This paper considers the safety-critical control problem for nonlinear second-order multi-agent systems with constraints of each agent and inter-agent ones. We overcome the challenge of the timevarying and position-dependent communication network with limited sensing range by introducing a truncated function for the smooth addition and deletion of links in the edge set, and design a distributed and locally Lipschitz-continuous safety-critical control law, composed of a nominal controller for the objectives such as consensus, formation, and position swapping, etc., and a safety controller, which only takes effect when some neighboring agent enters the custom-designed boundary set. Meanwhile, to rigorously verify the safety of the whole multi-agent system, a continuously differentiable control barrier function is proposed under a relaxed feasibility condition in the sense that it is imposed on each subsystem and only needed in the boundary area. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This article investigates the resilient control problem for a class of constrained uncertain nonlinear systems subject to impulsive false data injection (FDI) attacks. Different from those in the literature, impulsive FDI attacks are launched at discrete-time instants and cause transient jumps in the system state, thus rendering a nonlinear impulsive system. To overcome the difficulty of performance analysis caused by state jumps, we construct an impulsive constraint function and convert the system with full-state constraints into an unconstrained system. Then, a resilient controller based on the impulsive constraint function is proposed, and by further combining the average impulsive interval method, we provide a set of conditions to ensure the satisfaction of the state constraints and the stability of the closed-loop system.

The constrained output regulation problem for unknown linear discrete-time systems is solved in this work. This is achieved by first deriving sufficient conditions for the existence of regulator equation solution pairs, followed by the creation of a data-driven technique to obtain these solutions. Subsequently, a constrained observer is designed to provide precise state estimation while guaranteeing that the observer error confined within a predefined set. Finally, leveraging linear programming, we develop a data-driven output feedback controller that achieves reference tracking, enforces state and input constraints, and operates without explicit knowledge of the system dynamics.

Cooperative output regulation of singular multiagent systems has wide applications in unmanned systems, smart grids, and other fields. Prior study provided the pioneering work on the cooperative output regulation with deterministic models by introducing the novel distributed observers. However, when the mathematical models of follower agents are unknown, traditional model-based design methods may fail. This study extends the existing results to more general linear singular discrete-time multi-agent systems with unknown system matrices. To deal with the external disturbances, a coordinate transformation is employed to transfer the original tracking problem to a simplified stabilization problem. By using input and state data satisfying the full rank requirement, the feedback gain for each agent can be designed by solving data-based LMIs. Then, a data-driven distributed control scheme is proposed to solve the problem under some mild conditions. Finally, an illustrative example is provided to verify the correctness of the proposed scheme.

This paper proposes an integrated frequency identification and reinforcement learning based approach for active vibration control system to especially address the challenge from the frequency-switching excitation. Technically, we first propose a sparse autoencoder based method to identify frequencies, capable of directly extracting feature and achieving fast inference speeds, and then design an optimal frequencyspecific control policy with the aid of reinforcement learning technique. In particular, the Beta distribution is adopted in the construction of FxLMS-based reinforcement learning environment and used to model the action in Markov decision process due to its bounded property, which not only results in a stable training process, but also accelerates the convergence of policy optimization. Our integrated method requires no manual parameter tuning when the frequency of the excitation dramatically changes, and effectively minimizes the total error energy under different frequencies of the exciter, demonstrated in the experiment of 8-channel AVC system.

This article considers the robust safety-critical control problem of an uncertain second-order nonlinear system subject to denial-of-service (DoS) attacks on the sensor-controller and controller-actuator channels. In order to handle the safety constraint described by a general nonlinear function, we first propose a governor function, regarded as a generalized barrier function, with additional properties. Then, a safety-critical control strategy is designed to guarantee the safety and stability of the closed-loop system. Finally, a combined time- and event-triggered sampling mechanism is further introduced to detect the randomly occurring DoS, and the latest sampled information is used to update the safety-critical control strategy once the signal can be received through the sensor-controller channel. Our sampled-data safety-critical control strategy effectively addresses both the safety-critical control and security control problems of nonlinear systems with parametric uncertainty.

This paper investigates the resilient and safety control problem for a class of uncertain nonlinear systems subject to impulsive false data injection (FDI) attacks. Different from those in the literature, impulsive FDI attacks are launched at discrete-time instants and cause transient jumps in the system state, thus rendering a nonlinear impulsive system. To overcome the difficulty of performance analysis caused by state jumps, we construct a hybrid governor function and convert the system with full-state constraints into an unconstrained system. Then a resilient controller based on the hybrid governor function is proposed, and by further combining the average impulsive interval method, we provide a set of conditions to ensure the safety and the stability of the closed-loop system.

