenues, while the consumers maximize their utilities subject to their local constraints. Closed-form expressions are provided for the optimal strategies of all players. Based on these solutions, a power-allocation game has been formulated, which is shown to admit a unique pure-strategy Nash equilibrium, for which closed-form expressions are also provided. This equilibrium is found under the assumption that companies can freely allocate their power across the time horizon, but we also demonstrate that it is possible to relax this assumption. We further provide a fast distributed algorithm for the computation of all optimal strategies using only local information. We also study the effect of variations in the number of periods (subdivisions of the time horizon) and the number of consumers. As a consequence, we are able to find an appropriate company-to-consumer ratio when the number of consumers participating in demand response exceeds some threshold. Furthermore, we show, both analytically and numerically, that the multiperiod scheme provides incentives for energy consumers to participate in demand response compared with the single-period framework studied by Maharjan et al. In our framework, we provide a condition for the minimum budget consumers need and carry out case studies using real-life data to demonstrate the benefits of the approach, which show potential savings of up to 30% and equilibrium prices that have low volatility.

Keywords: Aerial robotics, Identification, Learning
Abstract: Low-cost real-time identification of multirotor unmanned aerial vehicle (UAV) dynamics is an active area of research supported by the surge in demand and emerging application domains. Such real-time identification capabilities shorten development time and cost, making UAVs' technology more accessible, and enable a wide variety of advanced applications. In this article, we present a novel comprehensive approach, called DNN-MRFT, for real-time identification and tuning of multirotor UAVs using the modified relay feedback test (MRFT) and deep neural networks (DNNs). The main contribution is the development of a generalized framework for the application of DNN-MRFT to higher order systems. One of the notable advantages of DNN-MRFT is the exact estimation of identified process gain, which mitigates the inaccuracies introduced due to the use of the describing function method in approximating the response of Lure's systems. A secondary contribution is a generalized controller based on DNN-MRFT that takes off a UAV with unknown dynamics and identifies the inner loops dynamics in-flight. Using the developed framework, DNN-MRFT is sequentially applied to the outer translational loops of the UAV utilizing in-flight results obtained for the inner attitude loops. DNN-MRFT takes on average 15 s to get the full knowledge of multirotor UAV dynamics, and without any further tuning or calibration, the UAV would be able to pass through a vertical window and accurately follow trajectories achieving state-of-the-art performance. Such demonstrated accuracy, speed, and robustness of identification pushes the limits of state of the art in real-time identification of UAVs.

Keywords: Energy Storage, Observers, Estimation
Abstract: A state-space model for Li-ion battery packs with parallel connected cells is introduced. The key feature of the model is an explicit solution to Kirchhoff's laws for parallel connected packs, which expresses the branch currents directly in terms of the model's states, applied current and cell resistances. This avoids the need to solve these equations numerically. To illustrate the potential of the proposed model for pack-level control and estimation, a method to bound the error of a state-estimator is introduced and the modelling framework is generalised to a class of electrochemical models. It is hoped that the insight brought by this model formulation will allow the wealth of results developed for series connected packs to be applied to those with parallel connections.

Keywords: Automotive applications, Control architectures, Nonlinear systems
Abstract: The use of regenerative braking and recent electrohydraulic or electromechanical brake-by-wire systems with enhanced dynamics enables improved antilock braking control performance in electric vehicles. However, the possible benefit is often limited by suboptimal control architectures with separate distributed controllers for electric motor (EM) and friction brake torque requests. Furthermore, oversimplifications in synthesis models for brake control design constrain the achievable performance. Instead, to date, threshold-based algorithms are widely used in modern production vehicles despite having been designed for slower hydraulic brake systems originally. To exploit the full potential, this work proposes a continuous nonlinear control design using input-output linearization for robust wheel speed tracking control. The design uses a unified controller for the redundant actuators. In addition, it explicitly considers drivetrain oscillations induced by regenerative braking using onboard EMs in the synthesis model. The stability for the resulting zero dynamics is shown through the Lyapunov analysis. The nonlinear controller is augmented by a subsequent control allocation for splitting the control effort on the redundant actuators. The allocation design ensures consistent control performance for both regenerative braking and hybrid braking while simultaneously aiming for high-energy recovery. The overall antilock braking control design is implemented in an electric test vehicle. Its tracking performance, disturbance attenuation, and robustness are experimentally validated through various emergency braking maneuvers on different road surfaces.

Keywords: Optimization, Robotics, Navigation
Abstract: In this paper, we present an efficient algorithm for solving a class of chance constrained optimization under non-parametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to formulate chance constrained optimization as one of minimizing the distance between a desired distribution and the distribution of the constraint functions in the RKHS. We provide a systematic way of constructing the desired distribution based on the notion of scenario approximation. Furthermore, we use the kernel trick to show that the computational complexity of our reformulated optimization problem is comparable to solving a deterministic variant of the chance constrained optimization. We validate our formulation on two important robotic applications: (i) reactive collision avoidance of mobile robots in uncertain dynamic environments and (ii) inverse dynamics based path tracking of manipulators under perception uncertainty. In both these applications, the underlying chance constraints are defined over non-linear and non-convex functions of the uncertain parameters and possibly also decision variables. We also benchmark our formulation with the existing approaches in terms of sample complexity and the achieved optimal cost highlighting significant improvements in both these metrics.

Keywords: Control applications, Manufacturing systems, Predictive control
Abstract: This paper discusses the possibility of making an object that precisely meets global structural requirements using additive manufacturing and feedback control. An experimental validation is presented by printing a cantilever beam with a prescribed stiffness requirement. The printing process is formalized as a model-based finite-horizon discrete control problem, where the control variables are the widths of the successive layers. Sensing is performed by making in situ intermediate stiffness measurements on the partially built part. The hypothesis that feedback control is effective in enabling the 3D-printed beam to meet precise stiffness requirements is validated experimentally.

Keywords: Cooperative control, Power systems, Distributed control
Abstract: Modular multilevel converters (MMCs) are predominant in novel high and medium power applications, such as renewable energy generation and high-voltage direct-current transmission. This hardware offers better performance than other voltage-source-converters due to its higher efficiency, modular design and lower harmonic content. To achieve the expected results, it is essential to coordinate the actions of all the MMC modules using balancing control methods. Different from the previous solutions, this paper proposes a consensus control approach using a leader-follower topology to solve the balancing control problem for MMCs. The proposed control method guarantees asymptotic convergence of all modules to a consensus state, allows a distributed implementation and increases the robustness against module failures. Simulation results using Matlab show the advantages of the developed method.

Keywords: Renewable Energy, Estimation, Machine learning
Abstract: When there exist a cause-effect relationship described by an input-output structural equation, for instance the power curve of a wind turbine, the effect is obviously predicted by computing the effect (generated power) from the structural model fed by the cause (wind speed). What is however going to happen if wind speed is not directly observed and is surrogated by a guess, as in the case of meteorological forecasts? Such a kind of error-in-variables framework is well understood in the linear Gaussian case: the straightforward application of the structural equation does not give optimal predictions, a phenomenon known as regression dilution. In the present work, the practical significance of regression dilution effects in the nonlinear regression of wind power from forecasts of wind speed is assessed through data taken from the Global Energy Forecasting Competition 2012 (GEFCOM2012). It is found that the effect is relevant and some lessons are learned, in particular about the benefit of using regression models tailored to the specific prediction horizon.

