Learning-Based Kinematic Control Using Position and Velocity Errors for Robot Trajectory Tracking
Sheng Xu, Yongsheng Ou, Zhiyang Wang, Jianghua Duan, Hao Li
Abstract
In this article, we address the trajectory tracking problem using the learning from demonstration (LFD) method. By using the LFD method, the parameter adjusting problem in the tracking controller is avoided. Consequently, a strategy can be provided to users with limited parameter adjusting experience. The kinematic tracking problem is formulated as a second-order system and the objective is to simultaneously reduce the errors in position and velocity. The extreme learning machines (ELM) algorithm is applied in the controller design. The velocity and position are utilized as the inputs and the output is the robot corrected kinematic movement. The controller parameters are learned from the desired human or programming demonstrations taking into consideration the stability constraints. In this work, we analyze the system local and global asymptotic stability in detail. The effectiveness of the proposed strategy is demonstrated by simulation comparisons and a practical experiment using a KUKA robot manipulator.