Adaptive Jacobian Based Trajectory Tracking for Redundant Manipulators with Model Uncertainties in Repetitive Tasks

Abstract

Tracking control of manipulators, which is also called kinematic control, has always been a fundamental problem in robot control, especially for redundant robots with higher degrees of freedom. This problem would become more difficult for systems with model uncertainties. This chapter presents an adaptive tracking controller that considers uncertain physical parameters. Based on the realtime feedback of task-space coordinates, by updating the motion parameters online, a Jacobian adaptive control strategy that does not require cartesian velocity and joint acceleration is established, which makes the controller much simpler. Then the Jacobian pseudo-inverse method is used to obtain the optimal repetitive solution as a secondary task. Lyapunov theory is used to prove that the tracking error of the end effector could asymptotically converge to zero. Numerical simulations verify the effectiveness of the proposed method.