Adaptive Fuzzy-Neural Control for a class of Nonlinear Time-Delay Systems using a State Observer

In this paper, an observer-based adaptive fuzzy-neural control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy-neural control method based on observer states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy-neural system is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

Keywords:

Adaptive fuzzy-neural control , state observer , nonlinear systems , time delay , stability