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

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.