Hybrid adaptive fuzzy controller of robot manipulators with bounds estimation

Abstract

A hybrid adaptive fuzzy controller comprises a weighted combination of a direct and an indirect adaptive fuzzy controllers, with a continuously-switched supervisory controller. The direct and indirect adaptive fuzzy controllers allow fuzzy control rules and fuzzy descriptions to be incorporated respectively, to achieve better adaptation speed. In the design of the supervisory controller, the bounds of the unknown robot dynamics are not required, they are adaptively estimated in Lyapunov sense. Theoretical results and simulation studies on a two-link manipulator show that the proposed hybrid adaptive fuzzy controller is robust and stable. It also outperforms both the direct and indirect adaptive fuzzy controllers of Neo and Er (1996) in terms of tracking accuracy and magnitude of control torque required.