Reinforcement learning control of a flexible two-link manipulator: an experimental investigation

He, Wei; Gao, Hejia; Zhou, Chen; Yang, Chenguang; Li, Zhijun

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2020
• Online Publication Date: Mar 5, 2020
• Publication Date: Dec 1, 2021
• Deposit Date: Mar 29, 2020
• Publicly Available Date: Apr 20, 2020
• Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• Print ISSN: 2168-2216
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 51
• Issue: 12
• Pages: 7326-7336
• DOI: https://doi.org/10.1109/TSMC.2020.2975232
• Keywords: Control and Systems Engineering; Human-Computer Interaction; Electrical and Electronic Engineering; Software; Computer Science Applications
• Public URL: https://uwe-repository.worktribe.com/output/5827665