A coordinated control challenge is addressed in 6-degree-of-freedom (6-DOF) fixed-wing multiple autonomous aerial vehicle (multi-AAV) systems under communication and state constraints. The primary obstacle in achieving this goal arises from managing frequent information interactions and the assurance that UAV states converge within prescribed bounds. On the one hand, a novel dynamic self-triggering mechanism is effectively proposed. Unlike current state-of-the-art approaches, the proposed dynamic self-triggering communication mechanism features a larger triggering threshold and eliminates the need for continuous monitoring of system state information. This reduces the demand on system communication and sensor resources. On the other hand, a new time-varying constraint bounded function is introduced to effectively relax restrictions on the initial system state. Then, the coordinated translational/rotational controllers are designed to ensure minimal consensus tracking error. Semi-physical simulations highlight the effectiveness of the proposed control algorithm. Note to Practitioners—In actual environment, the multi-UAVs flight always requires inter-communication to ensure the stable performance of the entire formation. However, period-based communication leads to a waste of communication resources. The event-triggering communication mechanism lowers the communication frequency of UAVs, thereby reducing energy consumption. Nevertheless, most existing control results on event-triggered communication overlook the fact that continuous monitoring of state information still causes unnecessary energy consumption. To further investigate the problem, a dynamic self-triggering mechanism is proposed in this study, which can determine the subsequent triggered moment based on the state information of the current triggered moment. In addition, the state of UAVs due to safety and physical constraints ought to be constrained. Therefore, a prescribed-time constrained control strategy is proposed, which not only improves the transient performance (e.g. small overshoot and fast adjustment time), but also ensures that the UAV state converges within a given constraint bound.

With the increasing popularity of autonomous driving systems and their applications in complex transportation scenarios, collaborative perception among multiple intelligent agents has become an important research direction. Existing single-agent multimodal fusion approaches are limited by their inability to leverage additional sensory data from nearby agents. In this article, we present the collaborative multimodal fusion network (CMMFNet) for distributed perception in multiagent systems. CMMFNet first extracts modality-specific features from LiDAR point clouds and camera images for each agent using dual-stream neural networks. To overcome the ambiguity in-depth prediction, we introduce a collaborative depth supervision module that projects dense fused point clouds onto image planes to generate more accurate depth ground truths. We then present modality-aware fusion strategies to aggregate homogeneous features across agents while preserving their distinctive properties. To align heterogeneous LiDAR and camera features, we introduce a modality consistency learning method. Finally, a transformer-based fusion module dynamically captures cross-modal correlations to produce a unified representation. Comprehensive evaluations on two extensive multiagent perception datasets, OPV2V and V2XSet, affirm the superiority of CMMFNet in detection performance, establishing a new benchmark in the field.

This article proposes a novel hierarchical reinforcement learning (HRL) algorithm for unmanned aerial vehicle pursuit-evasion (UAV-PE) game systems with an alternative delay update (ADU) method. In the proposed algorithm, the approximate solutions of the UAV-PE game problem are derived from a hierarchical learning process, which relies on a zero-sum game process of kinematics and a corresponding optimal process of dynamics. In this case, deep neural networks (NNs) are used to approximate the policy and value functions of UAV-PE game systems in kinematics and dynamics level. Furthermore, the ADU method is adopted to improve the training efficiency of deep NN by fixing one player of the UAV-PE game systems to form a stable environment. The goal of this article is to develop an HRL algorithm with an ADU method for obtaining approximate Nash equilibrium (NE) solutions of the considered UAV-PE game systems which are subjected to the coupling of kinematics and dynamics. Subsequently, sufficient conditions are provided for analyzing the convergence and optimality of the proposed HRL algorithm. Moreover, the inequalities of overload are obtained to guarantee that the state of dynamics tracks with the control input of kinematics in UAV-PE game systems. Finally, simulation examples are provided to demonstrate the feasibility and usefulness of the proposed HRL algorithm and ADU method.

In this article, the leader-following consensus problem is investigated for a class of nonlinear multiagent systems with transmission outliers. First, through the relative output information and observer states of neighbor agents, a distributed stubborn reduced-order observer is proposed to estimate the leader-following consensus error which is affected by abnormal and isolated transmission outliers. Moreover, considering that the leader agent in practice is a controllable plant, another distributed stubborn reduced-order observer is designed by using the relative information and inputs of neighbor agents. Sufficient conditions are established in terms of the Laplacian matrix such that two stubborn reduced-order observers are convergent and the studied nonlinear multiagent system could achieve leader-following consensus with directed topology. Finally, an example of the multispacecraft system is provided to illustrate the effectiveness of the proposed methodology.

