The Nash game-basedH2/H∞control method not only suppresses the impact of external disturbances but also minimizes anH2cost functional under disturbance inputs, making it more advantageous than usingH2orH∞control alone. However, its implementation poses significant challenges, as it requires solving complex, coupled generalized algebraic Riccati equations (GAREs). To address these challenges, data-driven reinforcement learning (RL) methods provide an effective and practical solution. This paper presents a RL algorithm for addressing the infinite-horizonH2/H∞control problem of a class of stochastic discrete-time systems. The proposed algorithm can learn the Nash game-basedH2/H∞control policy for the system even when its parameters are unknown. Furthermore, it investigates the impact of detection noise and analyzes the algorithm’s convergence, demonstrating that the control policy becomes admissible after a finite number of iterations. The algorithm also supports multi-objective control problems within stochastic frameworks. Finally, the algorithm is applied to the F-16 aircraft autopilot with multiplicative noise.

This paper investigates resilient tracking control for multi-agent systems subject to multi-channel asynchronous denial-of-service (DoS) attacks within a directed graph. We consider both homogeneous dynamics and uncertain heterogeneous dynamics in our analysis. We first propose a distributed resilient tracking control strategy against multi-channel asynchronous DoS attacks for homogeneous systems. The concept of equivalent decay-rates across different attacked channels is introduced to derive sufficient conditions for secure tracking control. Building upon this foundation, we extend the method to address tracking control under uncertain heterogeneous dynamics and constrained communication resources. To this end, we propose an event-triggered resilient control strategy based on equivalent decayrate analysis. The strategy reduces communication overhead by adopting demand-driven scheduling and enhances resilience through a decay-rate-guided feedback mechanism. Our proposed algorithms both mitigate asynchronous DoS attacks by constraining attack surfaces and ensure secure tracking under resource constraints. Finally, numerical examples are provided to verify our theoretical analysis.

This paper investigated the stability andH∞performance of discrete-time Markov jump systems (MJSs) with complex transition probabilities (C-TPs) and actuator saturation. The asynchronous phenomenon between the controller mode and system mode transmission, along with actuator saturation, complicates the control design significantly. To overcome this issue, an event-triggered asynchronous controller embedded with a Hidden Markov Model (HMM) was proposed. First, using Lyapunov functional technique, sufficient conditions for the stochastic stability of the closed-loop MJSs under a givenH∞performance index were established. Second, we consider that C-TPs exist in two processes of the HMM, enhancing the practicality of the theoretical results. Furthermore, two slack matrices were introduced, and matrix augmentation method was employed to address the coupling between variables, leading to the derivation of the required controller gains. Finally, Two examples were provided as application examples to validate the effectiveness of the proposed control method.

The distributed flexible job shop scheduling problem (DFJSP) has emerged as a critical challenge in the field of scheduling optimization due to its intricate resource allocation and the demand for production–logistics collaboration across multiple factories. However, most existing studies related to DFJSP only focus on the production and transportation process of jobs within a single factory, while neglecting the cross-factory logistics and the heterogeneous characteristics of transportation resources. Therefore, this article first investigates the distributed flexible job shop scheduling problem with heterogeneous transportation (DFJSPHT) resource constraints and proposes an end-to-end deep reinforcement learning (DRL) scheduling method to minimize the makespan. An innovative heterogeneous disjunctive graph model is constructed to uniformly represent the states of factories, machines, operations, and transportation resources in DFJSPHT, and the scheduling process is modeled as a Markov decision process (MDP). Next, a resource release strategy is developed to enhance the efficiency of transportation resources. To enhance the feature expression ability of the model, a graph neural network (GNN) is employed to capture the problem characteristics, and the policy network is trained using the proximal policy optimization. Comparative experiments are conducted on synthetic and benchmark instances demonstrate that the proposed method outperforms the classical priority scheduling rules and two popular DRL-based scheduling methods in solving DFJSPHT, with performance improvements exceeding 10% in most instances.

