Neuroadaptive practical fixed-time tracking control for nonstrict-feedback nonlinear systems under event-triggered mechanism

This paper is concerned with establishing an adaptive practical fixed-time tracking control strategy for nonstrict-feedback nonlinear systems with an event-triggered mechanism. Firstly, the piecewise functions and the command filtered technique are separately utilized to avoid the emergence of singularity problem and "complexity explosion." Secondly, neural networks are not only exploited to approximate unknown nonlinear functions and further deal with the nonstrict-feedback structure but also assisted in achieving practical fixed-time stability with the application of command filtered technique. Then, the construction of an event-triggered control signal is effectual means of saving communication resources, and the design of an estimator that identifies the adaptive parameter can effectively dodge the occurrence of over-parametrization. Furthermore, the proposed control algorithm enables that: (1) the tracking error converges to near the origin in a fixed time; (2) all signals of the closed-loop system remain bounded; (3) Zeno behavior will not occur. Finally, the validity of the proposed control strategy is verified via a practical example of the electromechanical system and a comparative simulation.