Distributed Event-triggered Control of Networked Strict-feedback Systems Via Intermittent State Feedback

TL;DR

This paper develops a distributed neuroadaptive control method for networked strict-feedback systems with intermittent state feedback, ensuring stability and improved tracking performance through event-triggered mechanisms.

Contribution

It introduces a novel fully distributed event-triggered control scheme for networked strict-feedback systems using triggered states, overcoming non-differentiability issues and ensuring stability.

Findings

All closed-loop signals are semi-globally uniformly ultimately bounded.

Tracking error converges to a residual set around zero.

Numerical simulations confirm the effectiveness and efficiency of the proposed control scheme.

Abstract

It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed neuroadaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered states from the neighbor agents. To circumvent the non-differentiability of virtual controllers stemming from state-triggering, we first develop a distributed continuous control scheme under regular state feedback, upon which we construct the distributed event-triggered control scheme by replacing the states in the preceding scheme with the triggered ones. Several useful lemmas are introduced to allow the stability condition to be established with such replacement, ensuring that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the output tracking error converging to a residual set around zero. Besides, with proper choices of the design parameters, the tracking performance in the mean square sense can be improved. Numerical simulation verifies the benefits and efficiency of the proposed method.