Online optimization based adaptive tracking control for redundant manipulators with model uncertainties

Tracking control of robot manipulators is always a fundamental problem in robot control, especially for redundant manipulators with higher DOFs. This problem may become more complicated when there exist uncertainties in the robot model. In this paper, we propose an adaptive tracking controller considering the uncertain physical parameters. Based on the coordinate feedback, a Jacobian adaption strategy is firstly built by updating kinematic parameters online, in which neither cartesian velocity nor joint acceleration is required, making the controller much easier to built. Using the Pseudo-inverse method of Jacobian, the optimal repeatability solution is achieved as the secondary task. Using Lyapunov theory, we have proved that the tracking errors of the end-effector asymptotically converge to zero. Numerical simulations are provided to validate the effectiveness of the proposed tracking method.