In this paper, a Zeroing Neural Network (ZNN)-based control framework is proposed for finite-time visual servoing of robotic manipulators, without requiring camera calibration or kinematic modeling. To address the challenge of the unknown robot-camera interaction, a data-driven Jacobian estimator is introduced, enabling real-time mapping without offline training or analytical derivation. A finite-time noise-rejection ZNN (FTNRZNN) controller is developed to ensure robust and fast joint-level control under measurement noise. The continuous-time scheme is further discretized for digital implementation. Rigorous Lyapunov analysis guarantees finite-time convergence. Simulations and real-world experiments validate the effectiveness of the method in both regulation and trajectory tracking, demonstrating strong adaptability to unstructured environments.

In this paper, we propose a novel generalized discrete-time variable gain active disturbance rejection control (DTVGADRC) method for then-th order discrete-time nonlinear systems. The error-driven generalized DTVGADRC can dynamically improve the control performances, including generalized discrete-time variable gain tracking differentiator (DTVGTD), generalized discrete-time variable gain extended state observer (DTVGESO), and generalized discrete-time variable gain controller (DTVGC). Furthermore, the stability analysis of generalized DTVGADRC is performed, and the parameters in the variable gain functions are determined by the theoretical analysis. Finally, the generalized DTVGADRC method is applied to parallel teleoperated manipulators, and the experiment results are presented to illustrate effectiveness of the proposed method.

This article investigates the leader-following consensus problem of discrete-time nonlinear multiagent systems (MASs) over fading channels. With the consideration of the transmission among followers maybe affected by the fading networks, the nonidentical fading channels model is constructed. To reduce the transmission network burden, the dynamical event-triggered mechanism (DETM) is developed. Different from most of existing event-triggered strategies, the threshold parameter in the developed dynamical event-triggering condition is dynamically adjusted according to a dynamic rule. Based on the DETM, a distributed consensus control protocol is designed under fading channels. Then, sufficient criteria are provided to ensure that MASs can achieve the leaser-following consensus, and satisfy the$H_{\infty}$performance index in the presence of fading channels. The desired controller parameters can be derived in terms of solutions of matrix inequalities that are lightly solvable. In the end, simulation results show that the designed dynamical event transmission policy is capable of diminishing communication burden more promptly and effectively than some existing ones.

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.

This paper investigates consensus of networked fractional-order systems over directed graph, with the networked double-integrator systems as its special case. An intermittent sampled position measurement distributed algorithm is proposed, which reduces the operation time and the update rates of controllers, and effectively responds to the circumstances if the information of agents’ velocity and current position cannot be measured. In order to reach consensus, some necessary and sufficient conditions with respect to the fractional order, communication width, coupling gains, and networked structure, are derived by applying the fractional Laplace transform and stability theory. Note that consensus can be reached only if some inequalities are fulfilled, which serves as a guide for selecting the appropriate communication width and sampling period to reach consensus. Finally, some simulation examples are illustrated to verify the theoretical results.

The broad learning system (BLS) of intelligent vehicle in different target environments is studied in this article. First, this article provides with the target recognition image data to be trained and detected through the automated guided vehicle (AGV) mobile platform, which can grab the recognition image of different angles and backgrounds. In order to avoid the data generalization phenomenon, the dataset can be expanded by the data normalization and data enhancement. Second, the data are input into the shared convolution layer to extract the feature image and maintain the image. The parameters of image height, width, and channel number are invariable, and the new feature image is obtained by further extraction. Furthermore, the region proposal network (RPN) prefiltering algorithm based on hierarchical clustering is used to filter the objects in the candidate box to determine the region image corresponding to the feature image. Then, the feature images of different sizes input into region of interest (ROI) pooling are used to keep the size of the image in the ROI consistent. Finally, the normalized image is input into the classifier module to obtain the category of the target recognition image to be detected. Through the simulation experiments of different groups, it can be seen that the target recognition system proposed in this design can not only accurately detect the objects but also stably recognize the objects in different environments. The target recognition accuracy for the optimized system is about 95%.