Keywords: Energy Systems, Reduced order modeling, Simulation
Abstract: Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Current model order reduction methods offer limited interpretability with regards to the non-reduced system, and are not in general applicable for e.g., varying flow rates, multiple producers, or changing flow directions. This article presents a novel method based on graph theory that approximates the solution of an optimization problem that minimizes the local truncation error for heat transport in the grid. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution. The relative root mean square error was 2.3% for the temperature in the evaluation scenario, comparing the reduced-order system with the non-reduced system at the instances of the coarser time-step.

Keywords: Optimization, Renewable Energy, Reinforcement learning
Abstract: We consider the problem faced by a hydropower plant owner who sells electricity on the spot and balancing power markets. We allow for risk sensitivity and consider the setting of a single power plant connected to a reservoir with a stochastic inflow. Our results indicate that risk-aversion tends to shift trading towards the less risky day-ahead market.

Keywords: Smart grid, Optimization, Distributed control
Abstract: Renewable energies have introduced new challenges in the path to the smart grid with the starting point of traditional distribution systems. A proposal to overcome these challenges is to split distribution systems into many smaller ones, also called microgrids, which are easier to control and operate. However, the interaction and interconnection between these subsystems is a remaining challenge, where these groups of interacting microgrids are called networked microgrids. In this work, we present an energy management system to control and operate networked microgrids, that considers power flow equations for every subsystem, applies to every sort of topology, devolves individual control and operation to each microgrid controller to enhance privacy levels, and avoids the requirement of the distribution system as an interaction coordinator. We first introduce the problem statement using a traditional AC optimal power flow formulation, allowing the energy management system to work over meshed and radial configurations. Then the distributed optimization algorithm based on the alternating direction method of multipliers is presented and validated through some study cases to show its convergence and applicability.

Keywords: Energy Storage, Reduced order modeling, Predictive control
Abstract: A growing interest in the study of aging related phenomena in lithium-ion batteries is propelled by the increasing utilization of energy storage systems in electric vehicles and in buildings as stationery energy accumulators paired with renewable energy sources. This paper proposes a mixed-degradation model approach that combines the benefits of a semi-empirical approach with that of a physics-based model. This enables easy calibration for different battery chemistries, the ability to extrapolate when necessary, and is computationally efficient enough to be coupled with real-time running control systems. To demonstrate the effectiveness of the proposed approach, the effect of two different control strategies in a smart home energy management system is demonstrated on the aging of a Lithium iron phosphate (LFP) battery.

Keywords: Fault detection/accomodation, Modeling, Aerospace applications
Abstract: Cold-gas thrusters are a common choice for the propulsion system of smaller satellites. This paper studies the problem of modeling, fault detection, and fault isolation of a typical cold-gas thruster system. First, the laws of the conservation of mass, energy, and momentum are employed to develop a dynamical model of the thruster system. Then, the dominant sources of uncertainty affecting this model are investigated. Based on the developed model and the structure of uncertainties, a suitable fault-detection and isolation (FDI) technique is selected from the literature and is tailored to the problem at hand. The performance of the FDI technique applied to a cold-gas thruster system is assessed in common fault scenarios. It is shown that the proposed technique is able to robustly detect and isolate these common faults without triggering false positives due to uncertainties and disturbances.

Keywords: Aerospace applications
Abstract: This study proposes an integrated guidance and task assignment methodology to intelligently and smoothly transition from the mid-course to the terminal phase of flight for a many-on-many engagement with a team of aggressors (pursuers) against a team of evaders. More specifically, as the engagements unfold, we develop regions for the pursuers to navigate to based on the concept of Apollonius circles. The goal for the pursuers is to minimize wasted energy from chasing evaders through a zone-based guidance (ZBG) approach, while maximizing end-game intercept through minimization of a cost matrix which balances estimated effort and time-to-go for each pursuer-region pair through user-defined constants. This approach is compared against the same cost matrix without utilizing zones, and against assigning targets a priori. On average, when selecting targets based only on time-to-go, the strategy presented in this work captured the evaders faster, with a smaller cost matrix than the alternative methods.

Keywords: Aerospace applications, Control applications, Nonlinear systems
Abstract: The impossibility of global asymptotic attitude stabilization through continuous feedback is attributed more so to the underlying properties of the attitude space than the control design methodology. This work proposes an augmentation algorithm, PAGAL, that ensures stabilization of the desired equilibrium from any arbitrary initial state. The stability proof of the novel algorithm in the sense of Lyapunov is given. The proposed algorithm also ensures maximum control effort for errors greater than 90 degrees about a given body frame axis. Hence, this ensures a better transient response when encountering large errors. Numerical simulations in MATLAB show the comparisons between the performance of existing controllers and the proposed algorithm. Experiments on unmanned quadrotor helicopters show the real-world applicability of the proposed algorithm.

Keywords: Aerospace applications, PID control, Modeling
Abstract: The mitigation of fuel propellant sloshing on-board satellites or during space launches is of particular interest. Fuel sloshing can indeed significantly affect spacecraft motion by increasing the maneuver settling time and occasionally destabilizing them. This paper presents a novel, model-free attitude control approach that actively compensates for the sloshing disturbance. One of the main novelties consists of using pressure sensors inside the fuel tank to measure a quantity proportional to the sloshing torque. Then, using this measurement, an attitude controller can be designed not only to reject the sloshing torque but also to keep the fuel sloshing within acceptable conditions. Technically speaking, this is achieved by combining model-free control with an output to input saturation transformation (OIST) add-on.

Keywords: Cybersecurity, Cyberphysical systems, Fault detection/accomodation
Abstract: Recent work into quantifying the impact of attacks on control systems has motivated the design of worst-case attacks that define the envelope of the attack impact possible while remaining stealthy to model-based anomaly detectors. Such attacks - although stealthy for the considered detector test - tend to produce detector statistics that are easily identifiable by the naked eye. Although seemingly obvious, human operators cannot simultaneously monitor all process control variables of a large-scale cyber-physical system. What is lacking in the literature is a set of practical detectors that can identify such unusual attacked behavior. In defining these, we enable automated detection of to-date stealthy attacks and also further constrain the impact of attacks stealthy to a set of combined detectors, both existing and new.

Keywords: Observers, Cyberphysical systems, Linear robust control
Abstract: A controller-observer compensator is proposed for Cyber-Physical Systems (CPSs) subject to sensor and actuator faults and communication network disturbances. The observer combines a norm approximator for sparse malicious attacks recovery with a bank of unknown input observers (UIOs) to estimate the CPS state. Convergence analysis of the state estimation error of the proposed UIO architecture is given. To enhance the observer's performance, sensor and actuator fault filters constructed using error correcting code (ECC) techniques are used. A model reference controller with a computable performance level is given. A simulation of the controller-observer compensator in a self-driving ground vehicle is used to demonstrate its effectiveness.

Keywords: Game theory, Smart grid, Optimization
Abstract: This paper proposes an optimal balancing transaction mechanism for dynamic storage management in smart grids in order to minimize the imbalance penalty charge of the grid in the wholesale electricity market. The proposed mechanism guarantees that the optimal power charge control profiles of the storage devices constitute a dynamic market equilibrium with a real-time pricing mechanism in a distributed fashion. Under the linear-quadratic dynamic grid model, the optimal design of the reference adjustment to modify the real-time pricing mechanism is analytically derived. The effectiveness of our proposed mechanism is also illustrated through simulation with real demand data.

