Keywords: Multi-agent Systems, Networked Control
Abstract: In recent years, multi-agent systems have attracted considerable attention, particularly with regard to their security dimensions in presence of Byzantine attacks. However, existing Byzantine-resilient algorithms rely on relatively strong network connectivity conditions, raising the question of whether these requirements can be relaxed while balancing performance trade-offs. To address this, we partition the full state of each agent into multiple blocks and leverage an essentially cyclic rule for each block update, based on which a filter-based block consensus algorithm is proposed. This approach not only relaxes network connectivity requirements but also reduces bandwidth usage per update, making it suitable for a wider range of networks. Theoretical analysis demonstrates that the algorithm achieves linear consensus speed, ensuring fast and robust aggregation among nodes despite Byzantine agents.

Keywords: Real-time Systems, Flexible Manufacturing Systems, Control Applications
Abstract: Digital Twins (DTs) are virtual representations of physical systems or Physical Twins (PTs) that are used for various data-driven applications in manufacturing industry such as predictive maintenance, diagnostics and condition monitoring. The data in DTs is collected through virtual sensors, which augment the physical sensors by providing additional data that cannot be directly observed. In high-throughput production systems like semiconductor manufacturing equipment, high-speed and high-precision motion stages control the equipment's movement. However, the use of DTs in these systems is limited due to their real-time requirements. To enable real-time applications, the DT must be synchronized with its physical counterpart to ensure timely data from virtual sensors. The synchronization mechanism should be non-blocking to prevent any impact on the throughput of physical systems. In this paper, we propose a synchronization mechanism for DTs in high-speed high-precision motion control systems. The mechanism involves sharing PT states with DT over a network and compensating for network delays. The synchronization mechanism is validated in a framework comprising an industrial motion stage system and its digital twin. The validation is done for different synchronization delay scenarios, demonstrating its effectiveness of proposed approach in eliminating synchronization delays without blocking the PT operation. The proposed mechanism enables real-time virtual sensing ensuring data timeliness with high accuracy.

Keywords: Nonlinear Systems and Control, Linear Systems, Discrete Event Systems
Abstract: This paper investigates the sampled-data feedback stabilization problem of a PDE-ODE cascade system in which both the actuator and the input experience arbitrarily long delays. The backstepping-forwarding technique is employed to effectively transform the original system with delays into a target system, governed by a linear differential equation with sampled and delayed inputs. A new sampled-data boundary controller is then proposed, extending the applications of truncated predictor feedback to delayed sampling systems, simultaneously compensating for delays and sampling effects while enhancing practicality and robustness. The stability of the target system is established via a small-gain-based analysis, which in turn ensures exponential stability of the original closed-loop system through the input-to-state stability (ISS) framework. Theoretical findings are supported by simulations.

Keywords: Multi-agent Systems, Linear Systems, Networked Control
Abstract: This paper investigates distributed Nash equilibrium (NE) seeking in linear multi-agent network games, where agents with unknown dynamics interact over weakly connected directed graphs under external disturbances. By reformulating the NE seeking problem as a cooperative output regulation problem, we develop a data-driven controln framework that embeds the internal model principle to achieve disturbance rejection and eliminate steady-state errors. Closed-loop stability and convergence to the unique NE are proven under standard assumptions on stabilizability, detectability, and data richness. Numerical experiments with a mobile robot network demonstrate the method’s effectiveness in achieving output NE seeking under noisy measurements and external disturbances.

Keywords: Robotics, Learning Systems, Learning-based Control
Abstract: Image-goal navigation ranks among the critical tasks in embodied robotic visual navigation, requiring the robot to navigate to the goal position indicated by the image in an unknown environment. While recent works have made progress in image-goal navigation by constructing image-based topological maps, the complexity of the real world, along with the lack of directional control and semantic information in topological maps, still pose significant challenges to robustness and reliability. To solve this problem, we propose a new strategy that utilizes a semantic topological map to help the robot’s navigation in unknown environments. Specifically, we quantitatively analyze the directional relationship between the current and goal nodes to construct the semantic edges of the topological map. This approach enables the robot to make informed decisions by dynamically assigning physically meaningful directional semantics to each edge. We deploy this method on a real-world four-wheeled ground robot, relying solely on visual input (RGB), to realize the image-goal navigation task. The experimental results demonstrate that the proposed approach significantly outperforms other highperformance baseline approaches in terms of navigation success rate, demonstrating excellent robustness.

