Robust decentralized adaptive control of robot manipulators

摘要

This paper presents a robust decentralized adaptive controller for robot manipualtors. The proposed controller does nto require special knowledge of the robot dynamcis model. Each joint of the manipulator is controlled by using an adaptive PD feedback controller and an adaptive gain acceleration feedforward. An auxiliary input is introduced to improve the convergence rate and to improve the robustness to torque disturbances and to a class of unmodelled dynamics. Lyapunov-based Model Reference Adaptive Control is used to establish the global stability of the tracking errors. Experimental results on an industrial robot are presented to verify the tracking performance of the proposed controller.