Keywords: Transportation systems, PID control, Optimization
Abstract: An important function of an Automatic Train Operation (ATO) system is to control braking to bring a train to a standstill at a specific location in a smooth and safe manner. To minimise journey times and to improve throughput on a rail network, the time required for braking needs to be minimised. This paper considers braking as an optimal, minimum time control problem and applies the solution as a feedforward braking traction. A feedback controller is then used to ensure that the train speed follows the optimal solution. The feedback is based on a PID controller to align with the existing structure of the ATO system. By considering the controller as a Lur'e system, the Popov criterion provides a limit on the allowable slope of the braking curve and the optimal profile is modified to ensure that this constraint is satisfied. The performance of the controller is evaluated on an industry supplied simulation of the train dynamics.

Keywords: Actuators, Aerospace applications, Simulation
Abstract: This article tackles the problem of designing rate-limiting elements, commonly found in feedback loops and used to filter signals before passing them to actuators. Under certain circumstances, these elements can introduce a phase delay, which can ultimately cause instability. For this reason, techniques to understand, analyse, and predict instability due to their phase loss have been proposed. Techniques to limit the rate of change of a signal without introducing the significant phase loss of a standard rate limiter have also been studied. However, said schemes have one or multiple of the following problems: the introduction of bias, the necessity to know the internal signals of actuators, difficult parameter tuning/interpretation, and the need to solve online optimisation problems. This study provides an alternative rate-limiting element that is competitive with state-of-the-art methods in phase-matching performance but avoids the mentioned drawbacks. The newly introduced phase anticipation scheme has been tested in step, sinusoidal, and mixed regimes. It proved able to recover up to 65% of the lost phase without introducing any significant downside. To illustrate its performance in a concrete application, we show its effectiveness in avoiding phase loss-induced limit cycles in an aircraft ground handling task in a high-fidelity simulator.

Keywords: Vision, Aerospace applications, Navigation
Abstract: In the aviation industry, autonomous vehicles are gaining more and more importance over time, asking for a reduction of accidents by lowering the pilots' workload in the most stressful maneuvers and introducing automated systems. While autonomous flight is widely studied, being de-facto a standard in many aircraft, autonomous ground navigation is still in its early stages. With increasing air traffic, managing on-ground operations has become very challenging, especially in low-visibility conditions. Exploiting the Global Positioning System (GPS) is a possible solution. Unfortunately, this approach is prone to inaccuracies, disturbances, and jamming. For this reason, a more robust solution includes using other exteroceptive sensors, such as cameras or radars. In this paper, we propose a 2-layer control system architecture capable of performing autonomous taxiing maneuvers using a monocular camera. We propose a model-oriented approach to control the ground handling dynamics, which are used to actively exploit the information from the camera. The results, obtained using a validated multibody simulator interfaced with a graphical engine, show good tracking performance and robustness to external light conditions.

Keywords: Control applications, Marine/underwater robotics, Soft Robotics
Abstract: This paper presents a flexible buoyancy engine designed for small autonomous underwater vehicles (AUVs), addressing limitations in existing buoyancy control technologies. The proposed system utilizes a constant-mass-variable-volume mechanism that inflates a bladder with an incompressible fluid, enabling precise depth control. A cascade feedback control loop is introduced, where the outer-loop uses a linear quadratic regulator (LQR) for depth stabilization and the inner-loop uses feedback linearization for volume regulation. The findings demonstrate local stability of the closed-loop bladder dynamics and highlight practical limitations, such as max depth and speed. This research advances buoyancy engine design by integrating soft robotics principles with conventional actuation, paving the way for compact AUVs.

