In this paper, we study the data-drivenH∞ optimal control problem for output synchronization of heterogeneous multi-agent systems via a computationally efficient reinforcement learning (RL) method. Unlike the existing RL control results which solve for the optimal controller through the vectorization and Kronecker product (VKP) operations where the large size of the Kronecker product increases computational complexity, a computationally efficient data-driven RL control method is studied in this paper. First, the optimal output synchronization problem is transformed into anH∞ optimal control problem by constructing an internal model-based distributed control protocol. Then, the corresponding algebraic Riccati equation for theH∞ optimal control is reformulated as a generalized Sylvester-transpose matrix equation (GSTME) at each iteration. To solve the GSTME, thematrixform of the conjugate gradient least squares algorithms that do not require the VKP operations is proposed based on the system dynamics and measurement data of multi-agent systems, respectively. Furthermore, the initial stabilizing gain required for policy iteration learning is proposed based on a data-based solution method. Finally, the feasibility of the designed algorithms is demonstrated through a numerical simulation, and its better computational efficiency compared to existing methods is shown.

Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (PTA) module, mode attention module and anchor-oriented decoder (AoD). It effectively performs early fusion on multi-source trajectory data from vehicles and road infrastructure, enabling the trajectories with high completeness and accuracy. The PTA module can efficiently capture potential interaction information among historical trajectories, and the mode attention module is proposed to enrich the diversity of predictions. Additionally, the decoder based on sparse anchors is designed to generate the final complete trajectories. Extensive experiments show that CoPAD achieves the state-of-the-art performance on the DAIR-V2X-Seq dataset, validating the effectiveness of the model in cooperative trajectory prediction in V2X scenarios.

This paper addresses the dynamic task assignment problem for multiple unmanned aerial vehicles (UAVs) operating under weak communication. Existing learning-based methods face two primary challenges: limited scene generalization and excessive reliance on communication resources. To address these issues, this paper proposes an event-triggered reinforcement learning algorithm based on graph neural networks. First, heterogeneous UAVs and tasks are embedded into a graph to construct a relationship model, which clearly represents the complex constraints between UAVs and tasks. This graph structure overcomes the limitations of existing methods in capturing constraint relationships. Second, the incorporation of heterogeneous graph neural networks and adaptive attention mechanisms enables effective learning of changes in adjacent node information, allowing the model to capture complex constraint relationships and environmental dynamics. This approach also addresses the lack of sensitivity to environmental changes observed in existing methods. Lastly, a dynamic synchronization mechanism is employed to update task assignment statuses in real time, preventing task conflicts and ensuring efficient allocation. Experimental results demonstrate that this method strikes a better balance between task assignment quality and efficiency. It performs well in untrained scenarios and significantly reduces communication resource consumption. These results highlight its promising potential for application in weak communication environments.

In this paper, a data-based containment control method is proposed for multi-agent systems where the system parameters of both leaders and followers areheterogeneousandunknown. Based on the collected data from the leader systems, the least square method is first utilized to identify the parameters of the leaders. Then, the data-based adaptive distributed compensator is designed for each follower to estimate the system parameters and the states of theheterogeneousleaders. Meanwhile, the collected data from follower systems is used to construct the local observer and establish the data-based linear matrix inequalities to determine the observer gains and feedback gains. On the basis of the collected data from leader and follower systems, the data-based adaptive solutions to the output regulation equations are provided. Furthermore, the data-based containment control protocols are designed to realize that the outputs of the followers converge to the convex hull formed by the unknown heterogeneous leaders. Finally, the effectiveness of the proposed data-based control scheme is illustrated by the simulation of the numerical example and the interconnecting RLC circuits.