Keywords: Motion Control, Adaptive Control
Abstract: This paper presents a novel approach to reducing tracking errors using a model-based input shaping algorithm. The proposed method features a specialized pre-filter design that shapes the input to ensure zero steady-state tracking error for higher order polynomial inputs. Unlike conventional methods that may compromise system stability, this algorithm exclusively modifies the input signal, leaving the inherent system stability unaffected. An additional advantage of our approach is the simplification of the main controller design by taking over the handling of steady-state errors. This allows the controller to focus on other performance criteria (BW, GM, PM) without being overly complex. By making the system behave as if it were a higher order "System Type" without the need for multiple pure integrators in the open loop, our method enhances performance and robustness. The algorithm's potential applicability spans a wide range of fields, including robotics, aerospace, and industrial automation, demonstrating its versatility and effectiveness in improving control system precision and reliability.

Keywords: Control Applications, Motion Control, Intelligent and AI Based Control
Abstract: Information entropy is an important measure that quantifies the uncertainty associated with random variables, and has been widely used in signal processing, machine learning, wireless communication, etc. For a dynamical control system, its behavior can also be measured by information entropy, once we assume its initial state follows a certain distribution. This measure is defined as state entropy, which forms a new basic property of control systems, except for stability, robustness, and adaptability. This paper systematically studies the definition and property of state entropy for deterministic control systems, and introduces the mechanism of its time evolution in continuous-time, discrete-time, linear and non-linear cases. We show that the time derivative of state entropy is the integral of trace of control Jacobian matrix over the initial state distribution, where the control Jacobian matrix is the full derivative of successive state with respect to the initial state. For control systems with constant value of divergence of its vector field, the evolution of state entropy is calculated without the knowledge of precise trajectory, as the trace is independent from the initial distribution. The variation of state entropy is exclusively determined by the positivity of the trace of control Jacobian matrix. We also derive the mathematical form of state entropy for discrete-time control systems. Our work provides a new angle to understand the behavior of dynamical control systems.

Keywords: Control Applications, Motion Control, Learning-based Control
Abstract: This paper investigates the trajectory tracking problem of a micro linear piezoelectric actuator (LPA) based on a Variable Forgetting Factor Iterative Learning Control (FFILC). Compared to traditional PID control methods, FFILC demonstrates advantages in improving response speed, resistance to disturbances, and smoother tracking curves. Compared to first-order Iterative Learning Control (ILC), FFILC enhances the disturbance rejection capability and tracking accuracy of the iterative learning algorithm by integrating existing data, offering valuable reference for engineering practice. Experimental results show that FFILC has faster convergence and higher tracking accuracy in step signal and sinusoidal signal trajectory tracking compared to PID control and first-order ILC.

Keywords: Motion Control, Multi-agent Systems, Nonlinear Systems and Control
Abstract: Aerial refueling technology utilizes tankers to refuel other aircraft in the air. Before sequential aerial refueling, multiple UAVs (including the tanker and receivers) are expected to perform a formation transformation. In this process, multiple receivers start from the observation area on the left side of the tanker and move to the pre-docking position to prepare for docking. During the transformation, the tanker and receivers maintain a close formation, characterized by small inter-aircraft distances and low safety margins. To ensure inter-aircraft safety during formation transformation, this work first defines the safety envelopes of the tanker and receivers according to their fuselage shapes. Based on these envelopes, a safe flight area for the UAVs is generated. Within this area, relative flight paths for formation transformation are planned using the Rapidly-exploring Random Tree (RRT) algorithm. These planned paths serve as desired positions within the formation, and an appointed-time prescribed performance control (APPC) method is designed to ensure flight safety throughout the transformation. Finally, simulations are conducted to demonstrate the effectiveness of the proposed scheme.

Keywords: Intelligent and AI Based Control, Motion Control, Control Applications
Abstract: This study presents an AI-driven hybrid model integrating a Deep Q-Network (DQN) and a Backpropagation Neural Network (BPNN) to enhance the motion performance of CNC machine tools. The ANOVA method is applied to analyze experimental data, while Pearson correlation aids in identifying key interpolation parameters. A trained BPNN, designed as a virtual system environment, models the dynamic behavior of CNC machine tools. The DQN interacts with this virtual system, optimizing interpolation parameters by maximizing the rewards. The optimized interpolator parameters are validated through standard CNC speed and accuracy tests. Experimental results demonstrate that the proposed AI-driven approach can further improve motion efficiency while maintaining contour accuracy, outperforming traditional manual tuning and BPNN methods.