Keywords: Robotics applications, Real-time systems, Aerospace applications
Abstract: The desire to establish in-space logistics systems has led to the development of new methods for satellite life extension. These methods include on-orbit refueling, propulsion system upgrades, and module swapping or coupling using highly mobile servicing satellites. This paper outlines a methodology to execute a sequence of actions for a smart satellite equipped with a robotic manipulator to autonomously assemble to a passive module, creating a more capable satellite. This paper showcases a demonstration of this methodology at an Air Force Research Laboratory (AFRL) ground-based test facility, the Robotic Orbital Control (ROC) lab. The testbed at this facility includes two hexagonal float craft suspended by air bearings on top of a granite table, with the ability to move without friction. These modular robotic craft are equipped with docking adapters and a robotic arm. A layered and modular software architecture handles autonomy and control, with communication within the system occurring via Robot Operating System 2 (ROS2). Ground truth data collected using a motion capture system (MCS) verifies successful completion of the planned trajectories and assembly events.

Keywords: Autonomous systems, Modeling, Power Electronics
Abstract: A weighted criterion based secondary control layer strategy for balancing single-phase residential microgrids is presented in this paper. This strategy is built on top of previously developed intra- and inter-phase power management strategies. The proposed control scheme involves logic based decision making along with single-phase back-to-back converters that interconnect each of the three phases at the distribution side of the grid. This strategy is effective where each phase has its own generation via renewable energy resources with storage units. Two cases are simulated with high fidelity simulations performed in PSCAD/EMTDC. Experimental verification is also carried to validate the simulation results.

Keywords: Cybersecurity, Cyberphysical systems, Energy Storage
Abstract: A cloud-based battery management system (BMS) requires accurate terminal voltage measurement data to ensure optimal and safe charging of Lithium-ion batteries. Unfortunately, an adversary can corrupt the battery terminal voltage data as it passes from the local-BMS to the cloud-BMS through the communication network, with the objective of under- or over-charging the battery. To ensure accurate terminal voltage data under such malicious sensor attacks, this paper investigates a Koopman-based secure terminal voltage estimation scheme using a two-stage error-compensated self-learning feedback. During the first stage of error correction, the potential Koopman prediction error is estimated to compensate for the error accumulation due to the linear approximation of the Koopman operator. The second stage of error compensation aims to recover the error amassing from the higher-order dynamics of the Lithium-ion batteries missed by the self-learning strategy. Specifically, we have proposed two different methods for this second stage error compensation. First, an interpretable empirical correction strategy has been obtained using the open circuit voltage to state-of-charge mapping for the battery. Second, a Gaussian process regression-based data-driven method has been explored. Finally, we demonstrate the efficacy of the proposed secure estimator using both empirical and data-driven corrections.

Keywords: Control applications, Cyberphysical systems, Cybersecurity
Abstract: Nowadays, many control systems are networked and embed communication and computation capabilities. Such control architectures are prone to cyber attacks on the cyberinfrastructure. Consequently, there is an impellent need to develop solutions to preserve the plant’s safety against potential attacks. To ensure safety, this paper introduces a modular safety filter approach that is effective for various cyber-attack types. This solution can be implemented in combination with existing control and detection algorithms, effectively separating safety from performance. The safety filter does not require information on the received command’s reliability or the anomaly detector’s feature. It can be implemented in conjunction with high-performance, resilient controllers to achieve both high performance during normal operation and safety during an attack. As an illustrative example, we have shown the effectiveness of the proposed design considering a multi-agent formation task involving 20 mobile robots. The simulation results testify that the safety filter operates effectively during undetectable, intelligent attacks.

Keywords: Autonomous systems, Cyberphysical systems, Smart grid
Abstract: Red teaming is an adversarial approach used to assess a cyber-physical system's (CPS) defenses in order to identify vulnerability. As the CPS grows in complexity, manually conducting red-teaming grows harder due to the larger attack surface and the need for domain expertise. In this paper, we develop an automated red-teaming agent that can couple with simulation test-beds which can be used to assess system-wide impacts of cyber-attacks. The proposed agent is adaptive and is able to take the system-state into account to determine the optimal attack action at any given point of time. The efficacy red-teaming agent is demonstrated on a microgrid use-case subjected to Man-In-The-Middle attacks.