This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the "explosion of complexity" of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

In this paper, a distributed T-S fuzzy security control strategy based on memory events is proposed for a class of multi-input-multi-output (MIMO) cyber-physical systems (CPSs) that subjected to replay attacks. This strategy can also address common uncertainties in practical systems, including communication latency, unmodeled dynamics, and external unknown disturbances. It is worth noting that the replay attack model in the paper is highly generalized. And the attack location, target, and frequency are all uncertain. Therefore, a suitable distributed memory event-based strategy (DMEBS) is designed. It can dynamically adjust the usage of historical data to optimize the release of sampled data, thereby determining when to update the control laws of each subsystem, ensuring system performance while greatly saving communication resources. In addition, the stability of the system is demonstrated by establishing a suitable Lyapunov-Krasovskii (L-K) functional, ensuring the elimination of the Zeno phenomenon. Finally, the effectiveness of the proposed method is validated through simulations conducted on two commonly encountered practical systems.

This article investigates the robust data-driven containment control problem for heterogeneous multiagent systems where the system dynamics of both the leaders and the followers are unknown. During the data collection phase, the follower systems are disturbed by unmeasurable but bounded noises. A data-based linear matrix inequality condition is first constructed from the noisy data to determinate the feasible feedback gain for each follower. Then, the distributed observer which is independent of the system matrix of the leaders is designed to estimate the convex hull of the leaders. Moreover, the approximate solution to the linear matrix equation for heterogeneous system is solved with bounded approximate error where the noisy data of each follower and the normal data of arbitrary leader is utilized. Based on the proposed feedback gain and observer as well as approximate solution, the robust data-driven control protocol is provided to guarantee the uniform boundedness of containment error. Finally, a numerical example and a multivehicle model are given to verify the effectiveness of the designed containment control protocol.

This paper advances a strategic paradigm for sensor power scheduling within Cyber- Physical Systems (CPSs) under adversarial scenarios, leveraging a Markov Stackelberg game combined with a Signal-to-Interference-plus-Noise Ratio (SINR) framework. A novel approach, which introduces reinforcement learning for the adaptive optimization of power distribution, is proposed to ascertain energy efficient sensor transmission tactics in the presence of jamming. Empirical simulations are given to validate the superiority of the proposed algorithm, underscoring its effectiveness in diminishing estimation inaccuracies and alleviating the adversarial influence.

This paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. The algorithm utilizes interpretable greedy trees as the foundational model and extends its applicability to handle multi-classification problems, thereby enhancing interpretability and detection accuracy. This methodology furnishes a more dependable and secure framework for discerning attack categories within the domain of network security. The experiment results show that the proposed algorithm achieves better balance between interpretability and identification precision.

In this article, a switching-event-based interval type-2 variable universe fuzzy tracking control strategy is proposed for a class of uncertain nonlinear systems. Remarkably, the nonlinearities and the large unknown uncertainties (including parametric and structural) can be allowed. Therefore, a novel switching-event-based mechanism is proposed. It not only determines when to update the control law, but also when to update the design parameters. At the same time, generous computing resources are saved. In addition, the interval type-2 fuzzy control technology is introduced to resist unknown uncertainties by identifying the controlled model online. Moreover, the Lyapunov function is designed to prove that the Zeno phenomenon does not occur and the closed-loop system is asymptotically stable. Finally, two practical simulations are given to demonstrate the effectiveness of the proposed method.

In this paper, a switching-event-based interval type2 (IT2) T-S variable direction fuzzy tracking control strategy is proposed for time-varying delay systems with unknown control directions. To deal with the time-varying delay problem, the T-S fuzzy logic system (TSFLS) is used to approximate the unknown nonlinear functions. A novel logic-based switching mechanism is proposed to handle the problem of unknown control directions. At the same time, in order to ensure the stability and tracking performance of the system, an auxiliary controller is designed. The proposed controller not only ensures the tracking performance well, but also reduces the communication burden between the controller and the actuator. In addition, through designing appropriate Lyapunov-Krasoviskii (L-K) functional for tracking error, the system is proved to be asymptotically stable and the Zeno phenomenon is excluded. Wherein the main parameters discussed are tracking error and time interval for eventtriggering. Finally, the effectiveness of the proposed method is verified by using both a mathematical model and an actual physical model.