Keywords: Game theory, Stochastic/uncertain systems, Robotics applications
Abstract: In this paper, we analyze two-player zero-sum differential games with asymmetric information. In the proposed formulation, asymmetric information is associated with the knowledge about a vector of model parameters that is subject to variations about a nominal value. It is assumed that one of the players knows only the nominal value of the parameter vector while the other player has also information about the parameter variations. Conservative strategies are developed using sensitivity functions for the minimizing player, which is the player at an information disadvantage. The proposed approach is tested for pursuit-evasion games with an uncertain dynamic obstacle, where the pursuer is assumed to know only the nominal value of the obstacle's speed.

Keywords: Cyberphysical systems, Control Technology, Control applications
Abstract: The paper proposes a zeroing control barrier function (ZCBF) for signal temporal logic (STL) tasks that have time constraints. STL uses propositions in which a sufficiency relationship is determined by functions that are evaluated by continuous-time signals. In conventional control methods using CBFs to achieve STL tasks with time constraints, we need to design new time-varying CBFs. The design of new CBFs relies on heuristics and there are many candidates of CBFs. Furthermore, it is difficult to design candidates for CBFs such that inputs always exist that satisfy the CBF constraints until the time that the task is completed. Thus, in reality, when we control whith CBF-like functions, there are cases where we cannot obtain control inputs. We propose a control method to achieve STL tasks in which the CBFs can be easily designed using a few parameters instead of designing new time-varying CBFs. The proposed controller achieves STL tasks with time constraints, like a time limit on which tasks should be achieved. In addition, the introduction of a virtual time reduces the risk of disappearing of control inputs that satisfy the CBF constraint. The effectiveness of the proposed method was confirmed in numerical simulations by applying the method to an adaptive cruise control.

Keywords: Autonomous systems, Game theory, Aerospace applications
Abstract: This paper considers a problem of target protection where an attacker tries to reach a target. A defender protects the target by placing itself between the attacker and the target in order to deny capture of the target by the attacker. The interesting case where the attacker is faster than the defender is considered. In such a case, the defender is unable to intercept the attacker due to a speed disadvantage, but it may be able reach the target before the attacker and reject the attacker. On the other hand, the attacker needs to strategically use its speed advantage in order to circumvent the defender, and its capture zone, in order to successfully reach the target. For the long range scenario, the time required by the Attacker to maneuver around the Defender and head directly at the Target unimpeded is given. Using this time, bounds on initial conditions are given, which determine whether or not the Attacker is able to reach the Target.

Keywords: Sliding mode control, Nonlinear robust control, Nonlinear systems
Abstract: This paper presents an integral sliding mode controller (ISMC) that can handle matched and unmatched uncertainties present in singularly perturbed nonlinear systems. Fast sensor dynamics in series with the plant are explored as study case. By incorporating the knowledge of the given sensor dynamics into the SMC design and appropriate parameters tuning of the proposed controller, the main result shows that the closed loop system can converge to a small neighbourhood of the origin. More precisely, by selecting a sliding surface with a convergence time constant even smaller than the time constant governing the sensor dynamics, the closed loop system is shown to be semi-globally practically input-to-state stable. Simulation results on Maglev model are presented to show the effectiveness of the proposed approach.

Keywords: Software tools, Robust control, Verification and validation
Abstract: We present iqcToolbox, an open-source robustness analysis toolbox based on integral quadratic constraint (IQC) theory. To better enable collaborative contributions, this MATLAB toolbox is modularly designed and avoids dependencies on other closed-source toolboxes, excepting the Control System Toolbox. In contrast to existing robustness analysis packages, iqcToolbox is capable of analyzing discrete-time time-varying systems as well as continuous-time systems. iqcToolbox also allows the specification of time-varying IQC multipliers, and we demonstrate how the incorporation of time-varying systems in the toolbox can generate new uncertainties (such as eventually time-invariant parameters), express new disturbance signal sets (such as constant or periodically updated signals), and enable new analysis tools (such as worst-case reachability analysis). Both the expressivity and modularity of our core analysis objects provide a rich and stable framework for extensions by current and future developers.

Keywords: Linear parameter-varying systems, Linear robust control, Linear systems
Abstract: Models of dynamics systems with resonance modes might include uncertainty on the exact numerical value of the damping and natural frequency. These models are referred to as parametric uncertainty models. Input shaping (IS) design is often computed for a nominal model, rejecting any possible variations in the parameters. Input shaping that is robust for a parametric uncertainty on the resonance mode over a range is required. The process defined in this paper considers the problem of robust input shaping, where a single input signal is designed for a range of uncertainty in the parameters of a dynamic model. Robust input shaping is achieved by considering the extreme cases in the parameter variations, and designing a robust input signal for which constraints on both input and output signals are satisfied. The approach is tested on a second order model with 10% variation in the natural frequency and 10% variation on the damping, which illustrates the ability to design a robust input signal.

Keywords: Automotive applications, Sensor fusion, Robotics applications
Abstract: This paper proposes an improved localization that integrates LiDAR-SLAM, GNSS, and odometry in environments with sparse features such as rural areas with only a few surrounding objects and ample features such as urban street canyons. Localization is important for autonomous driving of automobiles, and SLAM is useful in environments without a map; however, it sometimes collapses in environments with few feature observations. Fusion of GNSS and odometry will complement the lack of observed features, but deciding when to switch between them can be difficult. This research proposes using a probabilistic data association filter to fuse the LiDAR-SLAM, GNSS, and odometry methods. This filter exploits probabilistic weighting for each measurement by calculating the uncertainty of observations. This study evaluated the performance of the method via numerical verifications in challenging environments, including urban and rural areas, modeled on a realistic simulator.

Keywords: Distributed control, Power systems, Predictive control
Abstract: DC microgrids are getting more attention from utilities due to increasing DC sources and loads. DC microgrids have several advantages over AC microgrids, such as lower power loss, higher efficiency, and smaller size. Unlike AC grids, DC microgrids are exposed to small disturbances in their sources. DC sources' output power varies due to the weather uncertainties, and output power deviations in AC sources have constant ripples resulting from AC/DC converters that may cause instability. DC microgrids' stability depends on DC bus voltage deviation, especially with constant power loads (CPLs). In this paper, a reduced-order distributed tube-based model predictive control (TB-MPC) is proposed and designed for a two-area microgrid to ensure the system's stability in the presence of small disturbances in the sources. The effectiveness and performance of the reduced-order distributed TB-MPC is evaluated via simulations in comparison to the centralized TB-MPC approach. The results display a notable improvement in disturbance rejection by employing the reduced-order distributed TB-MPC.

Keywords: Energy Systems
Abstract: Commercial buildings have significant flexibility in how and when they consume energy, and this freedom can be put to good use by grid operators managing the stability of the grid. This talk will discuss demand response for buildings, or the throttling of power consumption in reaction to signals sent by the grid operator. By shifting energy consumption in time, these techniques can be seen as utilizing the thermal inertia of buildings as a form of virtual electrical storage. In the first part of the talk, we will introduce and develop a predictive control approach for a hybrid storage system that combines the best properties of electrical batteries, commercial buildings and generation re-scheduling to better provide balancing services at multiple time scales, and which has the side-effect of minimizing energy consumption by the building. While these techniques have been shown to be effective, the cost of modeling and of commissioning such advanced controllers is one of the key challenges currently preventing their widespread deployment. In the second part of the talk, we will discuss a number of direct data-to-control approaches in order to bring development costs down significantly and will demonstrate the effectiveness of these frameworks on buildings on the EPFL campus.

