This paper investigates the consensus control problem of multi-agent systems under intermittent communication and unmodeled external disturbances. The main contribution is to overcome the limitation of current synchronous intermittent framework and propose a novel asynchronous intermittent communication methodology on multi-agent systems. In this intermittent methodology, the state space is divided into three distinct regions by introducing both safety and intermittent boundaries, which enables effective monitoring of agent error dynamics.Furthermore, an asynchronous intermittent communication protocol is designed, where the activation and rest intervals are adjusted based on the real-time error states of the agents.By utilizing the distributed extended observer to observe the relative output information and unmodeled disturbances, the novel asynchronous intermittent consensus protocol with disturbance rejection is designed to realize the overall consensus of the multi-agent systems. The proposed spatial-segmentation-dependent intermittent communication methodology can adjust communication and non-communication time of each agent asynchronously according to the communication requirements, under which the multi-agent systems can tolerate more non-communication time and reduce the communication frequency. Finally, numerical simulations are performed to verify our results.

This article investigates the distributed resilient energy management (EM) strategy for the seaport microgrid under stealthy attacks. First, based on an analysis of seaport microgrid characteristics, we construct an EM model that aims at minimizing both operating cost and security defense resource (SDR) cost. Second, we present a distributed, resilient strategy by defining node security levels and establishing dynamic security intervals. We prove that the gap between the obtained feasible solution and the optimal one is bounded. The designed strategy is capable of tolerating the effect of the unlimited number of stealthy attacked nodes on the seaport microgrid. In addition, given the limited SDRs for the resilient EM of the island seaport microgrid, a distributed mechanism for searching the minimum security connected dominating set (MSCDS) is proposed to minimize the size of trusted nodes. Finally, simulation results demonstrate the effectiveness of the proposed strategy. Note to Practitioners: This article addresses the vulnerability of the seaport microgrid, a critical issue that impacts EM and disrupts seaport operations. Current approaches to seaport EM do not account for potential attacks. Meanwhile, existing methods for attack resilience often overlook the costs of security resources. We propose a new approach for the distributed and resilient EM of the island seaport microgrid. Secure operation is achieved by protecting the fewest trusted nodes, thereby conserving SDRs. We then show how this algorithm (searching the MSCDS) can be efficiently designed. Preliminary simulations indicate its feasibility, though it has yet to be tested in a production environment. Future research will focus on designing trusted nodes within dynamic topologies.

This article addresses the event-based adaptive optimal fault-tolerant consensus control problem for a class of nonlinear multiagent systems (MASs) under a directed graph. It can be solved that both control gains and actuator failure parameters are unknown in considered MASs, which enhances the practicability of optimal consensus control. First, a reinforcement learning-based distributed optimal control law is designed by constructing the identifier-critic-actor learning networks. Furthermore, by utilizing the distributed optimal control law as an auxiliary variable, an adaptive fault-tolerant controller is proposed to effectively compensate for actuator failures. Meanwhile, a co-design scheme is proposed for the construction of an event-triggered control input with the fault-tolerant property. It is proven that the designed control input ensures the boundedness of closed-loop systems through rigorous stability analysis. Finally, the effectiveness of the developed approach can be illustrated via simulations of numerical nonlinear MASs and a group of autonomous underwater vehicles.

In this paper, the collision avoidance process of underwater vehicles is considered, and an improved potential function is proposed to reduce the energy consumption of underwater vehicles when approaching the target position. Aiming at the negative gradient of the potential function in the controller, the potential function based on target position is proposed to prevent underwater vehicles from oscillating near the target position due to the excessive negative gradient of the potential function. Then, the Lyapunov function is used to prove that the potential function can achieve the purpose of underwater vehicles collision avoidance [1], and the improved potential function is compared with the general potential function during collision avoidance. Finally, two potential functions are compared by simulation, which shows the advantage of improving the potential energy function in reducing unnecessary energy consumption.

This article investigates an output consensus control problem for heterogeneous multi-agent systems with switching disconnected networks. As compared to similar works, each follower can measure only part information of the leader’s output in this paper, which lightens the measurement burden of simple agents when the dimension of leader’s output is large-scaled. In this case, due to the coexistence of incomplete measurements of leader’s output and disconnected networks, the outputs of some agents can deviate from the leader though there exists the observer-based control on them. In order to overcome this difficulty, we utilize the theory of switching unstable systems and propose a novel segmented time unit method. With the aid of this method, the switching intervals are segmented into some time units. Then by analyzing the cooperative control rule within the time units, the stabilizing characteristics of switching behaviors can be obtained to offset the divergence during the switching intervals. On this basis, a novel segmented time-varying Lyapunov function is developed to analyze the error states and sufficient criteria for the output consensus are derived. At last, a numerical simulation is shown to verify the theoretical results.Note to Practitioners—Most existing works on switching disconnected networks require that the leader (or the exosystem) is critically stable (or stable). However, unstable high-dimensional leader widely exists in the fields of multi-agent systems, such as the formation control of MASs where the agents are affine functions of time. On this account, this paper studies multi-agent systems with unstable high-dimensional leader under switching disconnected networks. To solve this problem, a novel segmented time unit method is proposed in this paper to study multi-agent systems with switching disconnected networks and incomplete leader measurement. Based on the segmented time unit approach, the observer-based control protocols and switching signals are given to realize the overall consensus. Numerical simulations suggest that this approach is feasible but it has not been tested in production. Future works will consider the formation control problem with switching disconnected networks and incomplete leader measurement.

