Dynamic Event-Triggered Fixed-Time Prescribed Performance Control for Uncertain Robot Manipulator With Actuator Faults

Abstract

This article studies an adaptive fixed-time control approach for uncertain robot manipulator with actuator faults via dynamic event-triggered strategy and prescribed performance techniques. Primarily, a dynamic event-triggered mechanism with bounded variable is developed to economize the computation burdens. Then, with the aid of bound estimation method and neural networks approximation, the impacts of measurement errors and actuator faults are counteracted. Moreover, by integrating state transformed function technique into backstepping design, the proposed control scheme ensures that all closed-loop signals converge to prescribed small domains around zero within fixed time, and meanwhile the tracking errors are constrained in the user-defined boundaries even if the actuator faults occur. Compared to existing tracking control schemes, the proposed approach can remarkably reduce the communication burden, while rendering fast convergence and prescribed tracking performance. Simulation and experiment studies on Phantom Omni Touch robot exhibit the merits of the developed scheme.