Keywords: Power Electronics, Predictive control, Nonlinear systems
Abstract: Low-voltage four-wire grids have undergone significant changes in recent decades. New power electronics-based resources have been integrated into the system, enhancing its flexibility but also impacting its stability and safety. As a result, modern control strategies must consider safety aspects and directly contribute to converter protection while supporting the network during faults. In this paper, we build upon the nonlinear model predictive control formulation proposed in [1] for three-wire systems and tackle the control design challenges and performance requirements that are present in four-wire grid-connected power electronics converters. This entails not only extending the modeling to describe the dynamics of the system's common-mode components, but also capturing the coupling of the different variable components through the control problem's constraints. The new extended formulation is tested through simulations of both symmetrical and asymmetrical faults, showing effective current and voltage limitation while performing as demanded by grid codes.

Keywords: Predictive control, Neural networks, Automotive applications
Abstract: The multi-timestep command governor (MCG) is an add-on algorithm that enforces constraints by modifying, at each timestep, the reference command to a pre-stabilized control system. The MCG can be interpreted as a Model-Predictive Control scheme operating on the reference command. The implementation of MCG on nonlinear systems carries a heavy computational burden as it requires solving a nonlinear program with multiple decision variables at each timestep. This paper proposes a less computationally demanding alternative, based on approximating the MCG control law using a neural network (NN) trained on offline data. However, since the NN output may not always be constraint-admissible due to training errors, its output is adjusted using a sensitivity-based method. We thus refer to the resulting control strategy as the neural network-based MCG (NN-MCG). As validation, the proposed controller is applied as a load governor for constraint management in an automotive fuel cell system. It is shown that the proposed strategy is significantly more computationally efficient than the traditional MCG, while achieving nearly identical performance if the NN is well-trained.

Keywords: Manufacturing systems, Reinforcement learning, Control applications
Abstract: Directed Energy Deposition (DED) is a widely used additive manufacturing for fabricating metal components with complex geometry and mechanical properties. A critical challenge in DED is the regulation of the melt pool’s solidification cooling rate (SCR), which directly influences the microstructure and mechanical properties of the fabricated part. This paper proposes reinforcement learning (RL)-based feedforward control for the SCR regulation in DED. An RL control is developed to optimize process parameters, namely, laser power, traverse speed, and inter-layer dwell time, on a layer-by-layer basis. The proposed method leverages offline finite difference simulations to iteratively learn an optimal control policy that minimizes SCR deviations from a reference value while reducing overall production time. The effectiveness of the RL controller is evaluated for different references, demonstrating superior regulation performance and production time compared to control with constant process parameters.

Keywords: Reinforcement learning, Energy Systems, Control applications
Abstract: Efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial for optimizing energy use and maintaining indoor comfort in buildings. Traditional control methods, such as PID control, cannot handle energy use trade-offs among multiple components in the building energy system at a supervisory level. Reinforcement learning (RL) presents a promising solution, offering adaptive and data-driven control strategies that optimize performance over time. However, RL also faces several challenges, including the conflicts encountered in co-optimizing energy savings, occupant comfort, and indoor air quality, and the requirement for extensive interactions with the environment in training. We proposed a flexible simulation platform that integrates a hybrid model for RL training and designed an RL agent to control the entire central HVAC system, focusing on co-optimizing energy consumption, thermal comfort, and indoor air quality (CO2 and PM2.5 concentrations). Finally, we evaluated the RL agent's performance over an annual cycle. Our findings indicate that the RL agent can effectively manage the HVAC system with 14.7% energy savings annually and balance multiple objectives, which demonstrates significant potential for improving HVAC system control and sustainability in buildings.