Keywords: Motion Control, Nonlinear Systems and Control, Optimal Control
Abstract: This paper proposes a 3D Clothoid-based decoupled path and speed planning framework for the fixed-wing UAV to generate smooth and dynamically feasible trajectory in 3D space. Using 3D Clothoid curves, the framework ensures curvature continuity, while the decoupled planning approach lowers computational complexity by separating spatial and temporal optimization. A data association mechanism links path points with attractors, repellers, and barriers. An obstacle avoidance model based on these constraints, combined with repeller potentials and barrier functions, ensures safe navigation in constrained environments. Experimental results validate the ability of the framework to produce safe, smooth, and efficient trajectories, demonstrating its suitability for advanced UAV operations in complex environments.

Keywords: Nonlinear Systems and Control, Estimation and Identification
Abstract: A method for joint state and disturbance estimation is developed for discrete-time nonlinear reversible control systems. The observer is constructed in two steps. First, the state vector is extended to include also the disturbances. Under the assumption that the disturbances are constant, the extended state equations depend only on the extended state variables and input variables. Thus, a standard state observer can be constructed for the extended state equations. For this, in the second step one transforms the extended state equations into the generalized observer form for which an observer can be easily constructed. Since the extended state includes both the state variables and the disturbance variables, the observer will find the estimates of both. Finally, the developed method is applied to estimate the state and parameter values of type 1 diabetes model.

Keywords: Nonlinear Systems and Control, Control Applications
Abstract: A nonlinear orbital station-keeping solution for the circular and elliptic versions of the Earth-Moon Restricted Three-Body Problem (R3BP) is developed via a backstepping technique. Formal guarantees for global asymptotic stability (GAS) are attained, as shown through Lyapunov’s stability theory. The adequacy of the proposed control law is evaluated through the means of numerical trials over closed periodic solutions of the circular and elliptic R3BPs. The ramifications of the control gain choice are carefully studied and simulated.

Keywords: Nonlinear Systems and Control, Control Applications
Abstract: Zero dynamics are essential to the design and analysis of input-output feedback linearization control, particularly in nonlinear systems. This study revisits an input-output feedback linearization problem through an illustrative example where the zero dynamics admits multiple equilibrium points. The effect of such zero dynamics on system performance and control input has also been investigated. Conventional tools such as 1-contraction and Lyapunov theory are not directly applicable to the analysis of zero dynamics with multiple equilibria. Therefore, the theory of 2-contraction is employed to perform the analysis effectively. The impact of such zero dynamics on the external control input, tasked with regulating the system's output is also analyzed. Extensive numerical simulations are conducted to validate and exemplify the theoretical results.

Keywords: Nonlinear Systems and Control, Fuzzy and Neural Systems, Robust and H infinity Control
Abstract: This paper investigates the problem of H∞ filtering for continuous-time Takagi-Sugeno fuzzy systems. The method adopts the continuous-time model, effectively capturing the complex characteristics of Takagi-Sugeno fuzzy systems. Utilizing integral Lyapunov functions and employing extra free variables, sufficient conditions in the form of linear matrix inequalities are derived. Compared with existing fuzzy filtering methods, this design significantly reduces the conservativeness. Finally, the effectiveness and superiority of the proposed method are verified through two simulation examples.

Keywords: Adaptive Control, Learning-based Control, Nonlinear Systems and Control
Abstract: Resilient performance is a critical safety aspect required for the learning-enabled control of safety-critical mechanical systems, which is an additional consideration for standalone static safety sets and currently is not yet well investigated. To address this limitation, this work proposed an adaptive safe optimized backstepping control for the high-order strict-feedback system that realizes resilient performance by flexibly adjusting the safe performance boundary with consideration of the system saturation limitation. Therefore, the concept of safety in the proposed method codifies the inherent safe adaptive learning mechanism and this resilient performance. More specifically, firstly, the adaptive optimized safe control of the overall system is designed with safe optimized backstepping and constraint adaptation mechanism to ensure the ultimate policy within the safe region during the whole learning phase. The constructed adaptive safe learning framework embodies the decomposition design with Barrier Lyapunov Functions-based optimized backstepping control that considers the performance-based state-variables constraints. Secondly, considering the resilient performance, the auxiliary system is designed to adaptively generate flexible performance boundaries based on the input saturation error while conflicts of saturation limitation occur, enabling the overall learning system to attain additional resilient performance during the whole control period. Comparison simulations consider the typical vehicle's longitudinal control problem to verify the effectiveness of the proposed method in showing the resilient performance of learning-enabled control of safety-critical systems.

