Dynamic Memory Event-Triggered Adaptive Control for a Class of Strict-Feedback Nonlinear Systems
Yuezhi Liu, Yong Chen
Abstract
This brief presents a dynamic memory event-triggered mechanism based adaptive control strategy for a class of strict-feedback nonlinear systems. Firstly, the dynamic memory event-triggered mechanism (DMETM) is established, rigorous proof demonstrates that the triggering intervals of the proposed DMETM are larger than that of the memoryless dynamic event-triggered mechanism. Furthermore, the adaptive control strategy is designed via the dynamic surface control, by which the “explosion of complexity” in the backstepping design process is avoided. Additionally, the proposed DMETM based adaptive dynamic surface controller can guarantee the closed loop system to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the simulation results illustrate the validity of the proposed control strategy.