Keywords: Aerospace applications, Control applications, Mechatronic systems
Abstract: In aircraft braking, actuators must dissipate most of the aircraft mechanical energy, leading to an elevated degradation phenomenon which can impact the operating performances and compromise the safety of the maneuver. The health status of the component is related to its capability to deliver the right amount of output torque for a given required pressure, so that its health condition is directly related to the braking torque and pressure. To design and realize active monitoring systems, having a reliable torque estimation is essential. In this work we propose and compare different solutions for torque estimation in aircraft, and evaluate merits and drawbacks using a realistic set of operating conditions tested on a high-fidelity nonlinear aircraft model.

Keywords: Aerospace applications, Fault-tolerant systems, Adaptive control
Abstract: This work presents the development and testing of a methodology for dynamic control allocation of generic over-actuated aircraft equipped with a set of primary and secondary control effectors. The proposed approach enforces a hierarchy in the allocation strategy that aims at favoring the primary effectors whenever possible, and distributes the control signal over the redundant effectors in the presence of control limitations, faults and loss of effectiveness, without the need to resort to fault detection schemes. The suitability of the proposed solution and its implementability on embedded flight controllers is verified on actual flight tests conducted on a scaled-down model equipped with a set of secondary effectors.

Keywords: Fault-tolerant systems, Aerospace applications, Fault detection/accomodation
Abstract: Reliability and fault tolerance are crucial aspects for the industrialization of Airborne Wind Energy (AWE) systems, yet the scientific literature on this topic is still scarce. A study on fault tolerant control of a hybrid rigid-wing/quad-copter tethered aircraft used in AWE is presented. The fault tolerant control structure features a combination of passive measures. The former combine daisy chain control allocation with Internal Model Control (IMC), enabling the system to immediately counteract saturations or faults of one or more actuators. The active measure employs a quantitative model-based fault detection and isolation (FDI) approach to identify a failure in one or more of the discrete control surfaces (rudder, ailerons, elevator). The faulty actuator is then excluded from the control allocation strategy. The FDI approach is based on a residual indicator of the discrepancy between the actual system behavior and the one predicted by a dynamical model fed by the commanded control signals. Simulations of a realistic model of the tethered aircraft show promising performance of the method.

Keywords: Control applications, Aerospace applications, Nonlinear systems
Abstract: Bistable structures are a type of morphing structure which have the ability to obtain two stable shapes which do not require any energy to be maintained. In this work, a reference governor control scheme is implemented to control the shape change of a bistable structure while satisfying actuator saturation constraints. The structure is modelled as the nonlinear Duffing-Holmes oscillator and the actuation force becomes a polynomial of the states once the system is linearized. By extending an augmented version of the state, the computation of the maximal output admissible invariant set in the case of polynomial actuator constraints is achieved. The reference governor is implemented by solving a nonlinear optimization problem at initialization and then uses a bisection algorithm at subsequent time steps. This methodology is shown to be an effective means by which a bistable structure may be controlled despite the polynomial constraints associated with actuator saturation. The proposed methodology is then compared to the most current method for controlling bistable structure shape change.

Keywords: Aerospace applications, Nonlinear systems, Localization
Abstract: We present a trajectory tracking controller for a quadrotor unmanned aerial vehicle (UAV) configured on SU(2)xR^3, and relate this result to a family of geometric tracking controllers on SO(3)xR^3. The theoretical results are verified in several simulation examples, and the controller is subsequently implemented in practice and integrated with a simultaneous localization and mapping (SLAM) system through an onboard extended Kalman filter (EKF). The proposed control system can be used for inventorying tasks in a supermarket environment without the need for external positioning systems.

Keywords: Actuators, PID control, Aerospace applications
Abstract: Abstract—Actuators are the building block for every automated system. The field of applications is wide open, from home appliances to aerospace. In this joint project between OTM Servo Mechanism and Brunel University London, we design and develop custom-tailored actuators for the aerospace industry. Actuators at aerospace specifications are suitable for many applications, such as aeroplanes and unmanned aerial vehicles. The design requires significant considerations to the hosting working environment and size and weight constraints. This paper discusses the design and simulation of an actuator control unit (ACU) for an electromechanical actuator driven by a 51mm airborne three-phase brushless DC motor. By modelling and simulating the whole system, we can set and tune the control modes of the controller. The ACU comprises the controller and necessary components to complete the automatic control process. We rely on a set of feedback control loops to meet the desired setpoints for the employed actuator despite the applied load and disturbances. Electromechanical actuators are an excellent fit for all-electric systems that require new developments in the field of automatically controlled actuators.

Keywords: Control applications, Machine learning, Health and medicine
Abstract: This paper considers a bilateral tele-rehabilitation using electrical stimulation with bilateral teleoperation. In order to ensure the patient safety, the patient fatigue is estimated using Gaussian process regression. First, a mathematical model is given for the motion of the patient's knee joint. Then, the patient's fatigue is considered as an unknown disturbance input. Next, the disturbance is estimated using Gaussian process regression based on the mathematical model and the observed knee joint angle. Finally, an experiment of repeated flexion and extension of the knee joint is conducted for the tele-rehabilitation. The disturbance increases as the bending exercise continues. After a rest, though the fatigue is recovered once, the disturbance increases again. Therefore, the fatigue can be estimated.

Keywords: Cooperative control, Machine learning, Sensor networks
Abstract: In this paper, we propose a control algorithm to explore an unknown environment while guaranteeing the safety of robots by learning safety constraints from sensor information. A sparse Bayesian classifier (SBC) is trained to estimate the probability that the robots will not collide with obstacles at each point based on the local distance data to obstacles obtained from onboard sensors. Then, we propose a control barrier function (CBF), named an SBCBF, which is used to avoid obstacles estimated by the SBC. We also develop a persistent coverage control based on the SBCBF for exploring the area keeping the robot at a given safety level. Furthermore, we build an online control algorithm that integrates the SBCBF synthesis and safe persistent coverage control. Finally, we demonstrate the effectiveness of the proposed algorithm by the simulation and experiment.

Keywords: Cooperative control, Autonomous systems, Robotics applications
Abstract: We consider the problem of remotely manipulating an unknown payload using multiple agents in the presence of sensing and actuation delays. The overall objective is to find an optimal input force for each agent so that the linear and angular velocities of a rigid object track a reference. We prove that combining primal–dual gradient dynamics with a passivity-based technique called the scattering transformation yields a completely distributed algorithm that converges locally to the optimal solution for the associated optimization program when the delays are homogeneous. We also show that when the delays are heterogeneous, the algorithm is still guaranteed to converge, but to a possibly suboptimal solution. Numerical simulations demonstrate the velocity tracking performance and optimality of the resulting force inputs on practical problems.

Keywords: Cooperative control, Control applications, Vision
Abstract: A key application of multi-drone coverage control is a 3-D map reconstruction from images acquired by aerial drones. In this framework, it is required not only to sample images at every point on the environment but also to sample them from rich viewing angles. A quadratic program (QP)-based solution to this problem, i.e. the so-called angle-aware coverage control, was recently presented by the authors. In this paper, an experimental study of the proposed coverage control is presented. To this end, the control algorithm was implemented on a multi-drone testbed, i.e. the Tokyo Tech Robot Zoo Sky. The experimental validation revealed that the QP-based controller achieved the desirable drone motion while reducing the cost function and also taking situation-adaptive actions. Namely, each drone was able to quickly escape the well-observed regions while they slowed down when the region was not sampled by any other drone in the past. Moreover, the effectiveness of the proposed angle-aware coverage control was compared in terms of quality of the map reconstructed by the images through a structure-from-motion technique with an existing persistent coverage control, which does not take into account angles. The results show that the angle-aware coverage control outperforms the traditional coverage control scheme.