In this article, an event-based hierarchical fuzzy prescribed performance control strategy is proposed for a class of underactuated systems with input dead zones. It is worth noting that the slope of the input dead zone is uncertain (time-varying/fuzzy). Therefore, a suitable hierarchical fuzzy logic system (HFLS) is designed to compensate for uncertain dead zones while significantly reducing the number of fuzzy rules. In addition, a dynamic event-based mechanism (DEBM) is proposed, which not only determines when to update the control law of the upper-level fuzzy system, but also when to update the parameters of the lower-level fuzzy controller. Moreover, this strategy can better tolerate interference while achieving the specified transient and steady-state performance of the system, and greatly save computing/communication resources. Furthermore, a Lyapunov function is designed to prove the stability of the system and eliminate the Zeno phenomenon. Finally, simulations are conducted using a quadcopter under two uncertain dead zone conditions to verify the effectiveness of the method.

Water level prediction is of great importance in alleviating the increasing water scarcity and preventing frequent floods. However, current water level prediction models do not consider the spatial and temporal features in water levels at monitoring stations. This study proposes a ladder water level prediction model for the Yangtze River based on transfer learning and Transformer to obtain more accurate predictions of water levels under tidal interactions. Our model utilizes the attention mechanism of the Transformer and incorporates spatial features and correlations of water level variations at the tidal limit of rivers. In addition, transfer learning is employed to explore and analyze the temporal characteristics of the wet season and dry season. Numerical experiments conducted at monitoring stations in the lower reach of the Yangtze River in China validate the effectiveness of our model. In 24-h water level prediction, our model achieves an average reduction of 52.14% in mean absolute error (MAE), 52.99% in root mean square error (RMSE), and an average increase of 5.70% in the pass rate within ±0.3 m compared to existing studies.

This paper studies the secure and safe control problem of connected and automated vehicles (CAVs) with false data injection (FDI) attacks. A secure and safe controller with a novel surrounding vehicles’ state estimator and an attack detector is proposed. The state estimation for surrounding vehicles is collectively processed by combining the deep neural network-based predictions with model-based estimations. Additionally, a weight in the loss function is proposed for more accurate predictions of vehicles that are closer to the ego vehicle. For attack detection, a novel scheme that utilizes control outcomes to train detection actions is proposed. Different from the objectives of existing detectors, the reward for the proposed detector is designed to encourage the CAV to fully utilize the observations. A reward setting and the decision preference of the ego vehicle are theoretically analyzed. The effectiveness of the proposed algorithm is validated in an open simulation environment.

This brief addresses the neural network (NN) approximation problem for uncertain nonlinear systems with time-varying parameters (that is, unknown nonlinear spatiotemporal systems). Due to the fact that the unknown spatiotemporal functions cannot be directly approximated by NNs, a so-called time-varying parameter extraction is given to separate time-varying parameters from uncertain nonlinear spatiotemporal functions. By using the supremum of Euler norm of the extracted time-varying parameters, the nonlinear spatiotemporal function is mapped to an unknown state-based boundary function, which can be approximated by NNs. Based on the time-varying parameter extraction, an adaptive neural tracking control law is designed for uncertain strict-feedback nonlinear spatiotemporal systems, which guarantees the convergence of the tracking error with a trajectory performance. The effectiveness of the designed method is verified by simulations.

This article focuses on the average-tracking control issue for heterogeneous linear multiagent systems via a fixed-time approach. The agents, with varied dynamics and state dimensions, are subject to external disturbances and each has a unique reference signal that cannot be accessed by the other agents. In this setting, each agent is provided a multiple reference signal state compensator in order to estimate the states of all reference signals. Furthermore, external disturbances are estimated using a fixed-time sliding mode disturbance observer. An anti-disturbance control protocol is proposed by combining the disturbance observer and the state compensator for agents to track the average value of reference signals within a fixed time, which is predetermined and independent of initial states. The efficiency of the suggested average-tracking control mechanism is shown by numerical experiments.