This article investigates the resilient noncooperative game strategy design problem for the continuous-time system subjected to the Wiener type disturbance. To reflect the reality, the fading measurement and gain perturbation phenomena are simultaneously taken into consideration. Due to the considered complexities, there is little chance of obtaining the accurate cost function. For the sake of quantifying the controller performance, we resort to a certain upper bound of cost function (UBoCF) as an alternative. Then, the so-called noncooperative game strategy is designed which is capable of minimizing the derived UBoCF in a parallel manner. The designed noncooperative game strategy is the solution to a bunch of coupled differential Riccati-like equations (DREs). In addition, the theory analysis is generalized to the infinite-horizon with hope to describe the steady-state behavior. Specifically, the restraint conditions are provided to guarantee the stability of the system. Finally, a numerical example on the load frequency control (LFC) system is shown to verify the effectiveness of the proposed methodology.

In this article, we consider a discrete-time Nash equilibrium (NE) seeking problem for graphic game subject to disturbances. For the first-order dynamics, the discrete-time outlier-resistant extended state observer (ESO)-based game strategy is proposed to enable the players to estimate the disturbances under effect of anomaly measurements and then compensate them. An event-triggered mechanism is applied between adjacent players to reduce the frequency of communication. The convergence of the outlier-resistant ESO and control strategy is presented. Moreover, the upper bound of ϵ -NE solution deviating from the unique point of nominal system is given analytically. Then, the addressed issues are extended to high-order game systems. The NE seeking-based control strategy for each player is designed such that the equilibrium point converges to the ϵ -NE which is also analytically calculated. Finally, in order to verify the effectiveness of the proposed game strategy, an example of satellite system is given.

In this article, the primary focus is on studying a Nash equilibrium (NE) seeking algorithm to maintain system resilience in multi-agent systems (MASs), which are subject to denial-of-service (DoS) attacks and disturbance. Furthermore, we demonstrate the performance of the algorithm in tolerating such attacks. DoS attacks are modeled using Markov processes, and their impact on interagent communication is investigated. The considered n-order MAS is unable to maintain normal communication links when subjected to DoS attacks. To address this issue, stability analysis is conducted to demonstrate the effectiveness of the proposed NE seeking algorithm in achieving secure control of MAS. Conditions for maintaining resilience under attacks are also provided. Finally, numerical simulations are performed on a satellite cluster system, and physical experiments are conducted using a wheeled robot ground platform to validate the effectiveness of the algorithm.

In this article, a class of the relay pursuit–evasion problem is dealt with. Multiple pursuers and a single evader take part in the considered game. We propose a region-based relay pursuit scheme for the pursuers to capture a single evader that adopts the pure evasion strategy. First, a round pursuit strategy, which is a quickest decent control law, is proposed as an alternative strategy to the pure evasion strategy. Next, a partition of the planar space is given by using the Voronoi diagram and some circular barriers to indicate the dominance region of each pursuer and each pursuit strategy. Then, the region-based relay pursuit scheme is put forward. Finally, two simulation examples are provided to verify the effectiveness and superiority of the proposed methodology.

In this article, the event-triggered$\epsilon $-Nash equilibrium ($\epsilon $-NE) seeking problem is investigated for a class of$n$-integrator multiagent systems with the consideration of multisource disturbances and measurement outliers. In the game setup, the partial information case is considered where each player/agent is only capable of obtaining information from its neighbors. For the purpose of saving network resources, the event-triggered mechanism is utilized which could schedule the signal transmission. In order to suppress the measurement outliers and estimate the disturbances, a stubborn extended-state observer (ESO) is developed by utilizing a saturation-type gain function. The estimates provided by the stubborn ESO act as a feedforward term to offset the disturbances. Then, by integrating the feedforward term with the feedback term, a composite event-triggered NE seeking strategy is developed such that the states of the noncooperative agents could converge to the${\epsilon }$-NE solution. In the end, a numerical example is given to verify the validity of our methodology.

In this article, the distributed model predictive control (MPC)-based noncooperative game problem is dealt with for the discrete-time multiplayer systems (MPSs) with an undirected graph. To reflect the reality, the state and input constraints are considered along with the matched disturbances and unmatched disturbances. The disturbance-observer-based composite MPC strategy is put forward which optimizes a given cost function over the receding horizon while eliminating the matched disturbances. An iterative algorithm is developed such that the model predictive dynamic game (MPDG) converges to the so-called$\varepsilon $-Nash equilibrium in a distributed manner. Sufficient conditions are established to guarantee the convergence of the proposed algorithm. In addition, easy-to-check conditions are also provided to ensure the uniform boundedness of the studied MPSs. Finally, a numerical example of a group of spacecrafts is provided to verify the effectiveness of the proposed methodology.