Keywords: Control applications, Reinforcement learning
Abstract: This work presents a novel approach to temperature control in buildings using Meta-Reinforcement Learning (Meta- RL) to address uncertainties in building thermal dynamics. The proposed method utilizes a control-oriented model to train a Meta-RL controller in simulation across a wide range of building dynamics, generated by varying the most influential parameters of the building dynamics within large uncertainty ranges. As a result, the Meta-RL controller learns adaptable control strategies tailored to the dynamic characteristics within the whole uncertainty region. Initially, an RL agent is employed to create a suitable encoding of thermal dynamics parameters. During the deployment, an Adaptation Module substitutes the RL agent inferring the right encoding from real-time data. The paper provides details on the specifically designed modeling and control architecture and its parametrization. Extensive validation demonstrates that the developed Meta-RL architecture is able to effectively and quickly adapt to diverse building dynamics, is as efficient as a standard dedicated MPC control, and finally is also able to quickly restrict the uncertainty ranges during deployment

Keywords: Adaptive control, Optimization, Mechatronic systems
Abstract: High performance demands on accuracy and system throughput are common in many industrial applications, making well-designed control laws and reference trajectories essential. Yet, while system performance depends on the joint effects of feedforward control, feedback control, and the design of the reference, these different elements of the control system are typically designed sequentially rather than simultaneously. Such a sequential design approach potentially leads to a loss in obtainable performance. In this work, we present a novel unified data-driven co-design framework for feedforward, feedback, and/or reference design. This framework aims at minimizing the duration of the transient phase in setpoint control: the time required to execute a point-to-point task and reach a desired level of accuracy. The efficacy of the proposed approach in minimizing this duration is demonstrated in a case study of an industrial wire bonder system, in which reductions of up to 54% in the duration of the transient phase were obtained compared to the current industrial state-of-practice.

Keywords: Discrete event systems, Reinforcement learning, Optimization
Abstract: A critical question in multi-agent reinforcement learning (MARL) is what the agents may share with each other, and how to utilize the information shared by others. A key challenge is to provide mathematical analysis on this issue rather than just ad hoc numerical analysis. Though mathematical analysis in general might be challenging, the particular structural property of supply demand matching problems might provide a clue. We consider this important issue in this work, and make the following major contributions. First, we formulate the sample allocation problem to optimize the performance of the policy learned by the multiple agents in supply demand matching problems, and convert the problem to the maximization of the probability of correctly selecting (PCS) the best action at a given state. Second, we provide an algorithm to iteratively allocate the sample budget across the action space to best learn from the other agents. Third, we show that this algorithm asymptotically maximizes the PCS. And we prove that under certain conditions, this algorithm strictly improves this PCS comparing with not sharing. We hope that this work sheds light on quantifying how knowledge shall be shared and digested in MARL.

Keywords: Energy Systems, Iterative learning control, Marine/underwater robotics
Abstract: In this paper, we present the methodology and results for a real-time velocity trajectory optimization for a solar-powered autonomous surface vessel (ASV), where we combine indirect optimal control techniques with iterative learning. The ASV exhibits cyclic operation due to the nature of the solar profile, but weather patterns create inevitable disturbances in this profile. The nature of the problem results in a formulation where the satisfaction of pointwise-in-time state of charge constraints does not generally guarantee persistent feasibility, and the goal is to maximize information gathered over a very long (ultimately persistent) time duration. To address these challenges, we first use barrier functions to tighten pointwise-in-time state of charge constraints by the minimal amount necessary to achieve persistent feasibility. We then use indirect methods to derive a simple switching control law, where the optimal velocity is shown to be an undetermined constant value during each constraint-inactive time segment. To identify this optimal constant velocity (which can vary from one segment to the next), we employ an iterative learning approach. The result is a simple closed-form control law that does not require a solar forecast. We present simulation-based validation results, based on a model of the SeaTrac SP-48 ASV and solar data from the North Carolina coast. These simulation results show that the proposed methodology, which amounts to a closed-form controller and simple iterative learning update law, performs nearly as well as a model predictive control approach that requires an accurate future solar forecast and significantly greater computational capability

Keywords: Aerial robotics, Learning, Control applications
Abstract: This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based on Simulink for performance verification. Finally, the resulting gains are implemented in a physical quadrotor and the results for waypoint and trajectory tracking are reported in this paper. The proposed control approach is the continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm, which is simpler to implement within continuous-time simulation environments and whose performance does not depend on appropriate sampling time choice.

