Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints

Abstract

This article investigates the event-triggered adaptive neural network (NN) tracking control problem with deferred asymmetric time-varying (DATV) output constraints. To deal with the DATV output constraints, an asymmetric time-varying barrier Lyapunov function (ATBLF) is first built to make the stability analysis and the controller construction simpler. Second, an event-triggered adaptive NN tracking controller is constructed by incorporating an error-shifting function, which ensures that the tracking error converges to an arbitrarily small neighborhood of the origin within a predetermined settling time, consequently optimizing the utilization of network resources. It is theoretically proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), while the initial value is outside the constraint boundary. Finally, a single-link robotic arm (SLRA) application example is employed to verify the viability of the acquired control algorithm.