Keywords: Automotive applications, Control applications, Transportation systems
Abstract: The development of automated driving technology is expected to reduce traffic congestion. It is necessary to consider the impact that vehicle control of connected automated vehicles (CAVs) has on the overall traffic flow in situations where automated and manually driven vehicles are mixed. In this paper, we represent the traffic flow as an asymmetric simple exclusion process (ASEP) network exploration of multi-agent systems to evaluate the traffic congestion due to the altruistic lane changing of CAVs. To evaluate the congestion, we propose a new method of representing traffic congestion, the Demand-for graph, which represents the action demands among agents. By expressing the demand for altruistic lane changes, deceleration, and acceleration in a demand-for graph, we can evaluate the scale of traffic congestion due to lane changes and the reduction speed of the congestion.

Keywords: Nonlinear systems, Autonomous systems, Marine/underwater robotics
Abstract: In this paper, Lyapunov theory for uniform practi- cal asymptotic stability (UPAS) is presented and utilized to solve the problem of position control of a planar underwater snake robot (USR). First, a precise definition of UPAS is presented, which imposes that, locally, all solutions converge to the origin up to a steady-state error that can be arbitrarily reduced by a convenient parameter tuning. Additionally, a sufficient condition for UPAS of a time-varying nonlinear system and a theorem for UPAS of cascaded systems are presented. These are then utilized to design controllers that stabilize the position of an USR when approaching from such a direction that the USR moves against the current. Results from numerical simulations are then investigated to validate the theoretical results.

Keywords: Cooperative control, Robotics applications, Mobile Robots
Abstract: The increasing complexity and scale of today's tasks assigned to autonomous robotic systems entails that it is often neither possible nor efficient to accomplish the given task in a reliable and performant manner by utilizing a sophisticated individual robot. Instead, distributed robotics brings about the potential to master complex tasks by means of comparatively simple robots. This anticipation is enhanced by continuous advances in communication, processing units, and sensors. In that regard, this paper considers the model problem of non-prehensile cooperative transportation of objects. Relying on optimization-based methods, a group of omnidirectional mobile robots is enabled to push arbitrary polygonal, possibly non-convex, objects along a desired path in the plane. In contrast to some of the authors' previous work, this paper novelly proposes an approach to manipulate the object based on force-level considerations. This is of particular interest since it provides the opportunity to naturally consider heterogeneous robotic groups or to handle more intricate scenarios such as transporting deformable objects. The proposed scheme's functionality and versatility is demonstrated through simulation scenarios.

Keywords: Marine/underwater robotics, Observers, Sensors
Abstract: This paper describes the application and experimental validation of two different extended state observers for autonomous surface vehicles. Only the vessel position and orientation measurements are assumed to be available, and both estimators are able to recover, not only the velocities but also the so-called lumped generalised disturbances, which put together both environmental disturbances and nonlinear/unmodelled vessel dynamics. The experimental platform and the open-loop experiments performed to assess and validate the performance of both state estimators are described. The experimental results confirm the prospects given by their previously published simulation results, though some room for improvement is detected in the estimation of the state dynamics.

Keywords: Marine/underwater robotics, Nonlinear systems, Autonomous systems
Abstract: Enabling vehicle-manipulator systems to perform complex and precise intervention operations requires a robust control framework capable of handling redundancy and interaction forces. In addition, it is desirable that the method allows completion of several tasks with a strict prioritization to keep safety critical tasks unaffected by other goals. In this paper, a control method is developed for a broad class of vehicle-manipulator systems, with the main use case being floating base underwater vehicle manipulator systems subject to hydrodynamic forces. In underwater applications, large model uncertainties will be present due to hydrodynamic and hydrostatic effects, unknown disturbances and modelling errors. This means that a viable solution must be robust to disturbances and modelling errors, while also satisfying strict task priorities and compliant contact behaviour. To achieve this, a robust impedance based task-priority control method is presented, and its stability and robustness properties are proven. The method is validated in a simulation of an articulated intervention autonomous underwater vehicle (AIAUV).

Keywords: Control applications, Mechatronic systems, Robotics applications
Abstract: When it comes to robot joints, pneumatic actuators are an interesting alternative to classic electric actuators. They are inherently compliant due to the compressibility of air, which is an advantageous safety feature for human-machine collaboration. In this work, we consider a recently developed pneumatic swivel drive, to be employed as a robot rotary joint. This joint exhibits strong nonlinearities, which makes it challenging to design a trajectory tracking controller. We formulate the nonlinear multiple input multiple output (MIMO) model of the joint’s kinetics and pressure dynamics. Based thereon, two model-based nonlinear control concepts are presented. The first one is a cascaded controller consisting of an outer loop with a joint angle controller and disturbance observer, and an inner pressure control loop. For the second concept, differential flatness of the system is shown and a flatness-based controller is derived, which combines both kinetics and pressure dynamics in one central controller. Experimental results demonstrate the trajectory tracking performance of both controllers. Based on these results, the differences between the two control strategies are analyzed. This provides the foundation for the control of more complex pneumatically-driven robot manipulators.

Keywords: Predictive control, Adaptive control, Distributed control
Abstract: Vehicle platooning control is a branch of Cooperative-Intelligent Transport Systems that aims at reducing the risk of rear-end collisions and improving traffic fluidity. Platooning technologies can benefit of advanced and distributed control strategies, such as the Model Predictive Control, suitable to deal with constraints deriving from inter-vehicle distances and communications as well as to provide adaptive behaviour against variations of speed limits, safety distance, environmental and meteorological parameters provided by the Smart Road. Since vehicle platooning is based on inter-vehicle communications, the control strategy needs to be robust with respect to communication delays. This paper performs a real-time and low-cost Hardware-In-the-Loop testbed based on Raspberry Pi boards that emulate a vehicle platoon managed by a Distributed Model Predictive Control strategy. Results showed a good robustness of the proposed control strategy with respect to communication delays, considering a variable transmission delay in the range [100, 400] ms. The obtained results pave the way to real-road tests, considering connected vehicles equipped with IoT Gateways based on Raspberry Pi hardware.

Keywords: Control applications, Adaptive control, Intelligent systems
Abstract: Cloud and High-Performance Computing (HPC) systems are increasingly facing issues of dynamic variability, in particular w.r.t. performance and power consumption. They are becoming less predictable, and therefore demand more runtime management by feedback loops. In this work, we describe results addressing autonomic administration in HPC systems through a control theoretical approach. We more specifically consider the need for controllers that can adapt to variations along time in the behavior of controlled systems, but also to being reused on different systems and processors. We therefore explore the application of Model-Free Control (MFC) in the context of resource harvesting in a Computing Grid, by regulating the injection of flexible jobs while limiting perturbation of the prioritary applications.

Keywords: Sensor networks, Linear systems
Abstract: Tight clock synchronisation is vital for time-critical distributed systems, as can be wireless controls in modern industrial settings. There are many approaches to the problem, but the disturbances to reject come invariantly from two sources: the employed communication channel, and the physics of the clocks themselves. We here focus on the latter, and specifically on counteracting the effects of temperature on crystal oscillators---a relevant issue in harsh-environment applications. We present an extension to the FLOPSYNC-2 clock synchronisation scheme, complementing its communication-based feedback mechanism with a feedforward compensation capable of reducing the maximum synchronisation error during temperature transients by 87% exploiting a temperature to frequency model. Besides helping in heavy-duty applications, the proposed extension allows to reduce the use of the radio channel, thus being useful also when the environment is not an issue but communication is expensive in any sense, most typically as for battery duration.