Keywords: Signal Processing, Sensor/Data Fusion, Optimal Control
Abstract: This article addresses the nonlinearity of Doppler-bearing measurements for target tracking with a pseudolinear Kalman filtering (PLKF) scheme. A pseudolinear equation for range rates is derived by taking the Taylor series expansion of the radial unit vector, and a precise model of the corresponding noise is presented along with statistical analysis. To achieve superior estimation performance with low computational complexity, one-step state predictions are employed to construct an instrumental variable-based PLKF (IV-PLKF). A strategy to handle initial uncertainties is developed. Monte Carlo simulations are provided to illustrate the performance by comparing the IV-PLKF with existing algorithms and theoretical bounds.

Keywords: Control Applications, Automated Guided Vehicles
Abstract: This paper presents a novel shared steering control approach aimed at enhancing driver steering performance while adhering to system constraints. The proposed method employs an Interpolating Control (IC) approach to dynamically balance the influence between the human driver and the autonomous controller. Additionally, the methodology incorporates a Simple Interpolation Control (SIC), eliminating the need for online numerical optimization. The SIC generates a sub-optimal interpolation coefficient to weigh control inputs while maintaining system response within predefined constraints. Simulation results demonstrate that the controller improves driver performance across different characteristics when compared to scenarios without shared control.

Keywords: Control Applications, Robotics, Motion Control
Abstract: This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case for long-time flight or aggressive maneuvers. Firstly, the refined dynamics of rotor drones considering VD disturbance are presented. Based on the dynamics, a voltage drop observer (VDO) is developed to accurately estimate the VD disturbance by decoupling the disturbance and state information of the drone, reducing the conservativeness of conventional disturbance observers. Subsequently, the control scheme integrates the VDO within the translational loop and a fixed-time sliding mode observer (SMO) within the rotational loop, enabling it to address force and torque disturbances caused by voltage drop of the battery. Sufficient real flight experiments are conducted to demonstrate the effectiveness of the proposed control scheme under VD disturbance.

Keywords: Control Applications, Multi-agent Systems, Nonlinear Systems and Control
Abstract: This paper presents a nonlinear model predictive control (NMPC) framework integrated with control barrier functions (CBFs) and control Lyapunov functions (CLFs) for safe formation control of multiple vertical take-off and landing unmanned aerial vehicles in dense environments. In realworld scenarios, UAVs face significant challenges in collision avoidance and safe inter-vehicle distance maintenance. The proposed method integrates formation-keeping constraints with safety-critical constraints, ensuring collision avoidance and dynamic feasibility. By leveraging NMPC’s predictive capability alongside the safety guarantees of CBFs and CLFs, our approach effectively generates smooth flight trajectories while maintaining formation and avoiding obstacles. The proposed method demonstrates reliable performance in dynamic and constrained environments, providing a practical solution for real-world applications.

Keywords: Control Applications, Linear Systems, Process Control & Instrumentation
Abstract: In this paper, we present a software framework implementing Model Predictive Control (MPC), a widely adopted method in industrial automation. Designed for versatility, it operates with a range of target platforms—such as programmable logic controllers and distributed control systems. Notably, beyond its core MPC functionality, the framework supports multiple realtime simulation models, positioning it not merely as a conventional controller but also as a foundational digital twin for industrial processes. This capability facilitates a seamless transition from academic research to real-world application—an area that often proves challenging due to the gap between laboratory settings and practical industry requirements. The experimental validation on a laboratory test bench demonstrates the effectiveness of the proposed solution, underscoring its role as a bridge between academic developments and industrial implementation.

Keywords: Control Applications, Adaptive Control, Robotics
Abstract: This paper addresses the challenge of developing an integrated nonlinear adaptive controller for under-actuated high-speed marine vehicles equipped with a propeller and rudder. Leveraging a previously identified model, the proposed controller is designed to ensure trajectory tracking despite constant external disturbances and unknown dynamical parameters. A detailed description of our cost-effective testing setup based on the PX4 ecosystem is provided, enabling researchers to readily replicate this high-speed ASV research platform capable of speeds up to 12 m/s. Experimental results demonstrate the effectiveness of the proposed system, with the vehicle successfully following a challenging trajectory consisting of arcs and straight lines at speeds up to 3 m/s.

