Observer-based dynamic event-triggered control for nonstrict-feedback stochastic nonlinear multiagent systems

Abstract

In this paper, the consensus tracking control problem is investigated for stochastic nonlinear multiagent systems with nonstrict-feedback structure and unmeasurable states. Firstly, by proposing an event-triggered estimator, all followers not only can estimate the signal of leader, but also can avoid the continuous communication between agents. Then, an observer is designed to estimate the unmeasurable states. Based on backstepping and variable separation approach, an adaptive neural control strategy is proposed to deal with nonstrict-feedback nonlinearities. Furthermore, a modified dynamic event-triggered mechanism is developed to reduce the number of controller executions. Based on barrier Lyapunov function, an adaptive neural compensation control scheme is designed to tackle the input saturation and the state constraints of multiagent systems. Finally, it is confirmed that the proposed event-triggered adaptive controller can guarantee the convergence of consensus tracking errors and the semiglobal boundedness of all signals in the closed-loop system.