Keywords: Modeling, Simulation, Nonlinear systems
Abstract: In the last decade, high-performance multi-core processors have become pervasive. Processors with high cores count, multi-die SoC configuration with multiple heterogeneous units, and accelerators are commonplace today, while tailored and specific operating points are required for efficient execution of applications. In this scenario, an advanced and configurable Power Controller System (PCS) is essential to meet power and thermal constraints, as it is not effective to rely on simple reactive control policies with static conservative margins on the operating points. This paper provides a mathematical model of an HPC processor and the different control approaches implemented in state-of-the-art PCS solutions. In particular, we are comparing the performance of a custom cascade model-based control algorithm that favors cores executing more demanding workloads with the IBM Power9 control algorithm used as a reference design. The results show an average increase in the number of retired instructions of 3.46%, with a peak of 6.45% increase for the cores executing more demanding instructions.

Keywords: Control applications, Adaptive control, Nonlinear systems
Abstract: Soberness—in terms of electrical power—of data centers and high-performance computing systems is becoming an important design issue, as the global energy consumption of information technologies is rising at considerable levels. This issue is all the more complex as these systems are increasingly heterogeneous and variable in their behavior, for example, w.r.t. performance and power consumption, and less predictable, thus demanding runtime management and feedback control. This paper addresses the problem of the control of the power allocated to processors and hence their energy consumption and performance. The use of feedback control allows the energy consumption to be reduced by decreasing the speed without losing performance, by exploiting periods where read/write operations slow the progress. Previous works present limitations regarding both modeling (nonlinear models with numerous parameters) and control performance (mainly instability caused by platform variations). We develop a novel adaptive control that is robust to the variety of execution platforms while maintaining the existing global goals of energy management. We evaluate—on a real system using the Grid’5000 testbed—the robustness of the control to changes in initial parameters and to disturbances, and we compare it with the previous PI control. Our adaptive control approach allows for up to 25% energy savings.

Keywords: Fault detection/accomodation, Energy Storage, Estimation
Abstract: Advanced Battery Management Systems (BMSs) rely on mathematical models to enhance battery safety and improve reliability. Operating conditions and temperature have an impact on the performance, longevity and safety of Li-ion batteries. In particular, thermal breakdowns are caused by faults of different physical and chemical nature. In this paper, we use a set-based fault detection approach with unknown but bounded uncertainties to address the challenge of detecting thermal faults of Li-ion series-connected cells. The proposed approach relies on Constrained Zonotopes (CZ), a set representation capable to preserve the computational benefits of zonotopes while describing asymmetric convex polytopes. An equivalent circuit model (ECM) together with a lumped thermal model are used for state estimation taking into account bounded parametric uncertainties and measurement noise. Numerical simulations demonstrate the effectiveness of the proposed strategy in detecting thermal faults at an early stage and indicate the benefits. The results corroborate the advantages of constrained zonotopes over zonotopes.

Keywords: Modeling, Energy Systems, Nonlinear systems
Abstract: Due to population growth, especially in urban areas, demand for residential and commercial space will further drive the construction of high-rise buildings. To counteract negative side effects of the emerging urban heat island (UHI), the facade surfaces can be equipped with adaptive cooling components. Large-scale application of these facades primarily affects the urban microclimate, but as a side effect also the interior of buildings by influencing the heat exchange with the environment. Working towards an optimal operation strategy for outdoor and indoor conditioning, this work develops a model of an adaptive facade with facade-integrated rainwater harvesting and evaporative cooling. Based on mass and enthalpy balances, the dynamic model equations are derived analytically and further simplified to quasi-stationarity. A parameter study will be conducted for the rainwater absorption and evaporation surface as well as the permeability of the facade.

Keywords: Modeling, Hybrid systems, Energy Systems
Abstract: The building sector is responsible for a large share of the world's resource and energy consumption. A lot of energy is spend to climatize buildings, thus leading to high associated CO2 emissions. As a climate friendly solution, a solar driven facade-integrated system based on adsorption can be used to sustainably cool the interior. A hybrid model based on mass and energy balances is introduced, which describes the working phases of the adsorption facade system and the dynamics in each phase. The system runs in a daily cycle that starts with a regeneration phase in which solar radiation is used to charge the system for the cooling phase. Numerical simulations are conducted to analyze the behavior of the controlled system and give an outlook on control requirements for design and construction of the adsorption facade system.

Keywords: Control applications, Energy Systems, Optimization
Abstract: The boost to the development of micro-scale Organic Rankine Cycles (ORCs) as effective waste heat recovery systems poses new challenges in their design and management. This paper presents the Real-time Optimisation (RTO) of a micro-scale ORC unit to maximise the overall electrical efficiency under different operating conditions. The implicit RTO performs in a model-free fashion using the Extremum Seeking Control (ESC) approach, which exploits only system output measurements, to directly adapt the process input. In particular, two control schemes, i.e. the Gradient-based ESC and the Newton-like ESC, are designed and assessed through simulations thanks to the availability of a Matlab-based ORC model.

Keywords: Power systems, Smart grid
Abstract: Aggregations of residential air conditioners can be controlled to provide important services to the grid, such as frequency regulation. To facilitate such applications, it is essential that models used to represent air conditioners capture their key dynamics. Using house temperature measurements, we demonstrate that there is a delay between the time instant at which an air conditioner switches and the time when the regulated indoor temperature starts moving in the opposite direction, a phenomenon that is not captured by existing models commonly used by power system researchers. We show that this delayed temperature evolution is dependent upon the outdoor temperature and model this relation using a quadratic curve fit to the available data using a robust linear least-squares method in order to address the presence of heteroscedasticity. We extend a commonly used house model that captures both air and mass temperature dynamics in order to account for the aforementioned temperature delays. We assess the impacts that these temperature delays can have on the performance of an existing model-based controller. We develop a new model of aggregate air conditioner dynamics that captures the delays, and modify the controller to incorporate this model. We show that our new controller is able to achieve better frequency regulation performance without compromising user comfort.

Keywords: Power systems, Energy Storage, Energy Systems
Abstract: The paper extends the packetized energy management (PEM) control strategy to enable coordination of compressor-based thermostatically controlled loads (TCLs), such as air conditioners. This establishes a new method of harnessing the flexibility of this ubiquitous resource, enabling a variety of grid services, such as frequency regulation. In the original PEM scheme, resources request energy packets and turn on if their request is approved. That PEM scheme has been further extended by introducing the concept of turn-off requests. We find that this increases flexibility and improves tracking performance. Through a case study involving over 1000 air conditioners, we evaluate the performance of a population of TCLs providing frequency regulation under PEM, highlighting both the capabilities and limitations. Simulations indicate our controller extensions significantly increase resource availability and tracking performance. We show that it is possible to achieve RMS tracking error below 2% when providing more than 250kW of frequency regulation.

Keywords: Soft Robotics, Control applications, Robotics applications
Abstract: The focus of this paper is on an approach to use a reference tracking feedback control for sensing a touch between a compliant soft robotic finger and an object in its environment. The approach uses the reference tracking error as an indicator of contact with the object and removes the necessity for a distal force sensor. The study is performed in virtual reality on a device that mimics essential properties of a previously fabricated soft finger. To achieve the reference tracking control, we identify the finger model and use it for the control design. We demonstrate the work of the controller and the approach in experiments with a robot-mounted finger in which the finger interacts with objects in its environment. In these experiments, we are able to detect objects, obtain data about their geometry and control the finger to get in a complaint touch with an object.

