Learning Control for Flexible Manipulators with Varying Loads: A Composite Method with Robust Adaptive Dynamic Programming and Robust Sliding Mode Control

Abstract

This paper focuses on the learning-based motion control for flexible manipulators with varying loads via the singularly perturbed technique. Considering the two-timescale feature of the flexible manipulator, system dynamics are decomposed into fast and slow subsystems, and corresponding sub-controllers are designed with robust adaptive dynamic programming (RADP) and robust sliding mode control (RSMC) methods, respectively. In the proposed composite control framework, an RADP-based sub-controller is developed to realize the trajectory tracking and alleviate the parametric uncertainty utilizing rotating angles in the slow timescale, while an RSMC sub-controller is introduced to improve the vibration suppression in the fast timescale. Finally, the stability of the closed-loop system is guaranteed, and simulations are carried out to show the effectiveness of the proposed control algorithm.