This article studies the optimized backstepping consensus control problem for uncertain nonstrict-feedback nonlinear multiagent systems with intermittent actuator faults and input quantization. A fuzzy approximation-based optimal consensus control scheme is proposed via the reinforcement learning algorithm. Meanwhile, a time-varying constraint function is embedded into the optimized backstepping consensus design to guarantee the consensus errors converge to a preassigned residual set within the prescribed time. To overcome the difficulty of the virtual optimized controller design caused by unknown virtual control coefficients, a class of intermediate variables is designed. A modified variable separation technique is proposed to circumvent the problem of algebraic loop caused by the construction of virtual optimized controllers. Then, based on the reconstruction of unknown bounds, a fault-tolerant controller under the quantization action is developed to compensate for the impact of unknown actuator faults automatically, and achieve semiglobally ultimately uniformly bounded results. In the end, a practical example of a group of single-link manipulators is given to illustrate the validity of the presented method.

This article addresses an adaptive consensus control problem for heterogeneous multiagent systems (MASs) with switching disconnected topologies. Unlike the existing works on switching disconnected topologies, the unstable high-dimensional leader is first considered in this work. To tackle this problem, we propose a novel blockwise energy descent approach. This approach divides the switching periods into several time blocks and mines the operation laws of agents within these blocks. Then, the descent phenomenon at switching time can be obtained, which can be used to counteract the divergence within the switching periods. Building upon this, we develop a time-varying Lyapunov function to describe the system's dynamics and establish conditions for achieving the output consensus. Finally, we develop a simulation example to confirm the validity of our theoretical results.

This article addresses the problem of prescribed-time optimal consensus control for a class of uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks over resilient event-triggered communication (RETC). First, a novel performance index function is devised to balance the convergence of consensus error and the cost of control energy, thereby achieving the optimal consensus with the preassigned steady-state precision and convergence time. Subsequently, a set of intermediate variables is established within the framework of reinforcement learning (RL)-based optimized backstepping to solve the problem of unknown control gains, which facilitates the implementation of optimal controllers. Furthermore, the designed actual optimal control law is transmitted through RETC, which provides two advanced advantages: 1) a resilient switching control protocol to avoid the impact of DoS attacks and 2) a flexible switching threshold event-triggered mechanism to conserve communication resources and balance system performance. Finally, the effectiveness of the presented approach is verified by a simulation example.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.

This article investigates optimal control problem for a class of discrete-time (DT) nonlinear systems with unknown dynamics. With the help of a broad learning system (BLS), a novel online adaptive dynamic programming (ADP) controller is presented. First, to approximate the unknown system dynamics, an approximator based on BLS is presented. The connection weights are calculated by the data of the system by using the ridge regression algorithm. Then, two BLSs are adopted to approximate the optimal cost function and optimal control law, respectively. The connection weights of these two BLSs are updated using the given weights tuning law at each sampling instant. The proposed optimal controller is proved to ensure that all the system states and estimation errors are uniform ultimate bounded. Finally, simulation examples are carried out to further demonstrate the effectiveness of the proposed BLS-based approximator and optimal controller.

For a class of stochastic nonlinear systems in pure-feedback form with dead-zone input and multiple time-varying delays, a novel neural network (NN)-based adaptive control approach is presented in this paper through the use of backstepping approach and dynamic surface technique. By choosing proper Lyapunov-Krasovskii functionals, utilizing the characteristic of hyperbolic tangent functions and adopting the function separation technique, difficulties of controller design that introduced by the time-varying delays can be dealt with properly. Moreover, all unknown nonlinear functions are lumped together and approximated by the NN. Additionally, any information over the boundedness of dead-zone parameters is not needed in the process of controller design. The control scheme proposed in this paper ensures the boundedness in probability of all signals in the closed-loop system, besides, excellent performance of arbitrarily small tracking error will be achieved by selecting control parameters appropriately. At last, two numerical simulation examples are provided to verify the validity of the designed algorithm.

A distributed optimization problem based on a continuous-time multiagent system (MAS) disturbed by general noise is considered in this paper. The general noise, under some relaxed assumptions, which may be a stationary process, is proposed to describe the disturbance among agents more accurately. The noise-to-state (NOS) stability of the concerned MAS is analyzed based on an improved theoretical result of random differential equations. Furthermore, the relative sufficient conditions in the form of linear matrix inequality are developed with less conservatism, from which the minimum estimation error between the optimal solution and the NOS stable state of the proposed MAS with general noise can be obtained by choosing some appropriate distributed optimization parameters. One example is used to verify the effectiveness of the proposed approach.

Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be nonwhite, is introduced to NNs. Since NNs with nonwhite noise cannot be described by Itô integral equation, a novel modeling method of stochastic NNs is utilized. By a framework in light of random field approach and Lyapunov theory, the global asymptotic stability and stabilization in probability or in the mean square of NNs with general noise are analyzed, respectively. Criteria for the concerned systems based on linear matrix inequality are proposed. Some examples are given to illustrate the effectiveness of the obtained results.

This paper investigates non-fragile exponential H∞ control problems for a class of uncertain nonlinear networked control systems (NCSs) with randomly occurring information, such as the controller gain fluctuation and the uncertain nonlinearity, and short time-varying delay via output feedback controller. Using the nominal point technique, the NCS is converted into a novel time-varying discrete time model with norm-bounded uncertain parameters for reducing the conservativeness. Based on linear matrix inequality framework and output feedback control strategy, design methods for general and optimal non-fragile exponential H∞ controllers are presented. Meanwhile, these control laws can still be applied to linear NCSs and general fragile control NCSs while introducing random variables. Finally, three examples verify the correctness of the presented scheme.

This brief is concerned with the stability criteria for recurrent neural networks with time-varying delay. First, based on convex combination technique, a delay interval with fixed terminals is changed into the one with flexible terminals, which is called flexible terminal method (FTM). Second, based on the FTM, a novel Lyapunov-Krasovskii functional is constructed, in which the integral interval associated with delayed variables is not fixed. Thus, the FTM can achieve the same effect as that of delay-partitioning method, while their implementary ways are different. Guided by FTM, Wirtinger-based integral inequality and free-weight matrix method are employed to develop several stability criteria, respectively. Finally, the feasibility and the effectiveness of the proposed results are tested by two numerical examples.

In this paper, a dynamic delay interval (DDI) method is proposed to deal with the stability problem of neural networks with two delay components. This method extends the fixed interval of a time-varying delay to a dynamic one, which relaxes the restriction on upper and lower bounds of the delay intervals. Combining the reciprocally convex combination technique and Wirtinger integral inequality, the DDI method leads to some much less conservative delay-dependent stability criteria based on a linear matrix inequality for neural networks with two delay components. Furthermore, the criteria for the system with a single time-varying delay are provided. Some examples are given to illustrate the effectiveness of the obtained results.

In this paper, the local synchronization problem of Markovian nonlinearly coupled neural networks with uncertain and partially unknown transition rates is investigated. Each transition rate in this Markovian nonlinearly coupled neural networks model is uncertain or completely unknown because the complete knowledge on the transition rates is difficult and the cost is probably high. By applying the Lyapunov-Krasovskii functional, a new integral inequality combining with free-matrix-based integral inequality and further improved integral inequality, the less conservative local synchronization criteria are obtained. The new delay-dependent local synchronization criteria containing the bounds of delay and delay derivative are given in terms of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.

New stability criteria and stabilization methods on networked control systems (NCSs) with short time-varying delay (STVD) are proposed in this paper. An NCS with STVD is transformed into a time-varying discrete time system. And then, this discrete time system is converted into a equivalent time-invariant system with norm-bounded uncertainties by using robust control techniques. Using this method, the conservatism of the stability condition caused by STVD can be reduced. Based on that, a single norm-bounded uncertainty is replaced by N norm-bounded uncertainties to further reduce conservatism. Theoretical analysis shows that when N is increased, the stability condition becomes less conservative. For a fixed sampling period, the obtained stability conditions explicitly depend on the upper and lower bounds of the time delay. The existence condition and design method for the controllers are also presented. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

A secondary delay partitioning method is proposed to study the stability problem for a class of recurrent neural networks (RNNs) with time-varying delay. The total interval of the time-varying delay is first divided into two parts, and then each part is further divided into several subintervals. To deal with the state variables associated with these subintervals, an extended reciprocal convex combination approach and a double integral term with variable upper and lower limits of integral as a Lyapunov functional are proposed, which help to obtain the stability criterion. The main feature of the proposed result is more effective for the RNNs with fast time-varying delay. A numerical example is used to show the effectiveness of the proposed stability result.

In the stability analysis of recurrent neural networks, one of the tasks is to reduce the conservativeness of the stability criterion. Along this routine, there are two ways to be considered. One is how to construct the Lyapunov-Krasovskii functional (LKF), and the other is how to use mathematical skills to estimate the derivatives of the LKF. The purpose of this paper is to present a brief review on the evolution on the construction of LKF for recurrent neural networks with single time-varying delay. By summarizing the observation, one can find the core elements in the construction of LKF. Moreover, one can find the evolution history on the delay-partitioning and its applications in the construction of LKF.

The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. By introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive delay-dependent criteria for the exponential stabilization problem. Three numerical examples are carried out to demonstrate the feasibility of our delay-dependent stabilization criteria.