Keywords: Predictive control, Mobile Robots, Planning
Abstract: The ultimate goal for autonomous robots is quick and safe navigation in a variety of environments and under realistic conditions. Consider a robot tasked to quickly and safely navigate a cluttered environment such as a heavy forested area. One common feature of outdoor motion planning is the presence of a base station that can communicate with, plan and control the motion of this robot. This base station can offer several advantages over local motion planning, such as information about the environment that is not locally available to the robot and superior computational resources. With such considerations, we propose a novel networked receding horizon planning method to navigate cluttered environments. Our proposed approach has the following capabilities: (i) it generates a sequence of waypoints optimized over a future time horizon considering knowledge about the lower level controller and the dynamics of the robot and (ii) it detects and adapts to communication delays to maintain safety. The proposed scheme is validated with simulations of a UAV flying in a cluttered forested environment under different communication losses with a base station.

Keywords: Cooperative control, Mobile Robots, Navigation
Abstract: We introduce here a distance-based formation maneuvering control scheme for a group of nonholonomic mobile robots operating in 2D environments with unknown static obstacles. We merge the distance-based formation controller with the Vector Field Histogram method such that the robots temporarily separate into subsets of the full formation for obstacle avoidance purposes, thus facilitating the subsequent reassembly. The overall control system is decentralized since it only requires onboard sensors for inter-robot relative positioning and obstacle detection. We illustrate the proposed control via simulations for two typical obstacle avoidance scenarios.

Keywords: Planning, Optimization, Cyberphysical systems
Abstract: We develop a distributed motion planner for multi-robot systems with Signal Temporal Logic (STL) objectives. Existing approaches to STL-based motion planning are limited to fragments of STL that might not capture all desired behaviors or safety requirements. Focusing on the case of a fleet of multi-rotor Unmanned Aerial Vehicles (UAVs) jointly tasked with satisfying a given STL mission, we develop a distributed method, Fly-by-Distributed-Logic (FBDL), for trajectory planning. The proposed method generates trajectories that maximize the smooth robustness of STL specifications, where the associated optimization is solved in a distributed manner. This, to the best of our knowledge, is the first distributed method that can handle the full grammar of STL (bounded time). Simulation studies show the applicability of our approach to a variety of STL missions, including those with nested STL operators and the Until operator. We also compare its performance to a centralized approach and show that our method is computationally faster, but generally computes trajectories with lower robustness.

Keywords: Autonomous systems, Control applications, Stochastic/uncertain systems
Abstract: Target tracking in urban environments using a fixed-wing unmanned aerial vehicle (UAV) is challenging due to the line of sight obstructions which are caused by buildings. Even with a cooperative target that sends out its location to the UAV, the vehicle may inevitably lose the line of sight due to its limited turning rate. Parts of the UAV operating space in which the UAV loses the line of sight are denoted in this paper as shadows. The shadows have complex shapes and move as the target changes its relative position to buildings. Avoiding the shadows increases the observation time while tracking the cooperative target. We present here a scalable feedback control approach for target tracking with shadow avoidance, which is based on a stochastic optimal feedback control solution. Our results are illustrated by numerical simulations.

Keywords: Automotive applications, Planning, Autonomous systems
Abstract: Autonomous road sweepers are an enabling technology in addressing cleaning tasks in public open spaces. In this paper, we investigate the sweeping of an obstacle-cluttered environment (e.g., a parking lot) using autonomous road sweepers. The challenge of this problem lies in the fact that the sweeper usually has a large turning radius and a small coverage radius. With a small coverage radius, the sweeper must operate near obstacles to maximize its coverage, making the coverage path sensitive to the mapping uncertainty. Due to a large turning radius, enough buffer should be left around obstacles, and the planned coverage paths should be dynamically feasible. To this end, we propose a novel coverage path planning (CPP) algorithm that explicitly accounts for the mapping uncertainty and vehicle dynamics, which includes three steps. First, the map is post-processed with morphological operations and convexification to reduce uncertainty in the map. Based on the post-processed map, the boustrophedon cellular decomposition is then modified to generate path segments that cover the sweeping area considering vehicle dynamics. Finally, a generalized traveling salesman problem is formulated and solved to connect the path segments for a CPP path with the minimum length. Through experiments in a parking lot at Isuzu Technical Center of America using an Isuzu VL22 truck, the effectiveness of the proposed approach is verified.

Keywords: Modeling, Reduced order modeling, Identification
Abstract: In recent years, digital twin-based techniques have been applied in many different engineering fields, including simulation, model design, performance analysis, system prognosis, and so on. However, for complex systems, the associated physical equations and dynamics are sometimes difficult to be constructed using domain knowledge-based derivations. On the contrary, data-driven-based modeling can skip this challenge providing the associated input/output data are available. As a result, this paper presents a modified data-driven method based on the AutoRegressive with eXogenous input (ARX). The Observer/Kalman filter identification (OKID) with eigensystem realization algorithm (ERA) and fast orthogonal search (FOS) are used for system identification in order to create digital twins that can consider the model stability, which most of the data-driven methods have lack of. The condition of the system is determined based on the difference between the real system and digital twins. Finally, a simulation using the MATLAB/SIMULINK Turbofan Engine is taken as a tested black box. Simulation results prove that the proposed method can identify faulty condition.

Keywords: Control education, Automotive applications, Cooperative control
Abstract: Autonomous and collaborative vehicles not only are seen as a possible solution to reducing congestion and traffic related fatalities. They also provide an excellent multi-domain test bench for engineering education at undergraduate and graduate level. Yet, the use of real scale platforms for experimental edu- cational activities bears prohibitive costs and complexity. While several small scale autonomous platforms have been developed in recent years to address this issue, still they require a significant investment of time and money, which is not always ideal for undergraduate education. Furthermore, none of the available platforms are specifically developed for platooning experiments. In this paper, we detail the results of an undergraduate student’s project where a pair of relatively low-cost, off-the-shelf small scale RC cars have been used to implement and test a well known platooning algorithm from the literature. Furthermore, a Virtual Potential Field (VPF) based lateral controller has been included in order to allow the cars to navigate a prescribed closed-circuit track. Self-location of each car has been obtained via a ceiling- mounted motion capture system. Results have shown that, even using a relatively low sampling rate of 10 Hz, accuracies in the order of 1 cm can be obtained when platooning at 0.5 m/s along a circuit of 4 by 3 m. As further improvements to the platform, apart from higher sampling rates for the control law, the inclusion of onboard perception is being explored, in order to eliminate the need for an external motion capture system.

Keywords: PID control, Nonlinear robust control, Optimization
Abstract: PID controllers represent the most frequently used feedback structure, especially in industrial applications. With an appropriate design of the individual controller parameters, they guarantee both stability and a good transient behavior, and they offer a clearly interpretable structure. Problems may arise in safety-critical applications, especially if the real system is subject to model uncertainty. In this paper, a model-based PID controller is considered for the power control of a solid oxide fuel cell, where safety aspects are highly relevant. Two alternative design models, a linear stack model and a nonlinear Hammerstein stack model, are used for the control design. For both models, a PID controller parametrization is determined that meets robust performance specifications using a multiplicative output uncertainty description. Simulation results point out the benefits of the nonlinear structure based on the Hammerstein model.