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic scenarios, so the application of DRL in autonomous driving presents a broad application prospect. In this article, an end-to-end autonomous driving policy learning method based on DRL is proposed. On the basis of proximal policy optimization (PPO), we combine a curiosity-driven method called recurrent neural network (RNN) to generate an intrinsic reward signal to encounter the agent to explore its environment, which improves the efficiency of exploration. We introduce an auxiliary critic network on the original actor-critic framework and choose the lower estimate which is predicted by the dual critic network when the network update to avoid the overestimation bias. We test our method on the lane- keeping task and overtaking task in the open racing car simulator (TORCS) driving simulator and compare with other DRL methods, experimental results show that our proposed method can improve the training efficiency and control performance in driving tasks.

The connectivity is an essential property of the connections between the nodes in networks. The efficient determination algorithm for the connectivity of complex directed networks is an important research direction in graph theory. Aiming at the determination problem of the strong connectivity of directed networks, we propose an improved algorithm over the Warshall algorithm, which extends the research object to complex directed networks and has only the half time complexity of that of the latter. In addition, this article also takes the lead in research on the determination algorithm for the unilateral connectivity of complex directed networks, and on this basis, we propose an algorithm to efficiently determine the unilateral connectivity. Finally, the above two algorithms are integrated into a unified and efficient algorithm with the time complexity of$\mathcal {O}({n}^{3}+4.5{n}^{2})$. This algorithm can determine not only the strong connectivity but also the unilateral connectivity of complex directed networks.

This paper develops two novel containment control protocols for networked fractional-order systems with sampled position data. In the first scenario, time delay is not considered and the protocol is about the last and causal past sampled position data, while in the second scenario, time delay is considered and the protocol is about the last and time delay sampled position data. Successively, some necessary and sufficient criteria are derived for two classes of protocols. It is interesting to find that containment control of networked fractional-order systems cannot be reached under the developed control protocols without the help of the causal past sampled position data or time delay. Lastly, the effectiveness of theoretical results is verified through a simulation example.

This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS)-denied and GPS-challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point-cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3-D lidar and the inertial measurement unit (IMU). Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3-D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.

This paper concentrates on the linear least mean square (LLMS) filtered and smoothed estimators for networked linear stochastic systems. Multiple packet losses, Markovian communication constraints, and superposed process noise are considered simultaneously. In order to reduce the channel load during communication, at every step, just one transmission node is permitted to send data packets. Hence, a Markovian communication protocol is utilized to arrange the packets of these transmission nodes. Moreover, multiple data packet dropouts occur during transmission due to an imperfect communication channel. Therefore, the global observation information cannot be obtained by the state estimator. The real state of Markov chain is assumed to be unknown to the estimator except the transition probability matrix. By means of the innovation analysis approach and orthogonal projection principle, we design Kalman-like estimators in a recursive form. Finally, through simulation experiments, we verify the effectiveness and superiority of the designed algorithm.

The problem of reliable controller design for two-dimensional (2-D) systems subject to extended dissipativity is investigated in this paper. Considering the wide usage of Roesser state-space model, a discrete-time Roesser model is introduced to describe 2-D systems. To reinforce the reliability of the system under consideration, actuator-failure model which may severely degrade performance, or even destabilize the system is introduced. Finally, the sufficient condition for the existence of reliable controller is presented to guarantee the mean square asymptotic stability and 2-D extended dissipativity of the closed-loop system under admissible actuator failures. The gains of 2-D controller are derived by means of the convex optimization method. A simulation result is exploited to verify the effectiveness and merits of the theoretical findings.

In this paper, a novel control strategy for synchronization of heterogeneous network systems in industrial applications is proposed. Nonidentical nodes are adopted to describe the different industrial processes. The target trajectory is the output of an autonomous linear time-invariant system. The designed controller for each nonidentical node is distributed and calculated on the local information. Each distributed controller composes by the reference generator (RG) and the regulator (RE), where RG can copy the dynamics of the target trajectory and RE can ensure the synchronization. Limited to communication constraints, the time-varying sampled-data control strategy is utilized to reduce the updated frequency of the controller and communication burden of the network. The upper bound of the sampling instants is calculated by the theory of small gain theorem and the theory of integral quadratic constraints. Finally, a practical numerical example is presented to illustrate the effectiveness of the distributed controller design strategy.

