An Improved Quadratic-Criterion-Based Iterative Learning Control for Trajectory Tracking of Robotic Arms

Abstract

In this paper, an improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic arms. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. Furthermore, the Extended Kalman Filter (EKF) is utilized to estimate error states, which restrain the effects of model errors and measurement noise. Simulations on a 2DOF Robot manipulator demonstrate that our method achieves faster convergence and better transient performance, compared to the original Q-ILC.