Keywords: Control Applications, Automated Guided Vehicles, Motion Control
Abstract: In the broader context of the automotive industry, a significant percentage of investments are directed towards the development and implementation of advanced automated driving systems aimed at enhancing the safety of drivers and other road users during driving. This study proposes a control method for an automatic lane-keeping system based on a lateral dynamic model with error correction, specifically targeting the minimization of position and orientation errors. The objective is achieved by implementing a state-feedback control approach combined with feed-forward control, resulting in a combined control strategy that ensures both anticipation of the system dynamics and effective error correction. The framework is augmented by utilizing a trajectory derived from real-world coordinates, complemented by the integration of variable longitudinal speed, which directly influences the vehicle's lateral dynamics. This variable speed approach enhances the realism of the system by closely mimicking the dynamic behavior of a vehicle under actual driving conditions. Overall, the study demonstrates the effectiveness of the proposed control method in minimizing errors encountered during vehicle motion, ensuring precise lane-keeping, and improving both efficiency and safety in driving scenarios.

Keywords: Intelligent and AI Based Control, Modeling and Control of Complex Systems, Process Automation
Abstract: The rapid growth of Internet traffic necessitates the adoption of effective routing algorithms to ensure faster and more stable network transmission. Single-path routing is insufficient for current demands, and the performance of multipath routing needs enhancement to address the constraints and requirements of practical scenarios. In this paper, we propose a comprehensive reinforcement learning assisted knowledge-based evolutionary algorithm (CRL-KEA) to solve multipath routing problems with the constraints of practical applications. In this method, deep reinforcement learning with a differentiated encoder and decoder (DRL-DC) is utilized to assist in constructing the initial population. By integrating the current network load state, DRL-DC achieves efficient subpaths construction. Moreover, various operators with specific problem knowledge are adopted to guide the solution updates and repair infeasible solutions. In this way, our method enables the rapid provision of high-quality multipath routing schemes for all network flows. Through experiments conducted under various network topologies, we demonstrate that CRL-KEA has significant advantages in both quality and speed.

Keywords: Motion Control, Real-time Systems, Optimal Control
Abstract: Currently, path planning for unmanned aerial vehicles (UAVs) in dynamic environments still faces risks and challenges such as poor adaptability and high collision risks caused by frequent environmental changes. This paper proposes a hybrid planning framework that integrates trajectory prediction with the Priority-aware Dynamic Window Approach (P-DWA). The framework constructs a trajectory prediction model based on dynamic obstacle position data, integrating time weights and uncertainty quantification. During the path search process, a priority queue mechanism is implemented. This mechanism is combined with a risk-aware collision cost function to avoid local optima. Simulation results demonstrate that the proposed method outperforms EGOv2 and DP in dynamic obstacle scenarios, particularly in terms of planning success rate and obstacle avoidance. Real-world UAV flight tests further validate the method's effectiveness in complex dynamic environments, showcasing its robustness and reliability.

Keywords: Real-time Systems, Learning Systems, Smart Buildings
Abstract: Effective building defect management is vital for the operational safety, longevity, and resilience of modern high-rise structures. However, traditional approaches often rely on a patchwork of inspection logs, manual measurements, and inconsistent documentation, which can complicate maintenance efforts and risk assessments. In this paper, we propose Defect-GPT, a framework that integrates Digital Twin (DT) modeling, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to streamline the lifecycle defect inspection and management. The system collects multi-modal data from Unmanned Aerial Vehicle (UAV) flights, Building Information Models, and Geographic Information Systems (GIS) to build a comprehensive digital twin of a high-rise building. On top of this unified data model, a fine-tuned LLM performs context-aware retrieval and generation, providing defect-related insights and maintenance recommendations that are grounded in up-to-date building data. Periodic field studies in Hong Kong demonstrate that this approach significantly enhances defect data retrieval precision and expedites decision-making workflows by leveraging domain-specific knowledge. These findings open new avenues for AI-driven building management, particularly in large-scale urban environments where efficient, robust, and explainable maintenance systems are increasingly essential.

Keywords: Motion Control, Learning-based Control, Control Applications
Abstract: This paper presents a model predictive controller for truck-trailer systems in off-road environments that takes into account uncertainties of the employed vehicle model. Model predictive controllers are widely used in the field of truck-trailer systems, as they enable to plan complex maneuvers, account for obstacles and consider system limits. A common model is derived from the kinematics of the vehicle. This model is based on the assumptions of flat surfaces and no slip. Driving in harsh environments, however, these assumptions are often violated, resulting in model uncertainties and thus in an impaired tracking accuracy. To address this issue, this paper enhances the kinematic model by Gaussian Process-based correction models that are adapted to the road conditions online. To avoid potentially dangerous maneuvers, the paper further proposes to consider the prevention of high model uncertainties an objective of the controller. The presented methods are evaluated in a high-fidelity simulation environment.

