Adaptive event-triggered control for nonstrict-feedback nonlinear systems with time-varying delay and uncertain nonlinearities: A neural network approach

This paper focuses on adaptive event-triggered control within nonstrict-feedback nonlinear systems that incorporate time-varying delays and uncertain nonlinearities. The system under consideration involves unknown parameters, external disturbances, and elusive nonlinearities. Distinguishing itself from conventional methods, this work eliminates the necessity for the hypothesis of input-to-state stability (ISS) regarding measurement errors, and avoids excessive constraints of nonlinearities. A new adaptive controller is derived by integrating the neural network (NN) approximation, backstepping, and the event-triggering mechanism (ETM) to ensure the semi-global boundedness of the closed-loop system, while improving the tracking performance and avoiding Zeno phenomenon. Simulation outcomes are demonstrated to illustrate the validity of the proposed control scheme.