Stable adaptive neural control for a nonlinear robot system in the presence of actuator failures and uncertainties

Abstract

The bounded nonlinear time-varying actuator torque coefficients as well as uncertainties may deteriorate the performance of a robot. This work presents a design methodology of a stable adaptive neural controller to overcome the performance degradation for an uncertain nonlinear robot system with actuator failures. The proposed control scheme is based on the Lyapunov stability approach for adaptive control using a GFN (Gaussian function network) to approximate a nonlinear dynamic terms. The proposed controller can improve performance degradation and achieve task completion despite actuator failures and uncertainties. Simulation results are shown to verify the validity and robustness of the proposed control scheme. ©ISAROB 2007.