Keywords: Nonlinear Systems and Control, Learning-based Control, Optimal Control
Abstract: Motion planning remains a critical research challenge in robotics, particularly for nonlinear systems where balancing safety, optimality, and computational efficiency poses inherent trade-offs. While numerous planners have been proposed to address these competing objectives, reconciling rigorous safety guarantees with real-time performance remains an open problem. This paper investigates safe near-optimal robotic motion planning and introduces a reinforcement learning (RL) framework integrating a neural network (NN)-approximated target navigator and a high order control barrier function (HOCBF)-based safeguarding controller. Lyapunov analysis provides formal guarantees on the near-optimality of the derived controller, the convergence of the robot to its target state, and the forward invariance of the robot within the collision-free set. Experiments on a four-dimensional unicycle-like robot validate the theoretical findings.

Keywords: Nonlinear Systems and Control, Adaptive Control, Motion Control
Abstract: Hybrid Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) demonstrate more flexible advantages in complex missions due to their capability to VTOL and conduct high-speed horizontal flight. But their ability to achieve safe transition flight still remains a challenge. This paper proposes a resilient transition control strategy for a VTOL UAV under the compounded impact of actuator faults and disturbances. A novel reaching law is introduced in the baseline sliding mode control (SMC) to mitigate the control chattering problem, and SMC is incorporated with a nonlinear disturbance observer to enhance the attenuation capability of external disturbances. On this basis, adaptive SMC is introduced to compensate for adverse effect caused by actuator faults and disturbances. The designed adaptive parameters combine continuous and discontinuous components without merely relying on the discontinuous control parts of SMC. Simulation test is finally carried out to qualitatively and quantitatively validate the superiority of the proposed method.

Keywords: Intelligent and AI Based Control, Learning-based Control, Motion Control
Abstract: Amphibious Unmanned Aerial Vehicle (A-UAV), equipped with multi-sensor systems, has significant value in digitization of water body by monitoring both aquatic and aerial domains. Compared to traditional methods, it offers improved multi-dimensional perception but faces challenges like complex operational paths and data fusion difficulties, which increase the risk of system failures. This study proposes a path planning model for A-UAV based on the CGRUA framework, combining drifting and flying paths to enhance endurance and measurement range. Using buoy data from the Global Drifter Program (GDP buoy ID 2101873), the accuracy of the hybrid CGRUA model predictions and the effectiveness of path planning was validated.

Keywords: Motion Control, Robotics
Abstract: This paper proposes and studies motion primitives and its application to control vehicles operating on a spherical surface. Motion primitives are fundamental motion patterns that enable structured control in robotics and motion planning. Here, they are adapted to movement on a spherical surface and utilized in the context of tethered aircraft guidance. The research defines circular motion on the sphere simply by selecting the center of rotation of a circular path. This work also explores the combination of motion primitives with two path-following guidance methods: we develop an adaptation of the L1 and L0 guidance logics to spherical motion using the proposed primitives. Smooth trajectories, preserving curvature continuity, can be guaranteed by imposing an orthogonality condition at the primitive switching points. Simulation results demonstrate the efficacy of the proposed approach, showcasing successful path adherence manoeuvres that can be applied in the take-off of tethered fixed-wing aircraft.

Keywords: Motion Control, Control Applications, Nonlinear Systems and Control
Abstract: The ability of automated vehicles to avoid obstacles on the road is one of the most critical functionalities, as it directly impacts passenger safety. The trajectory planning functionality plays a key role in this task, as it uses data from sensors to identify obstacles and plan a free obstacle path. Thus, this paper proposes a trajectory planning method based on a model predictive control (MPC) strategy that takes into account both static and dynamic obstacles. Additionally, a trajectory tracking controller based on a linear quadratic regulator (LQR) algorithm is designed to ensure the vehicle follows the planned path. To evaluate the proposed control solution for the lateral dynamics of the vehicle, a traffic scenario involving multiple obstacles (both static and dynamic) is used. The results show that the proposed solution effectively controls the vehicle's lateral dynamics and successfully avoids collisions with obstacles.

Keywords: Multi-agent Systems, Automated Guided Vehicles, Robotics

Keywords: Networked Control, Nonlinear Systems and Control, Robotics
Abstract: This paper proposes an event-triggered polynomial control method for trajectory tracking by unicycle robots. In this method, each control input between two consecutive events is a polynomial and its coefficients are chosen to minimize the error in approximating a continuous-time control signal. We design an event-triggering rule that guarantees uniform ultimate boundedness of the tracking error and non-Zeno behavior of inter-event times. We illustrate our results through a suite of numerical simulations and experiments, which indicate that the number of events generated by the proposed controller is significantly less compared to that by a time-triggered controller or a event-triggered controller based on zero-order hold while guaranteeing similar tracking performance.