Keywords: Mobile Robots, Robust control, Observers
Abstract: Mecanum-wheeled mobile robots enable omnidirectional motion, allowing for enhanced motion and benefiting applications like surveillance, exploration, and transport. However, strong susceptibility to slippage and vibrations cause several positioning errors. Therefore, advanced control techniques still require attention. This work presents a practical guide using a quasi-model-free control approach based on the active disturbance rejection framework, supported by theoretical background. The experimental results show that, even with minimal knowledge of model parameters, the quasi-model-free control approach achieves motion accuracy improvements compared with a proportional-derivative controller ranging for 49% to 60%.

Keywords: Mechanical systems, Optimization, Planning
Abstract: This paper considers the trajectory generation to minimize the motion time with suppressing vibration for mechanical systems like a overhead crane.Though many previous works for similar objectives have been conducted thus far, a practical method to speed up without vibration is still desired because it is an important issue deeply related to factory productivity. In this paper, the effectiveness of including a preshaper in rajectory optimization of vibration system is investigated.We present a design method of time-optimal motion trajectories or mechanical systems, which assumes to include a presahper for vibration suppression. It is shown that the presented approach enables a mechanical system to reach the target position faster than conventional methods, while satisfying constraints including residual vibration suppression. Simulation and experimental results demonstrated the effectiveness of the proposed method.

Keywords: Variable structure systems, Robotics applications, PID control
Abstract: This work investigates relay-based methods to reveal the frequency domain characteristics of BlueROV2 underwater vehicle. Two relay-based approaches are examined: the Modified Relay Feedback Test (MRFT) and a two-relay (twisting-type) controller. The distinguishing feature of these relay tests is their ability to capture process frequency-response data at a desired phase angle in the Nyquist plot. Approximate equivalence of these tests is also discussed. Influence of additional dynamics on oscillations for these tests is discussed and it is argued that these relay tests have capability to excite both actuator and plant dynamics simultaneously, hence provide a mean to perform full system identification and precise controller tuning. Experiment and simulations of oscillation tests are also presented.

Keywords: Control applications, Cooperative control, Aerial robotics
Abstract: This work addresses the practical problem of distributed formation tracking control of a group of quadrotor vehicles in a relaxed sensing graph topology with a very limited sensor set, where only one leader vehicle can access the global position. Other vehicles in the formation are assumed to only have access to inter-agent bearing (direction) measurements and relative velocities with respect to their neighbor agents. A hierarchical control architecture is adopted for each quadrotor, combining a high-gain attitude inner-loop and an outer-loop bearing-based formation controller with collision avoidance augmentation. The proposed method enables a group of quadrotors to track arbitrary bearing persistently exciting desired formations, including time-varying shapes and rotational maneuvers, such that each quadrotor only requires relative measurements to at least one neighboring quadrotor. The effective performance of the control strategy is validated by numerical simulations in MATLAB and real-world experiments with three quadrotors.

Keywords: Nonlinear systems, Neural networks
Abstract: This paper investigates the robustness of the Lur’e problem under positivity constraints, drawing on results from the positive Aizerman conjecture and robustness properties of Metzler matrices. Specifically, we consider a control system of Lur'e type in which not only the linear part includes parametric uncertainty, but the nonlinear sector bound is also unknown. We investigate tools from positive linear systems to effectively solve the problems in complicated and uncertain nonlinear systems. By leveraging the positivity characteristic, we derive an explicit formula for the stability radius of Lur’e systems. Furthermore, we extend our analysis to systems with neural network (NN) feedback loops. Moreover, we propose a method for obtaining tight sector bounds for NNs. This study introduces a scalable and efficient approach for robustness analysis of both Lur’e and NN-controlled systems. Finally, the proposed results are supported by illustrative examples.

