Event-Triggered Adaptive Prescribed-Time Tracking for Nonlinear Systems With Nonvanishing Uncertainties

Abstract

This article solves the problem of prescribed-time (PT) prescribed-performance tracking for a class of nonlinear systems with nonvanishing uncertainties based on a finite-time command filter (FTCF). To deal with nonvanishing uncertainties, a novel PT stabilization criterion that incorporates an adaptive method is proposed and a compensation mechanism is established to reduce the errors caused by FTCF, where the parameter adaptive estimation error achieves asymptotically zero convergence. Based on this, an event-triggered PT control strategy is designed to ensure that the closed-loop system achieves PT prescribed-performance tracking, in which a new relative-threshold event-triggered mechanism is constructed to better balance event-triggered errors and saving communication resources. Unlike the existing methods, the proposed method not only guarantees a more accurate control performance that the tracking error reaches zero at the PT moment, but also reduces the computational complexity by pioneering the introduction of FTCF. The effectiveness of the proposed method is verified by experiments.