This paper proposes a novel control strategy for the synchronization of network systems. The designed distributed controllers adopt the communication channels to exchange information. The designed controller for each heterogeneous node includes two parts: 1) the reference generator (RG) to copy the dynamics of the leader and 2) adaptive regulator (AR) to achieve synchronization purpose. Under the action of sampled-data control law, outputs of all RGs converge to the output of the leader. The closed-loop system of the leader and all RGs are be equivalently written as the interaction of an operator and a linear time-invariant system. The small gain theorem is utilized to calculate the upper bound of the sampling intervals. Furthermore, the integral quadratic constraints can provide the passivity-type property of the operator and give the less conservative results. Meanwhile, the AR can ensure that nonidentical node tracks its exosystem. Thus, all nonidentical nodes and the leader achieve output synchronization. The proposed control strategy is similar to the separation principle, which includes two steps. Finally, a numerical example is given to demonstrated the effectiveness of the proposed control strategy.

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter σ , and asynchronous rate are revealed.

A novel control strategy for synchronization control for network systems with communication constraints is presented in this paper. The dynamics of nodes in the network are nonidentical. The communication topology of network is weakly connected with communication constraints. The designed distributed controller for each node has two parts: reference generator (RG) and regulator. All RGs adopt the communication channels to exchange local information and track the target trajectory. Meanwhile, regulator can ensure that nonidentical node achieves synchronization with its RG. In order to reduce the communication frequency between node and its regulator, a sampled-date control strategy is utilized. The upper bound of the aperiodic sampling instants is calculated through the small-gain theorem, where the closed-loop system is equivalently formulated as the feedback interconnection of a linear time-invariant system and an integral sampled-data operator. Finally, some simulation results are given to demonstrate the effectiveness of the controller design strategy.

This paper studies the synchronization issue of time-varying Markovian jump neural networks (NNs). The denial-of-service (DoS) attack is considered in the communication channel connecting master NNs and slave NNs. An observer is designed based on the measurements of master NNs transmitted over this unreliable channel to estimate their states. The deception attack is used to destroy the controller by changing the sign of the control signal. Then, the mixed-type attacks are expressed uniformly, and a synchronization error system is established using this function. A finite-horizon l2- l∞performance is proposed, and sufficient conditions are derived to ensure that the synchronization error system satisfies this performance. The controllers are then obtained by a recursive linear matrix inequality algorithm. At last, a simulation result to show the feasibility of the developed results is given.

The problem of state-feedback reliable control against sensor failures for Markov jump nonlinear systems taking into account the mixed time delays is investigated in this paper. To describe the phenomenon of imperfect transmission between the system and the controller, a mode-dependent stochastic measurement fading model is adopted. By introducing two mutually independent and mode-dependent fading channel coefficients, the undulation of communication workload caused by the change of system mode can be accurately described. Also, the mixed time delays including the discrete and the infinite distributed delays are considered. Our goal is to design a reliable controller, which can guarantee the passivity of the closed-loop control system even when some sensors experience faults or failures. To realize this reliable controller, we introduce an additional matrix and utilize some inequality techniques. By using Lyapunov function technique, the gain of the designed controller can be solved. The merits and effectiveness of the developed design scheme are verified by a simulation example.

In this paper, we investigate the synchronization for heterogeneous network subject to event-triggering communication. The designed controller for each node includes reference generator (RG) and regulator. The predicted value of relative information between intermittent communication can significantly reduce the transmitted information. Based on the event triggering strategy and time-dependent threshold, all RGs can exponentially track the target trajectory. Then by the action of regulator, each node synchronizes with its RG. Meanwhile, a positive lower bound is obtained for the interevent intervals. Numerical example is given to demonstrate the effectiveness of the proposed event triggering strategy.