This paper studies how to renew and improve the expert rule and apply the rule update mechanism to optimize UAVs swarm adversarial strategy. A rule update approach is proposed, which uses the classifier subsystem to construct a training model based on expert experience, further trains the model through rule evaluation mechanisms and rule discovery subsystems to improve and enhance the rule base. A UAV swarm confrontation strategy model is further proposed based on the learning classifier system(LCS). Under the simulated aerial engagement environment of island capture between the red and blue sides, simulation experiments show that the model has robust combat effectiveness and offers significant practical utility for agent decision.

This article studies the detection of discontinuous false data-injection (FDI) attacks on cyber-physical systems (CPSs). Considering the unknown stochastic properties of the process noise and measurement noise, deep reinforcement learning is applied to designing an FDI attack detector. First, the discontinuous attack detection problem is modeled as a partially observable Markov decision process (POMDP) and a neural network is used to explore the POMDP. In the network, sliding observation windows which are composed of the offline fragment historical data are used as the input. An approach to designing the reward in POMDP is provided to ensure the precision of the detection when there are even some state recognition errors. Second, sufficient conditions on attack frequency and duration to guarantee the applicability of the detector and the expected estimation performance are further given. Finally, simulation examples illustrate the effectiveness of the attack detector.

This paper studies the distributed estimation problem of sensor networks where the noise parameters are unknown and some sensors cannot obtain the measurements. The expectation maximization (EM) algorithm integrating Kalman filtering algorithm, which is called the EM-KF algorithm, is adopted to sensor networks. Simulation examples are given to illustrate the effectiveness of the algorithm. By using LSTM network, and LSTM-KF algorithm is further proposed to improve the accuracy of EM-KF algorithm.

In this study, an adaptive controller for Cyber-Physical Systems(CPSs) subjected to cyberattacks is proposed to track a class of target system with unknown inputs and states. Firstly, an estimation strategy is proposed to adaptively estimate the unknown inputs and states of target on the basis of finite impulse response (FIR) filter. Secondly, due to the existence of cyberattacks in CPSs, an adaptive tracking control scheme with designed compensation signal is constructed to stabilize the system performance which might be damaged by attacks, where Lyapunov function related to adaptive estimated factor is adopted to prove that the CPSs are able to track the target under attacks. In the last part, a numerical experiment is implemented to demonstrate the validity of the theoretical results.

This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite time. It is assumed that the topology for the virtual leader and followers is directed. An additional condition of the so-called barrier function is designed to make all agents move within a limited area. A novel continuous finite-time path-following control law is first designed based on the barrier function and backstepping. Then a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as disturbances. The settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

This article addresses the spherical formation tracking control problem of nonlinear second-order vehicles moving in flowfields under both undirected networks and directed, strongly connected networks. Different from the previous adaptive estimate of the time-invariant parameters of flowfields, the flowfields under our consideration are spatial and absolutely unknown dynamics. Adaptive neural networks (ANNs) with the novel cooperative adaptive algorithms are proposed to approximate the flowfield acting on the channel of each vehicle's velocity (i.e., the mismatched flowfield) and the flowfield pushing the acceleration (i.e., the matched flowfield), respectively. For the purpose of avoiding the complex derivation derived from backstepping, the novel first-order filters are generated by dynamic surface based on barrier functions and relative positions of neighbors. The proposed control algorithms and adaptive upgrade law are fully distributed without using any global information of the graph. The uniform boundedness is analyzed in the Lyapunov sense. Simulation results are given to verify the theoretical analysis.

