This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

In this article, the optimal leader-following consensus of nonlinear second-order delayed multiagent systems with input constraints is addressed. To begin with, a novel intermittent event-triggered (IET) control protocol is proposed, and a novel time-delay piecewise differential inequality is established to effectively integrate state delay, intermittent control, and event-triggered strategy. Then, by the proposed control protocol and inequality, two criteria for the consensus of the second-order delayed multiagent systems and its optimality under input saturation are derived successively. Furthermore, a constrained-input PI algorithm and a critic-only online RL algorithm are devised for designing IET optimal control and approximate IET optimal controller, respectively. Finally, two numerical examples are created to verify the efficacy of the basic research.

This article investigates the intermittent event-triggered optimal leader-following consensus for nonlinear multi-agent systems (MASs) utilizing the actor-critic algorithm. First, we propose a novel distributed intermittent event-triggered control strategy, and a sufficient criterion is obtained to guarantee the leader-following consensus of MASs by establishing a novel piecewise differential inequality. Next, the intermittent event-triggered optimal control strategy is delicately given. Remarkably, the optimality of MASs is proven based on policy iteration and the convergence of the closed-loop system is also proved based on the Lyapunov stability theory. Then, the intermittent event-triggered approximate optimal control strategy is designed via an actor-critic network whose weights are only updated at the trigger instants. Furthermore, the Zeno behavior can be excluded in this article. Finally, two simulation examples further verify the effectiveness of the proposed scheme.

A new type of asymptotic stability for nonlinear hybrid neutral stochastic systems with constant delays was investigated recently, where the criteria depended on the delays’ sizes. Unfortunately, developed theory so far is not sufficient to deal with challenging problems of the decay rate, time-varying delays, and nonautonomous issues. These problems have not been tackled in the existing literature. Consequently, under the weak constraints, this article focuses on the general decay, including the exponential stability and the polynomial stability, for nonlinear nonautonomous hybrid neutral stochastic systems with time-varying delays by the approach of the multiple degenerate functionals. Moreover, this article derives the interesting assertions related to the general$H_{\infty }$stability and the polynomial growth at most.

In this paper, the th moment input-to-state exponential stability for delayed recurrent neural networks (DRNNs) with Markovian switching is studied. By using stochastic analysis techniques and classical Razumikhin techniques, a generalized vector -operator differential inequality including cross item is obtained. Without additional restrictive conditions on the time-varying delay, the sufficient criteria on the th moment input-to-state exponential stability for DRNNs with Markovian switching are derived by means of the vector -operator differential inequality. When the input is zero, an improved criterion on exponential stability is obtained. Two numerical examples are provided to examine the correctness of the derived results.

This paper established the vector Razumikhin-type theorem on exponential stability for stochastic functional differential systems by the stochastic analysis techniques and the property of${M}$-Matrix. Several novel stability criteria with vector$ {\mathscr {L}}$-operator differential inequalities for stochastic delay differential systems, especially some of which includes the cross-item, were obtained by means of the vector Razumikhin theorem. By applying these new results, the robustness of global exponential stability of delay recurrent neural networks to random disturbed has been analyzed.