A novel fuzzy-neural identification and control for uncertain robotic systems with time delays

In this paper, a fuzzy-neural identification and control for uncertain robotic systems with time delays is proposed for H_∞ tracking performance and to suppress the effects caused by multiple time-delayed state uncertainties, unmodeled dynamics, and disturbances. Each delayed uncertainty is assumed to be bounded by an unknown gain. A reference model with the desired amplitude and phase properties is given to construct an error model. A fuzzy-neural (FN) system is used to approximate an unknown controlled system from the strategic manipulation of the model following tracking errors. The proposed AFNC scheme uses two online estimations, which allows for the inclusion of identifying the gains of the delayed state uncertainties and training the weights of the FN system simultaneously. Stability and robustness of the AFNC scheme is analyzed in Lyapunov sense. It is shown that the proposed control scheme can guarantee parameter estimation convergence and stability robustness of the closed-loop system with H_∞ tracking performance for the overall system without a priori knowledge on the upper bounds of the delayed state uncertainties.