Keywords: Learning-based Control, Linear Systems, Optimal Control
Abstract: This paper presents a novel methodology for controlling nonlinear systems by integrating Koopman operators with Distributionally Robust Model Predictive Control (K-DRMPC). The proposed approach addresses the challenges posed by external disturbances and model uncertainties by modeling these factors as random variables characterized by their first and second-order moment information, which are estimated from empirical data. Initially, the nonlinear system is linearized using the Koopman operator, transforming the control problem into a linear framework that facilitates control design. Subsequently, K-DRMPC is employed to reformulate state chance constraints into Second-Order Cone (SOC) constraints, ensuring robust performance in the presence of uncertainties. Numerical experiments validate the effectiveness of the proposed approach under disturbances.

Keywords: Networked Control, Optimal Control, Control Applications
Abstract: Networked control systems enable real-time control and coordination of distributed systems, leveraging the low latency, high reliability, and massive connectivity offered by 5G and future 6G networks. Applications include autonomous vehicles, robotics, industrial automation, and smart grids. Despite networked control algorithms admitting nominal stability guarantees even in the presence of delays and packet dropouts, their practical performance still heavily depends on the specific characteristics and conditions of the underlying network. To achieve the desired performance while efficiently using communication resources, co-design of control and communication is pivotal. Although periodic schemes, where communication instances are fixed, can provide reliable control performance, unnecessary transmissions, when updates are not needed, result in inefficient usage of network resources. In this paper, we investigate the potential for co-design of model predictive control and network communication. To this end, we design and implement an event-triggered nonlinear model predictive controller for stabilizing a Furuta pendulum communicating over a tailored open radio access network 6G research platform. We analyze the control performance as well as network utilization under varying channel conditions and event-triggering criteria. Additionally, we analyze the network-induced delay pattern and its interaction with the event-triggered controller. Our results show that the event-triggered control scheme achieves similar performance to periodic control with reduced communication demand.

Keywords: Sensor Networks, Learning Systems, Intelligent and AI Based Control
Abstract: Internet of Things (IoT) honeypots are decoy systems deployed to entice attackers to gather threat intelligence and protect real systems. High-interaction IoT honeypots powered by reinforcement learning (RL) have emerged as a promising solution due to their cost-effectiveness and scalability. However, these systems are typically based on the assumption that attackers exhibit stationary behavior. In reality, attack strategies against IoT can be dynamic and adaptive, creating a non-stationary environment due to the adversarial nature of attacker-honeypot interactions. To solve this issue, we propose an IoT honeypot based on adversarial reinforcement learning, i.e., Repeated-Update-Q-learning (RUQL, a classical RL method for non-stationary environments). It is composed of a data preprocessing module, an RUQL module, and a response database. Experimental results show that compared to honeypots based on random strategies, classical Q-learning, and deep RL, the proposed system can effectively respond to attacks and improve attack capture and analysis capabilities.

Keywords: Multi-agent Systems, Networked Control, Optimal Control
Abstract: This paper investigates reach-avoid differential games involving two pursuers and one evader. The pursuers with different capture radii collaborate to protect a target region against the evader whose objective is to reach the target region while avoiding being captured by the pursuers. Previous studies have shown that the cooperation between pursuers can improve their capability of defending the target region against the evader. However, these studies did not account for the collision avoidance between the pursuers when designing pursuit strategies. In this work, the pursuers have different safety radii and must avoid collisions with each other. A feedback capture strategy is proposed to ensure that from a set of states, the pursuers can win the game with collision-free trajectories. This strategy relies on solving an optimization problem induced by the relaxed control barrier function (R-CBF) and a new geometric concept called the alpha-evasion space (alpha-ES). Sufficient conditions to guarantee both the pursuit winning and collision avoidance are presented. Numerical simulations are provided to show the collision-free trajectories of the pursuers under the proposed capture strategy.

Keywords: Learning-based Control, Nonlinear Systems and Control, Process Control & Instrumentation
Abstract: In this paper, we consider learning-based modeling and predictive control of nonlinear systems subject to multi-modal uncertainties. A Gaussian mixture Koopman operator, which learns the evolution of the observable distribution in a higher-dimensional space, is proposed to characterize the dynamics of the underlying nonlinear systems. Based on the learned probabilistic Koopman model, a stochastic model predictive control method with chance constraints is introduced for the nonlinear systems with state constraints. Finally, a simulated chemical process is used to illustrate the effectiveness of the proposed method.

