Event-triggered Adaptive Neural Control for Uncertain Nontriangular Nonlinear Systems with Time-varying Delays

This paper addresses the adaptive tracking problem for a class of uncertain nontriangular nonlinear systems with time-varying delays. By employing some special techniques and mean value theorem, the nonlinear time-delay system in a nonaffine and nontriangular form is transformed into a new nonstrict-feedback nonlinear time-delay system for which backstepping control design becomes feasible. In particular, a novel event-triggered mechanism including saturation is presented to pursue the low communication burden and keep the competitive control performance. By combining Lyapunov-Razumikhin method, backstepping technique, and neural network (NN) approximation-based approach, the event-based adaptive neural control strategy is developed for this class of systems. The event-triggered control scheme guarantees that the tracking error remains in a small neighborhood of the origin while all the signals in closed-loop systems are semi-global uniformly ultimately bounded (SGUUB). Finally, an illustrative example is given to clarify the feasibility and effectiveness of the developed design methodology.