Keywords: Aerospace applications, Observers, Robust control
Abstract: This paper presents a sliding mode observer (SMO)-based robust control method, which achieves simultaneous estimation, fluid flow velocity regulation and limit cycle oscillation (LCO) suppression in a flexible airfoil. The proposed control design is based on a dynamic model that incorporates the fluid structure interactions (FSI) in the airfoil. The FSI describe how the flow field velocity at the surface of a flexible structure gives rise to fluid forces acting on the structure. In the proposed control method, the LCO are controlled via control of the flow field velocity near the surface of the airfoil using surface-embedded synthetic jet actuators. The flow field velocity is expressed using a proper orthogonal decomposition based reduced-order flow model that formally incorporates the actuation effects of synthetic jet actuator. Specifically, this flow field velocity profile is driven to a desired time-varying profile, which results in a LCO-stabilizing fluid forcing function acting on the airfoil. A sliding mode observer is designed to estimate the unmeasurable states in the reduced-order model of the actuated flow field dynamics. The SMO is rigorously proven to achieve local finite-time estimation of the unmeasurable state in the presence of the parametric uncertainty in the SJA. A Lyapunov-based stability analysis is used to prove that the active flow control system asymptotically converges to the LCO-stabilizing forcing function that suppresses the LCO. Numerical simulation results demonstrate that the augmented observer provides a reduction in the control effort required to stabilize the LCO.

Keywords: Data analytics, Control applications, Discrete event systems
Abstract: We consider the problem of managing price discounts for seasonal goods in retail trade networks. Our goal is to sell all the seasonal goods planned to sell by the end of the season, and get the most sales revenue. The original algorithm of discounts formation is proposed. The algorithm is based on tracing of actual sales and their subsequent comparison with planned sales. We give examples of practical use of the proposed algorithm in a large retail trade network consisting from several hundreds stores.

Keywords: Autonomous systems, Automotive applications, Control architectures
Abstract: Software reusability is critical to the rapid development and certification of autonomous vehicles (AVs). However, little attention has been given to designing AV path planners for increased software reusability. In this paper we design a trajectory-optimizing nonlinear MPC planner that takes a driveable corridor, desired speed profile and constraints as input to compute the AV's trajectory. These inputs are hardware-independent, confining hardware dependencies to the planner software itself and clearly separating higher-level route planning from hardware-dependent algorithms. We implement the planner in simulation to demonstrate its feasibility in representing common traffic scenarios.

Keywords: Predictive control, Nonlinear systems, Modeling
Abstract: We propose a new method of Linear Model Predictive Control (LMPC) that can achieve the comparable control performance to that of Nonlinear Model Predictive Control (NMPC) using a linear model obtained by low-dimensional lifting linearization. In the lifting linearization, the time evolution of the nonlinear output functions of the system, called "observables", can be approximated as linear dynamics. In general, however, unless the linear model is lifted with a sufficiently large number of observables, the error increases, especially in long-time behavior. Therefore, when a low-dimensional lifted model is used for LMPC, the control performance is degraded. On the other hand, using too high-dimensional linear models is not preferable for the computational cost. In this study, using a known nonlinear model of the target system, we first derive a low-dimensional linear model by approximate lifting linearization, where the nonlinear terms in the model are utilized as observables, and the state-dependent matrices are treated as time-variant matrices with prior state information. We then propose a new algorithm for LMPC with high performance and low computational cost, in which the lifted model is used for the prediction where the observable-based constraints to compensate the prediction error and the additional corrective inputs are introduced. Numerical simulations are conducted to evaluate the effectiveness of the proposed method.

Keywords: Optimization, Mechanical systems, Control Technology
Abstract: This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removing a thin layer of material and imaging the exposed surface. The images are then used to construct a 3-dimensional image of a material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed (R2R0 controller will automate this process while improve the precision of the material removal. The proposed (R2R) controller solves an optimization problem designed to minimize the variance of the material removal subject achieving the expected target removal. This optimization problem was embedded in an (R2R) framework to provide iterative feedback for disturbance rejection and convergence to the desired removal rate. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our (R2R) controller from historical data. The proposed (R2R) controller is demonstrated through simulations. Future work will empirically demonstrate the proposed (R2R) through experiments with a real MSS system.

Keywords: Optimization, Predictive control, Nonlinear systems
Abstract: Model predictive control (MPC) has widely been used in both research and application in recent years. Methods from the field of distributed MPC (DMPC) are especially designed to handle large systems or applications with demands in terms of flexibility and modularity. Two distinctive algorithms for DMPC, namely Sensitivity-based DMPC (SENSI) and the Alternating Directions Method of Multipliers (ADMM) are compared in this paper concerning their applicability to building automation. First, the algorithms are presented and applied to two academic examples in order to analyze the convergence behavior and scalability of the algorithms. It is shown that ADMM and SENSI algorithm maintain constant computation time per agent with increasing number of agents in the network. Afterwards, the algorithms are applied to a building automation system in a setpoint control and an energy optimal control scenario. Both algorithms are capable of solving the corresponding optimal control problems (OCP) which demonstrates the applicability of the algorithms to building automation. Furthermore, the differences of the algorithms in scope of this application are analyzed showing a better performance of the ADMM algorithm in case of setpoint control. However, the sensitivity-based approach needs less communication steps than the ADMM in an energy optimal control scenario.

Keywords: Linear systems, Predictive control
Abstract: This paper proposes an extension of the reference governor, which we refer to as the High-Performance Reference Governor (HP-RG), for constraint management of linear systems. This extension is motivated by the fact that reference governors typically respond slower than model-predictive controllers, for example in response to a step change in the tracking setpoint. The newly-developed scheme introduces two modes: the ``standard mode", and the ``fast mode". In the standard mode, the scheme behaves the same as a standard reference governor. In the fast mode, however, the computed control command is less conservative than what a standard reference governor would compute. This control command is computed such that a constraint-admissible transition back to the standard mode is possible in the next timestep. This guarantees recursive feasibility and constraint satisfaction. To guarantee stability and prevent unnecessary mode switches, a stability-inducing mechanism is introduced, which allows transition to fast mode only when large variations in the setpoint are detected. To further reduce the computational footprint of HP-RG, an extension is presented that can be implemented using an explicit algorithm. Finally, the effectiveness of the developed algorithms is verified using numerical examples.

Keywords: Predictive control, Actuators, LMIs
Abstract: In this paper, we develop a model predictive control (MPC) strategy for discrete-time bilinear systems by interpreting the bilinear model as a linear system with uncertainty in the state matrix. Using the fact that this uncertainty is restricted to a polytopic set, we apply the existing methodologies on robust MPC for linear systems to stabilize the bilinear system. Due to the interrelated nature of the polytopic uncertainty and the ability to control the system, which are both tied to the bounds on the input, a phenomenon emerges that the region of attraction under the MPC policy can be expanded by lowering the actuator bounds. We present an approach and discussion around maximizing this region of attraction by balancing the uncertainty and input strength. Numerical examples demonstrate the efficacy of the presented technique for both cases of single-input and multi-input bilinear systems.

Keywords: Cooperative control, Renewable Energy, Optimization
Abstract: In this paper, we incorporate wake steering models into a multiobjective wind farm model for improving power extraction, specifically under the framework of handling maintenance of individual turbines. Wind turbine arrays can be viewed as coupled networks, where wake effects limit the available power extraction of turbines downstream. We expand upon previous work on a heuristic method to find axial induction factors for the far-field wake problem and incorporate the algorithm to a model predictive control framework. Simulation results are discussed, demonstrating improved power output over algorithms that do not update acknowledging that turbines are under maintenance.