This article addresses the three-dimensional (3-D) coordinated control problem of directed networked aircraft-like vehicles, that is to track a set of given orbits on a sphere and achieve a lateral formation flight. Different from the case of Newton particles, a nonholonomic dynamics with unknown disturbances is considered. A novel method to decouple the spherical orbit tracking subsystem and the lateral formation flying subsystem is proposed. By overlooking the control of the vehicle’s surge velocity, a nonsmooth spherical orbit tracking algorithm is designed by backstepping. Without considering the spherical orbit tracking errors and using any global information of topologies, a distributed, nonsmooth formation protocol is designed. The input-to-state stability (ISS) theory is used to analyze the converge property of the interconnected system consisting of these two subsystems. Simulation results are given to verify the theoretical analysis.

In this article, an intelligent attacker is considered, which aims to prevent leader-following multiagent systems from achieving consensus. The attacker randomly injects denial-of-service (DoS) attacks to some communication channels in the network, which make the corresponding attacked edges disconnected. The minimum number of communication channels needed to be jammed by the attacker to guarantee the system fails to achieve the consensus is provided based on the Max-Flow Min-Cut lemma and an algorithm to generate the minimum attacked edges is proposed. Furthermore, a lower bound and an upper bound of the attack probability to destroy the consensusability of the system are provided, respectively. Finally, numerical simulations are given to illustrate the results.

This paper studies the consensus Kalman filtering algorithm with distributed attack detection for reducing the effects of false data injection attacks in wireless sensor networks. The FDI attacks are randomly injected into communication channels or sensors with certain probabilities, which undermines the accuracy of the transmission data and the accuracy of measurement data respectively. χ2detector is applied, and if the received data is determined to be attacked, it is omitted in consensus Kalman filtering. It is proved that if the FDI attack is randomly injected into communication channels, under the consensus Kalman filtering algorithm with adaptive weighting protocol and attack detection, the estimation errors of the sensor network can be bounded in probability. If the FDI attack is randomly injected into sensors, a probability condition on the attacks is given to guarantee the estimation errors of the sensor network bounded in mean square sense. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.

This paper studies the secure consensus control of multi-agent systems where some agents are injected into malicious attacks and compromised. We focus on the distributed detection and identification of attacks on agents and the realization of consensus of agents against attacks. Firstly, we design monitors that can perform precise distributed attack detection. Then, we propose a distributed attack identification algorithm by constructing distributed observers. After that, a cooperative controller is proposed which ensures the normal control of systems by means of isolating the agents which are attacked. Finally, we validate our results through simulation examples.

This paper studies the design of a distributed sensor scheduling policy for a sensor network, in which each dynamical target can only be measured by partial sensors due to the restriction of sensor resources while each sensor requires to monitor all targets. Consensus Kalman filtering algorithm and stochastic scheduling strategy are applied. Firstly, a necessary condition of the observation probabilities of the targets, which can guarantee the boundedness of the expected covariance of the network, is provided. Secondly, the marginal utility of the expected covariance with respect to the observation probability is proved. Then, an algorithm is proposed to compute the optimal probabilities, which requires less complex calculations. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.

This paper proposes a block backstepping trajectories tracking control scheme for a class of unmanned helicopters. The control objective is to make the position and yaw angle trajectories of the helicopter track the desired position and yaw angle trajectories. In order to design the controller, the helicopter system is divided into three subsystems at first. Then based on the block backstepping technique, four control inputs which constitute the helicopter controller are designed step by step in three subsystems. The asymptotic stability of the closed-loop helicopter trajectories tracking error system is verified based on Lyapunov stability analysis. Finally, numerical simulations demonstrate the effectiveness of the proposed block backstepping control scheme.

This paper considers the cooperative control problem of second-order agents formation tracking a set of given curves on spheres when each agent suffers an unknown spatiotem-poral flowfield. The flowfield under consideration is composed of three known base vectors and the unknown corresponding coefficients. A novel coordinated adaptive estimator is proposed to estimate the unknown flow coefficients based on the neighbor to neighbor information. Adaptive backstepping, the geometric extension design and consensus are combined to design the robust spherical formation tracking control law. The effectiveness of the analytical results is verified by numerical simulations.