This paper considers a two-player linear quadratic differential hypergame where Player 2 holds misperception about the objective of Player 1. Such a problem arises in practical situations such as mixed human-autonomous driving, where the autonomous vehicle may misinterpret human driving intentions. Such misperception typically precludes exact hyper-Nash equilibria and brings technical challenge in the design of the optimal strategy for Player 2 due to obscured Nashrelevant parameters under state-only observations. To address the difficulty, we develop an inverse learning-based method that reconstructs the Nash-relevant closed-loop dynamics induced by the opponent's strategy from finite state trajectories. Based on the recovered game structure, a Riccati flow-based strategy update law is then designed, which drives the proposed strategy toward the exact Nash equilibrium of the underlying game. The effectiveness of the proposed strategy is validated by a car-following case with misperception between an autonomous vehicle and a human driver.

This paper considers the constrained robust output regulation problem for unknown linear discrete-time systems, specialized in addressing the primary challenges of state-input constraints and unmeasurable disturbances by the data-driven control technique based on the internal model principle and $\lambda$-contractive set analysis. As a result, the methodological approach is different from existing ones in the sense that an equivalent system is first reconstructed to eliminate the need of direct access to the disturbance signal and collect sufficient data by designing an input generator. An improved internal model is then proposed with the additional constrained property based on the solution to the regulator equations, obtained by a one-step data-driven method. The final data-based constrained control law is derived with its gain computed via linear programming.

This article presents a robust event-triggered voltage regulation strategy for DC-DC converters that address both state constraints and disturbances. Conventional barrier Lyapunov functions (BLFs) may face limitations in handling transient constraint violations caused by external disturbances or abrupt reference changes and can lead to potential control singularities. To overcome this, we propose a novel robust BLF (RBLF) integrated with a dynamic safety boundary (DSB). The DSB adaptively adjusts based on the tracking error, enabling the RBLF-based controller to manage large state deviations and robustly guide the system back into the specified safe set with bounded control inputs. This approach effectively prevents singularities and offers resilient safety. To enhance resource efficiency, an RBLF-based event-triggered controller is developed within the backstepping framework. This controller specifically addresses the discontinuity and nondifferentiability of the virtual control law under event-triggered sampling by leveraging its partial derivatives and sampled states. Theoretical analysis confirms closed-loop signal boundedness, uniform ultimate boundedness (UUB) of the system, and Zeno-free operation. Experimental results demonstrate stable voltage regulation, robust performance under multiple disturbances, and a substantial reduction in control updates.

This article considers the output regulation problem for an unknown discrete-time system subject to the random combination of denial-of-service, replay, and deception attacks on both sensor-controller and controller-actuator channels. We propose a learning-based receding-horizon control with historical output signals. It offers two advantages over state and output feedback regulators in the sense that it requires neither exact knowledge of system dynamics nor a direct measurement of external disturbance on one hand, and on the other hand, it can counteract the adverse impact of hybrid attacks on the executive capability of the actuator, regardless of the seriously tampered data on the sensor-controller channel. To overcome technical difficulties from hybrid attacks on both channels, we generalize the Markov-parameter-based time-series control method to generate a data packet containing the current and future control inputs, which are further compromised on the controller-actuator channel. Thus, a recovery procedure is additionally designed to solve the model-free output regulation problem by distinguishing the undamaged predicted inputs based on the proposed hybrid attack detection procedure.

We propose a closed-form solution to the landmark simultaneous localization and mapping (landmarkSLAM) problem. The core idea is to extend the recent advancement in the generalized Procrustes analysis (GPA) research by incorporating an affine-relaxed odometry term. We show that the resulting affine relaxed landmark-SLAM formulation, termed affine-SLAM, can be solved globally in closed-form. Through numerical experiments, we demonstrate that the affine-SLAM solution is rather close to the optimal solution of the standard nonlinear least squares (NLS) optimization, and thus can be used either as a stand-alone approximate solution or as a high-quality initialization for NLS solvers.

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

This paper proposes a trajectory generation method utilizing the Bézier curve for mobile robots and employs it in the cooperative transportation task. In contrast to the approach of predefining spatial features and then allocating time accordingly, the proposed method expresses the spatial characteristics through duration on the basis of ensuring the continuity of dynamics, and then concurrent planning of spatial and temporal characteristics, thereby leading to a trade-off between time efficiency and optimizing spatial features. Furthermore, the proposed method is combined with formation control and applied to a cooperative transportation task. The effectiveness and practicability of the proposed trajectory generation method are verified through both simulation and experiment.

