Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 51; no. 12; pp. 7326 - 7336
• Main Authors: He, Wei, Gao, Hejia, Zhou, Chen, Yang, Chenguang, Li, Zhijun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Control algorithms; Control theory; Differential equations; Dynamic models; Feedback control; Flexible structure; Heuristic algorithms; Learning; Manipulators; Mathematical model; neural networks (NNs); Ordinary differential equations; reinforcement learning (RL); Robot kinematics; robots; Vibration control; Vibrations
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2020.2975232

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor-critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov's direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control.