Adaptive Dynamic Surface Control of Uncertain Nonlinear Time-Delay Systems Based on High-Gain Filter Observer and Fuzzy Neural Networks

In this paper, a novel adaptive fuzzy-neural dynamic surface control (DSC) approach is proposed for a class of single-input and single-output (SISO) uncertain nonlinear strict-feedback systems with unknown time-varying delays and unmeasured states. Fuzzy neural networks are employed to approximate unknown nonlinear functions, and a high-gain filter observer is designed to tackle unmeasured states. Based on the high-gain filter observer, an adaptive output feedback controller is constructed by combining Lyapunov-Krasovskii functions and DSC backstepping technique. The proposed control approach can guarantee all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The key advantages of our scheme include that (i) the virtual control gains are not constants but nonlinear functions, and (ii) the problem of "computational explosion" is solved.