This paper addresses the distributed vibration reduction problem in multichannel active vibration control (AVC) systems, specifically under scenarios involving multitasking and actuator coupling. The objective is to develop an advanced algorithm that enhances the performance and adaptability of AVC systems in the complex environment. A distinctive diffusion Filtered-x Least Mean Square (FxLMS) algorithm is proposed in the sense that it integrates adaptive fusion matrix and the projected gradient method, capable of dynamically adjusting the exchange of nodal information and applicable to asymmetric AVC systems, different from the metropolis method based on time-invariant fusion matrices. Firstly, by extending the Banach fixed point theorem, we derive the convergence conditions for the FxLMS algorithm. It is technically challenging to prove the contraction of the nonlinear mappings and conduct the bias analysis with the introduction of the time-varying fusion matrix. Secondly, simulations for a multi-channel AVC system with 147 nodes are conducted. The simulation results show that the proposed algorithm demonstrates the fast convergence speed in the initial stage, reducing MSE by over 30% within 1000 iterations and achieving a final vibration reduction of 61.8% . Thirdly, an experiment on a vibration isolation platform with a four-actuator setup is conducted. The experimental results indicate that the proposed algorithm achieves an 85.6% reduction in the mean square error (MSE). This paper proposes a diffusion FxLMS algorithm that utilizes an adaptive fusion matrix to address the challenges of vibration reduction in multi-task and multi-channel coupling scenarios. The convergence conditions of the algorithm are provided in the performance analysis section. Simulations and experiments verify the effectiveness of the algorithm in addressing multichannel vibration reduction problems.

This paper proposes a spatial-temporal trajectory planning method for multi-robot systems subject to both motion and safety constraints. By decomposing the planning process into three sequential subproblems: path planning, individual time optimisation, and collision coordination, the proposed method not only reduces the complexity of the overall planning task but also facilitates the incorporation of diverse constraints and optimisation objectives. Specifically, the path planning subproblem generates smooth, length-minimised paths while ensuring adherence to static collision avoidance, boundary position and nonholonomic constraints. Building on these optimised paths, the individual time optimisation subproblem aims to minimise trajectory duration while adhering to dynamic constraints. Then, collision coordination subproblem accounts for collision avoidance constraints between moving robots with minimum makespan. The proposed method is applied to warehouse scenarios and the results indicate that it outperforms baseline methods in terms of planning success rate, computational time, as well as overall makespan.

This article considers the security control problem of a safety-critical system, described by a general nonlinear uncertain system with constraints for collision avoidance and internal dynamic limitations. We design an integrated security and safety-critical control law to prevent the system from operating in the unsafe mode under denial-of-service (DoS) attacks in the signal transmission channels. By combining the internal model principle and the time-and event-triggered sampling mechanism for DoS detection, an improved dynamic compensator is first proposed and converts the safety tracking problem into the attractivity problem of the constrained error system. Then a security control is constructed for the error system by integrating the safety-critical controller in the barrier function-based framework. Finally, we prove that the integrated control design can guarantee the security, safety, and stability of the closed-loop system.

This paper considers the prescribed performance control of unknown multi-input multi-output nonlinear systems with actuator faults. By combining a special funnel function and a barrier function, based on a new coordinate transformation, a low-complexity control approach is proposed not only to guarantee the full-state errors converge into a desired steady-state error boundary in a predefined time, but also to tolerate time-varying actuator faults and achieve the asymptotic tracking, as opposed to the semi-global bounded error tracking results. Our design has a simple structure in the sense that repeatedly taking derivatives of virtual controllers is avoided, and by introducing a time-varying function for the gain design, function approximation methods are also unnecessary. It is applied to the two-degree-of-freedom helicopter system, which demonstrates a good performance in the transient and steady-state stages.

This paper investigates the resilient control problem for a class of constrained uncertain nonlinear systems under impulsive false data injection (FDI) attacks. Differing from most existing literature, this attack occurs in discrete time. To address the challenges caused by impulsive jumps, we developed an impulsive constraint function to transform the constrained system into an unconstrained one. Moreover, using the average impulsive interval approach, a series of novel conditions are derived such that the corresponding performance of the closed-loop is satisfied.

This paper considers the output regulation problem for unknown linear discrete-time systems subject to hybrid cyber attacks on both sensor-controller and controller-actuator channels. A distinctive receding-horizon control is proposed to not only predict the future input based on the tampered data collected through the sensor-controller channel, but also recover the compromised input at the actuator end, capable of simultaneously mitigating hybrid attacks and achieving the asymptotic tracking of a dynamic reference. It is essentially a model-free optimal approach in the sense that the control parameters are designed under the condition that the system matrices are unknown and solved through the constrained optimization problem by combining the output regulation theory and reinforcement learning technique.