Keywords: Factory Modeling and Automation, Multi-agent Systems, Learning Systems
Abstract: Mission planning of continuous tracking moving targets (MPCTMT) by earth observation satellites is a crucial optimization problem in the field of modeling and optimization. However, due to the unknown task scenarios in multi-objective MPCTMT problem, existing optimization algorithms struggle to effectively address MPCTMT problem. In this study, we propose a novel multi-objective optimization algorithm tailored to address MPCTMT problem. To ensure a well-distributed set of solutions in all unknown scenarios, we introduce an anchors adjustment mechanism. Experimental results demonstrate that the proposed algorithm outperforms existing multi-objective evolutionary algorithms in all possible scenarios.

Keywords: Linear Systems, Optimal Control
Abstract: This paper presents a novel method for the co-design of observers and controllers for switched linear parameter-varying (LPV) systems subject to unknown but bounded uncertainties, disturbances, and faults. First, a polytopic functional interval observer (FIO) is employed to estimate the lower and upper bounds of the system states. Next, a proportional-integral (PI) observer is designed to estimate fault signals accurately. Building on these estimates, a fault-tolerant control (FTC) strategy is developed to ensure the stability of the closed-loop system and maintain reference model tracking in the presence of faults. The sufficient conditions for the existence of observers and controllers are derived using Linear Matrix Inequalities (LMIs), leveraging multiple Lyapunov functions and ensuring input-to-state stability (ISS) under the average dwell time (ADT) approach. Finally, the effectiveness of the proposed approach is validated through a simulation applied to vehicle lateral dynamics estimation and control.

Keywords: Linear Systems, Real-time Systems, Control Applications
Abstract: This paper addresses the problem of state observation techniques specifically designed for positive systems, which represent non-negative natural processes prevalent in various fields. Unlike traditional dynamical systems, positive systems necessitate state observers to maintain non-negativity throughout the estimation process. We propose an algebraic framework for synthesizing positive observers for positive linear systems while preserving positivity, accommodating the inherent constraints of positive systems. The proposed design, also provides a systematic and scalable solution for observer design in cascaded symmetric networks of positive systems. Through simulations and experimental validation, we demonstrate the effectiveness and versatility of the proposed approach, contributing to the advancement of theory in positive systems and facilitating the analysis to diverse applications.

Keywords: Discrete Event Systems, Multi-agent Systems
Abstract: Inference observability is one of the properties that must be satisfied to synthesize solutions to decentralized supervisory control problems. Current verification strategies require the construction of the complete system model. However, when a system model consists of multiple components, these monolithic approaches may be computationally infeasible due to the state-space explosion problem, a situation that arises when the system's state space increases exponentially with the number of its components. Incremental verification algorithms have been proposed to navigate this issue, where verification occurs component-wise. Our proposed solution to this issue involves updating an algorithm for the incremental verification of co-observability and extending it to inference observability.

Keywords: Control of Distributed Generation Systems, Multi-agent Systems, Control Applications
Abstract: Battery Energy Storage Systems (BESS) is pivotal balancing power supply and demand by dynamically adjusting charging and discharging power. However, their deployment in public networks renders them vulnerable to adversarial attacks, which can disrupt system coordination and potentially lead to failures. To address these challenges, this paper presents a distributed resilient consensus algorithm that integrates the Mean Subsequence Reduced (MSR) method with demand tracking, structured within a leader-follower control framework. The proposed algorithm guarantees that all non-adversarial agents achieve resilient state-of-charge (SoC) consensus and equitable power distribution, even in the presence of malicious battery storage units. Additionally, we introduce an error tracking factor for leader agents to facilitate accurate demand tracking by the BESS. We establish convergence conditions, demonstrating that the system converges to a final value determined by the communication graph, initial values, and BESS parameters. The effectiveness of the proposed algorithm is validated through a numerical simulation, confirming its robustness and reliability in maintaining system performance under adverse conditions.

Keywords: Discrete Event Systems, Control Applications, Smart Structures
Abstract: We investigate the use of formal methods to detect and resolve feature interactions (FI) in cyber-physical systems (CPS). These systems are often made up of multiple components that may interact with each other in unexpected and unwanted ways, potentially creating a security risk. Specifically, we use supervisory control theory to examine FI in a smart home, an example of a CPS. With the rising adoption of smart home devices, mitigating these interaction threats at the modelling stage is important before they are installed in homes. We present an extended taxonomy of FI threats that affect such a CPS. We demonstrate how one such threat, chaotic device management (Codema), can be detected and resolved in a system comprised of a smart light bulb that supports two disjointed device management channels.