Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

Abstract

This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with the time-varying disturbances. While the fuzzy logic systems (FLSs) serve as the approximators to the unknown dynamics, the compound disturbance is comprised of the time-varying disturbance and the approximation error of the FLS. An event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of "algebraic loop", seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC) that functions in the channel of sensor to controller. The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. While the satisfactory learning performance of the FLSs and the compound disturbances estimation are maintained, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment is carried out to exemplify the effectiveness of the proposed control scheme.