In this article, the backstepping control scheme is designed for a class of systems with multisource disturbances, actuator saturation, and nonlinearities in the domain of discrete time. To address the multisource disturbances, we put forward a novel discrete-time hybrid observer, which can deal with both modeled and unmodeled disturbances. In virtue of the radial basis function neural networks, the unknown nonlinearities are approximated. In addition, the anti-windup technique is adopted to cope with the actuator saturation phenomenon, which is pervasive in engineering practice. Bearing all the adopted mechanisms in mind, the composite control strategy is designed in a backstepping manner. Sufficient conditions are established to guarantee that the states of the system ultimately converge to a small range with linear matrix inequalities. Finally, the effectiveness of the presented methodology is verified for the spacecraft attitude system.

In this article, a discrete-time Nash equilibrium (NE) seeking problem is studied for a class of graphic games. In order to reduce the signal transmission frequency between adjacent players, a dynamic event-triggered mechanism is designed. For the purpose of regulating the actions of players to the NE points, a discrete-time NE seeking strategy is designed by only using the local action information. Then, sufficient conditions are provided to ensure that the actions of all players converge to the NE point. Finally, a numerical example of a multisatellite communication coordination problem is given to verify the effectiveness of the proposed NE seeking method.

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

In this article, the cooperative control problem is investigated for the nonlinear multiagent system (MAS). For the purpose of avoiding possible data collisions, the stochastic communication protocol (SCP) is adopted to schedule the data transmission at each time instant. To deal with the unmatched disturbances, the composite control strategy is put forward which integrates the model predictive control (MPC) and the integral sliding-mode control methods. The sufficient conditions are established to guarantee the cooperative behavior of the MAS subjected to SCP scheduling. Furthermore, the parameters of the MPC scheme are selected such that the recursive feasibility and mean-square practical stability are guaranteed. Finally, the numerical simulation on the satellites is conducted to verify the effectiveness of the proposed methodology.

This paper investigates the dynamic event-triggered fault-tolerant optimal control strategy for a class of output feedback nonlinear discrete-time systems subject to actuator faults and input saturations. To save the communication resources between the sensor and the controller, the so-called dynamic event-triggered mechanism is adopted to schedule the measurement signal. A neural network-based observer is first designed to provide both the system states and fault information. Then, with consideration of the actuator saturation phenomenon, the adaptive dynamic programming (ADP) algorithm is designed based on the estimates provided by the observer. To reduce the computational burden, the optimal control strategy is implemented via the single network adaptive critic architecture. The sufficient conditions are provided to guarantee the boundedness of the overall closed-loop systems. Finally, the numerical simulations on a two-link flexible manipulator system are provided to verify the validity of the proposed control strategy.

In this article, the synchronous fault-tolerant near-optimal control strategy design problem is studied for a class of discrete-time nonlinear pursuit-evasion (PE) games. In the studied PE game, the input saturation phenomenon and possible actuator fault are simultaneously taken into consideration. To accelerate the estimation speed, a novel nonlinear fault estimator is designed by introducing a nonlinear function. Then, for the purpose of obtaining the synchronous control strategy for the discrete-time PE games, an approximate Hamilton-Jacobi-Isaacs (HJI) equation is established, which is seldom utilized for the discrete-time approximate dynamic programming in most existing results. It should be noticed that the synchronous control strategy designed based on the approximate HJI equation can be convergent very fast because of its quasi-Newton's iteration form. Furthermore, the sufficient condition is established to guarantee that the studied system is uniformly ultimately bounded. Finally, a numerical simulation of the hypersonic vehicle system is carried out to validate the proposed methodology.

This paper is concerned with the problem of distributed H∞ filtering for switched stochastic time-delay systems with fading measurements over sensor networks. The underlying target plants are subject to fading measurements where the fading rates are described by continuous-time random variables with known statistical properties dependent on the system modes. The adjacency matrices characterizing the topology of the sensor networks are also allowed to be mode-dependent. Based on the multiple Lyapunov functional approach and average dwell-time concept, the distributed H∞filter is designed by means of the convex optimization scheme. A dedicated technique is developed via a simple algebraic equality in order to avoid solving a transcendental equation used in the existing results. With the designed filter, the error dynamics of the state estimation is guaranteed to have the mean-square exponential stability with a prescribed H∞disturbance attenuation level. Finally, a numerical example is used to demonstrate the effectiveness of the method.

