Robust Adaptive Neural Network Control For A Class Of Multiple-Input Multiple-Output Nonlinear Time Delay System With Hysteresis Inputs And Dynamic Uncertainties

This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems with dynamic uncertainties, hysteresis input, and time delay. The studied systems are composed of N nonlinear time-delay subsystems and the interconnection terms are contained in every equation of each subsystem. Adaptive neural control algorithms are developed by introducing a well-defined smooth function. The unknown time-varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov-Krasovskii functions and introducing an available dynamic signal. The main advantage of the proposed controllers is that they contain fewer parameter estimates that need to be updated online. Consequently, the accuracy of ultimate tracking errors asymptotically approaches a pre-defined bound, and all signals in the closed-loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the proposed adaptive neural network control schemes.