Event-Triggered Practical Prescribed Time Output Feedback Neuroadaptive Tracking Control Under Saturated Actuation
Shuyan Zhou, Yongduan Song, Changyun Wen
Abstract
This work focuses on the issue of event-triggered practical prescribed time tracking control for a type of uncertain nonlinear systems subject to actuator saturation and unmeasurable states as well as time-varying unknown control coefficients. First, a state observer with simple structure is constructed by means of neural network technology to estimate the unmeasurable system states under time-varying control coefficients. Then, with the help of one-to-one nonlinear mapping of the tracking error, an event-triggered output feedback control scheme is developed to steer the tracking error into a residual set of predefined accuracy within a preassigned settling time. Unlike existing related control methods, there is no need to involve finite-time state observer or fractional power feedback of system states, and thus, the control solution presented here is less complex and more acceptable. The key technique in control design lies in the establishment of an alternative first-order auxiliary system for dealing with the impact arisen from the input saturation. In our proposed approach, a new bounded function related to auxiliary variable and new dynamics of the auxiliary system are skillfully utilized such that the upper bound of the difference between actual input and designed input signal is not involved in implementation of the controller.