This paper is concerned with the synchronization of complex networks subject to partial couplings and Takagi-Sugeno (T-S) fuzzy nodes, by adopting the aperiodic sampled-data strategy. A decoupling method is utilized to handle the partial couplings among connected nodes, which enables us to investigate each channel of complex networks independently. On the basis of the input-delay approach, the hybrid system about each channel is reformulated to a continuous time-varying delay system. Then, the free-weighting matrix approach and a novel continuous Lyapunov functional are adopted to capture the information of sampling pattern. Sufficient conditions are obtained to ensure that the complex networks achieves synchronization with the target node. Furthermore, the proposed strategy is extended to more general case, in which there exist constant transmission time delays in the local interactions of connected nodes. Based on Wirtinger's inequality, a simplified and efficient synchronization strategy is proposed. Moreover, the corresponding optimization problem about the maximal sampling interval upper bound is addressed as well. Finally, the communication frequency reduction potential of the proposed synchronization strategy is well demonstrated via a numerical example.

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

A novel control strategy for the output synchronization and L2analysis of network systems is presented in this paper. Nodes in the heterogeneous network have nonidentical dynamics. The designed distributed controller contains reference generator to copy the dynamics of the target trajectory and robust regulator (RG) to achieve synchronization purpose. All RGs adopt the network communication channels to exchange system information. The distributed control law is calculated on static output feedback and ensure outputs of RGs converges to the target trajectory. Meanwhile, each nonidentical node tracks its RG with L2performance index. The controller design strategy is presented in the obtained necessary and sufficient conditions. Finally, the effectiveness of the controller design strategy is demonstrated by a numerical example.

In this paper, the problem of event-based synchronization of heterogeneous complex networks is investigated. Specifically, the influence of transmission delays on event-based synchronization is considered. The designed distributed controller for each nonidentical node in heterogeneous network includes reference generator (RG) and robust regulator. Event-based communication protocol is utilized to ensure the synchronization among the identical RGs and target leader. Meanwhile, the interevent intervals are lower bounded by a positive constant. Furthermore, the proposed high-gain output feedback and adaptive control law guarantee that all nonidentical nodes converge to their RGs. Based on these two steps, which are coincided with the separation principle, nonidentical nodes can track the target leader. In addition, theoretical results are demonstrated by a numerical example.

In this paper, the problem of output synchronization is investigated for the heterogeneous network with an uncertain leader. It is assumed that parameter perturbations influence the nonidentical linear agents, whose outputs are controlled to track the output of an uncertain leader. Based on the hierarchical structure of the communication graph, a novel control scheme is proposed to guarantee the output synchronization. As there exist parameter uncertainties in the models of the agents, the internal model principle is used to gain robustness versus plant parameter uncertainties. Furthermore, as the precise model of the leader is also not available, the adaptive control principle is adopted to tune the parameters in the local controllers. The developed new technique is able to simultaneously handle uncertainties in the follower parameters as well as the leader parameters. The agents in the upper layers will be treated as the exosystems of the agents in the lower layers. The local controllers are constructed in a sequential order. It is shown that the output synchronization can be achieved globally asymptotically and locally exponentially. Finally, a simulation example is given to illustrate the effectiveness and potential of the theoretic results obtained.

This paper is concerned with the consensus of multiagent systems with nonlinear dynamics through the use of aperiodic sampled-data controllers, which are more flexible than classical periodic sampled-data controllers. By input delay approach, the resulting sampled-data system is reformulated as a continuous system with time-varying delay in the control input. A continuous Lyapunov functional, which captures the information on sampling pattern, together with the free-weighting matrix method, is then used to establish a sufficient condition for consensusability. For a more general case that the sampled-data controllers are subject to constant input delays, a novel discontinuous Lyapunov functional is introduced on the basis of the vector extension of Wirtinger's inequality. This functional can lead to simplified and efficient stability conditions for computation and optimization. Further results on the estimate of maximal allowable sampling interval upper bound is given as well. Numerical example is provided to show the effectiveness and merits of the proposed protocol.