Event-triggered compound learning tracking control of nonstrict-feedback nonlinear systems in sensor-to-controller channel

This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with time-varying disturbances. While the fuzzy logic systems (FLSs) approximate the unknown dynamics, 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). The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. Estimation of optimal fuzzy weights and compound disturbances follows from the event-triggered update laws. While the satisfactory learning performance is achieved, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment verifies the effectiveness of the proposed control scheme.

Automated summary

This paper proposes an event-triggered tracking control method for nonstrict-feedback nonlinear systems, utilizing a compound learning algorithm and control law design to address system uncertainties and achieve stability.