Design of adaptive fuzzy sliding mode controller for robot manipulators

Abstract

In this paper the adaptive fuzzy system is used as an adaptive approximator for robot nonlinear dynamics. A theoretical justification for the adaptive approximator is provided by proving that if the representative point (RP or switching function) and its derivative in sliding mode control are used as the inputs of the adaptive fuzzy system, the adaptive fuzzy system can approximate the plant nonlinear dynamics in the neighborhood of the switching hyperplane. Thus the fuzzy controller design is greatly simplified, and at the same time, fuzzy control rules can be obtained easily by the reaching condition due to the sliding mode control. A new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics. The system stability and tracking error convergence are proved by Lyapunov techniques that yield a novel adaptive parameter learning law.