Event-Triggered Adaptive NN Tracking Control for Nonlinear Systems With Asymmetric Time-Varying Output Constraints and Application to an AUVs

Abstract

This paper studies the event-triggered adaptive neural network (NN) tracking control problem for autonomous underwater vehicle system (AUVs) with deferred asymmetric time-varying output constraints (DATV). First, a novel asymmetric time-varying barrier Lyapunov function (BLF) is constructed to deal with the DATV to simplify the stability analysis and the controller design. Second, an event-triggered adaptive NN tracking controller is established to enhance the utility of the network resources by introducing an error shifting function. The proposed controller guarantees that the tracking error converges to an arbitrarily small neighborhood of the origin within the pre-given settling time. It is proved that the initial value can lie outside the system constraint boundary and all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB). Eventually, an AUVs is offered to certify the feasibility of the acquired control algorithm.