In this paper, an adaptive fuzzy tracking controller is developed for a class of strict-feedback Markovian jumping systems subjected to multisource uncertainties. The unpredictable actuator failures, the unknown nonlinearities, and the unmodeled dynamics are simultaneously taken into consideration, which evolve according to the Markov chain. It is noted that the elements in the transition rate matrix of the Markov chain are not fully available. In virtue of the norm estimation approach, the challenges caused by the complex multiple uncertainties and actuator failures are effectively handled. Furthermore, to compensate for the unavailable switching nonlinearities, the fuzzy logic systems are employed as online approximators. As a result, a novel adaptive fuzzy fault-tolerant tracking control structure is constructed. The sufficient condition is provided to guarantee that the studied system is stochastically stable. Finally, a number of illustrative examples are employed to demonstrate the effectiveness of the proposed methodology.

The resilient control refers to the control methodology which provides an interdisciplinary solution to secure the control system. In this paper, the resilient control problem is investigated for a class of wireless networked control systems (WNCS) under a denial-of-service (DoS) attack. In the presence of the DoS attacker, the control command sent by the transmitter may be interfered, which can cause the degradation of the signal-to-interference-plus-noise ratio and further lead to packet dropout phenomenon. Such a packet dropout phenomenon is described by a two-state Markov-chain. A cross-layer view is adopted toward the security issue of the considered WNCS. The Nash power strategies and optimal control strategy in the delta-domain are obtained in the cyber- and physical-layer, respectively. Based on the obtained strategies, the coupled-design problem is solved which aims to drive the underlying control performance to the desired security region by dynamically manipulating the cyber-layer pricing parameters. Finally, a numerical simulation is conducted to verify the validity of the proposed methodology.

One of the key problems for space vehicles is how to deal with the contradiction between the control constraints and the disturbances from multiple resources. This paper focuses on the adaptive full state constrained controller design problem for the flexible air-breathing hypersonic vehicles with multisource uncertainties. In simultaneous presence of the aerodynamical uncertainties, the modeling errors, the external disturbances, and the contingent actuator failures, an adaptive fault-tolerant control scheme is put forward in the context of dynamic surface control. Then, the barrier Lyapunov functions are utilized to guarantee that the constraints on the velocity, the flight path angle, the altitude, and the pitch rate are never violated. In virtue of the bound estimation approach, the multisource disturbances are effectively dealt with. Finally, a number of illustrative examples are provided to demonstrate the effectiveness of the proposed methodology.

In this paper, the near-optimal nonfragile consensus control design problem is investigated for a class of discrete time-varying multiagent systems (MASs) with control- and state-dependent noises. A decentralized observer-based control protocol is proposed by using the relative output measurements. The gain perturbations/variations of the controller as well as the state- and control-dependent noises are simultaneously taken into consideration, which could better reflect the complexities in reality. The corresponding time-varying observer-based nonfragile near-optimal consensus protocol is designed for the underlying MASs over a finite horizon. To be specific, a certain upper bound is first derived for the associate cost function for the MASs. Then, such an upper bound is minimized by using the completing-the-square technique and Moore-Penrose pseudo inverse. The parameters of the time-varying observer/controller are obtained in terms of the solutions to the Riccati-like recursion. In virtue of the matrix partitioning technique, the explicit expressions of the control/observer parameters are presented. Finally, based on the derived consensus protocol, an upper bound of the associate cost function is provided as time goes to infinity. Some numerical simulations are conducted to demonstrate the validity of the proposed methodology.

In this paper, the near-optimal resilient control strategy design problem is investigated for a class of discrete time-varying system in simultaneous presence of stochastic communication protocols (SCPs), gain perturbations, state saturations, and additive nonlinearities. In the sensor-to-controller network, only one sensor is permitted to get access to the communication media so as to avoid possible data collisions. Described by a Markov chain, the SCP is employed to determine which sensor should obtain the access to the network at a certain time. Furthermore, two kinds of well-recognized complexities (i.e., state saturations and additive nonlinearities) are considered in the system model and the phenomenon of controller gain perturbation is also taken into special consideration. Accordingly, the resilient control strategy is designed by: 1) deriving a certain upper bound on the associate cost function of underlying systems and 2) minimizing such an upper bound through the utilization of the completing-the-square technique and the Moore-Penrose pseudo inverse. The resilient control strategy is obtained in an iterative manner by solving a set of coupled backward Riccati-like recursions. Furthermore, based on the proposed control strategies, the infinite horizon case is considered and the corresponding upper bound of the cost function is explicitly provided. Finally, numerical simulations are carried out on power systems in order to verify the validity of the proposed resilient control algorithms.