Keywords: Nonlinear robust control, Robust control, Nonlinear systems
Abstract: Regressor-based control of nonlinear systems uses a linear parameterization of the system dynamics. Most regressor-based controllers only consider uncertainties in the parameter vector, ignoring unmodeled dynamics and perturbations that might be present in the regressor matrix -- factors that can significantly deteriorate the controller's performance. In this paper, we propose a novel robust control approach with safety guarantees that also accounts for uncertainties in both the regressor matrix and the parameter vector. We design a control law comprised of several signals that ensure uniform ultimate boundedness of the tracking error and forward invariance of a desired safe set via Control Barrier Functions. The controller's stability and safety are validated using numerical simulations, while Monte Carlo simulations demonstrate its robustness to random perturbations in the modeling uncertainties.

Keywords: Sliding mode control, Manufacturing systems, Process control
Abstract: The laser powder bed fusion (LPBF) additive manufacturing process involves fusing powdered metals with a high powered laser moving at a high speed. As this process is governed by a complex multi-phase interaction between multiple physical domains, several models have been developed of varying complexity. This paper adopts a non-linear first order model from the literature governing the surface area of the moving melt pool created by the laser heating of the powder, as control of this state can lead to improved build quality (small melt pool hinders metal fusion, large melt pools lead to material evaporation and stress-concentrations in the final part). The model is reviewed, and a second order state space model is constructed using the error between the actual melt pool size and a desired reference value. The power supplied to the laser is used as a control input, and an exponentially stable sliding manifold is defined. A control law is defined to drive the system states to the manifold in finite time and hold it there, ensuring a desirable melt pool size throughout the duration of the build. Simulated results are presented, indicating the effectiveness of the control design using gains for slow and fast convergence.

Keywords: Sliding mode control, Adaptive control, Control Technology
Abstract: Abstract—This paper presents an Adaptive Integral Terminal Sliding Mode Control (AITSMC) strategy for a Steer-by-Wire (SBW) system to improve robustness and steering performance under parameter variations, external disturbances, and changing road conditions. AITSMC incorporates an adaptation mechanism to estimate the self-aligning torque coefficient, allowing real-time compensation for road condition variations. To evaluate its effectiveness, Conventional Sliding Mode Control, Adaptive Sliding Mode Control, and Integral Terminal Sliding Mode Control are also designed and analyzed. A comprehensive comparative study through MATLAB/Simulink simulations demonstrates the superiority of AITSMC in robustness and tracking performance against diverse road conditions as well as parametric uncertainties, which contributes to improve vehicle handling and stability of SBW systems.

Keywords: Sliding mode control, Observers, Estimation
Abstract: State estimation and control of quantum systems present significant challenges due to the inherent noise, uncertainty, and non-linearity of quantum dynamics. In this paper, we propose the application of Sliding Mode Observers (SMOs) as a robust and efficient solution for the state estimation and control of finite dimensional quantum systems whose state is described by a density matrix ρ. We demonstrate how these observers can be used to track quantum states accurately, despite the presence of measurement noise and system dynamics uncertainties. Furthermore, we introduce a control strategy that leverages the state estimates to stabilize the quantum system, ensuring desirable performance even under challenging conditions. Numerical simulations are provided to illustrate the effectiveness of the proposed methodology, showcasing improvements in system stability, accuracy of state estimation, and robustness to perturbations. The results highlight the potential of sliding mode observers as a viable tool for quantum control applications, including quantum information processing, quantum computing, and other emerging quantum technologies.

Keywords: Control Technology