Event-Triggered Adaptive Control for Nonlinear Switched Systems with MDADT

Abstract

This paper studies the event-triggered adaptive control for switched nonlinear systems under the mode-dependent average dwell time (MDADT) switching law. Neural networks (NNs) are utilized to approximate the unknown dynamics. The designed event-triggered mechanism relying on switching signals takes into account the influence of mismatch between subsystem and controller on system performance, and realizes the saving of communication resources on the basis of achieving the control objectives. Furthermore, by combining the MDADT scheme with the backstepping recursive design technique, an efficient event-triggered adaptive neural tracking controller design algorithm is proposed such that all signals of the closed-loop system are bounded, and the tracking error eventually converge to a small neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control algorithm.