In this paper, the force reflection control problem is addressed for a bilateral teleoperation system with time-varying delays. A dynamic gain force observer is proposed to obtain the force information for the force reflection control scheme. In virtue of the adaptive law, the internal uncertainties can be estimated based on the prescribed performance functions. The corrective wave variable method is utilized such that the bilateral teleoperation system could achieve a satisfactory control performance. A number of practical experiments are implemented on the bilateral teleoperation system to demonstrate the validation of the proposed methodology.

In this paper, the near-Nash equilibrium (NE) control strategies are investigated for a class of discrete-time nonlinear systems subjected to the round-robin protocol (RRP). In the studied systems, three types of complexities, namely, the additive nonlinearities, the RRP, and the output feedback form of controllers, are simultaneously taken into consideration. To tackle these complexities, an approximate dynamic programing (ADP) algorithm is first developed for NE control strategies by solving the coupled Bellman's equation. Then, a Luenberger-type observer is designed under the RRP scheduling to estimate the system states. The near-NE control strategies are implemented via the actor-critic neural networks. More importantly, the stability analysis of the closed-loop system is conducted to guarantee that the studied system with the proposed control strategies is bounded stable. Finally, simulation results are provided to demonstrate the validity of the proposed method.

In this paper, the state estimation problem is investigated for a class of continuous-time complex networks with bounded distributed delay. For the network under consideration, only the outputs from a fraction of nodes are available and this put forward the new challenge, that is, the so-called partial-nodes-based state estimation problem. In order to reduce the usage of the communication resources, a general event-triggering rule is considered in the design of the estimator. A novel estimator is constructed and, by constructing novel Lyapunov-Krasovskii functionals, some easy-to-check conditions are derived such that the error dynamics is exponentially ultimately bounded. Furthermore, it is shown that the Zeno behavior can be excluded from the event-triggering rules. Numerical simulations are presented to further illustrate the effectiveness of the theoretical results.

In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.

In this paper, the event-triggered strategy design problem is addressed for a class of discrete-time nonlinear quadratic noncooperative games subject to matched disturbances. The event-triggered scheme is proposed based on the relative error of the input signals with aim to determine whether such signals should be transmitted to the actuator or not. The disturbance-observer-based game strategy is put forward to compensate the matched disturbance and also optimize the individual cost function for each player. The main purpose of the addressed problem is to design the time-varying strategy parameters such that the upper bound of the individual cost function of each player is minimized unilaterally over a finite horizon [0,N]. Sufficient conditions are first established for the existence and uniqueness of the game strategies through backward Riccati-like recursions and then the desired strategy parameters are computed iteratively by utilizing the Moore-Penrose pseudo inverse. Finally, a simulation example is provided to verify the effectiveness of the proposed design method.

We consider the problem of resilient control of networked control system (NCS) under denial-of-service (DoS) attack via a unified game approach. The DoS attacks lead to extra constraints in the NCS, where the packets may be jammed by a malicious adversary. Considering the attack-induced packet dropout, optimal control strategies with multitasking and central-tasking structures are developed using game theory in the delta domain, respectively. Based on the optimal control structures, we propose optimality criteria and algorithms for both cyber defenders and DoS attackers. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed design procedure.

This paper investigates the decentralized multi-objective robust control for the interconnected fuzzy singular perturbed model with multiple perturbation parameters. Some practical systems, such as the large power system, usually exhibits the characters of multiple time scale and interconnections among subsystems. Thus, the interconnected fuzzy singular perturbed model is proposed to model such industrial system. Additionally, the fuzzy singular perturbed model with multiple perturbation parameters can describe a nonlinear multiple time scale system more precisely than the one with a single perturbation param- eter. However, the concentrated control of interconnected fuzzy singular perturbed model can not achieve excellent performance due to the interconnection term. Sometimes the interconnection term, which can be regarded as the external disturbance, may even lead to divergence in the control system. Thus, in this paper, the decentralized multi-objective robust controller is proposed. On one hand, the control of the fuzzy singular perturbed model can achieve good results under the condition of cross-linking among the subsystems by using the decentralized controller. On the other hand, a multi-extreme optimization model is established considering the noise resistance level, dynamical performance and the control input amplitude comprehensively. The method of Estimation of Distribution Algorithms is utilized to solve this optimization model. Numerical Simulations demonstrate the effectiveness and superiority of the method proposed in this paper.