Enhancing trajectory tracking accuracy for industrial robot with robust adaptive control

Abstract A robust adaptive control method is systematically proposed in this paper to significantly reduce the relatively tracking errors of 6 degree of freedom (DOF) industrial robots under both external disturbances and parametric uncertainties. The robust adaptive control law is formulated based on the robot dynamics in the task space of the robot end-effector. The control law is designed by combining robust term and adaptive term to track the desired trajectory of the end-effector with sufficient robustness and accuracy in the presence of unknown external disturbances and parametric uncertainties. The trajectory tracking performances of the proposed control are finally guaranteed based on Lyapunov function and Barbalat's lemma. Furthermore, a stable online parametric adaption law is proposed to estimate the unknown parameters in the control law based on persistent excitation and residual estimation. Test results are obtained to show that the combined robust adaptive control reduces the final trajectory tracking error significantly as compared with conventional control.