Title: Adaptive PID computed-torque control of robot manipulators based on DDPG reinforcement learning

Authors: Akram Ghediri; Kheireddine Lamamra; Abdelaziz Ait Kaki; Sundarapandian Vaidyanathan

Addresses: Laboratory of Electronics and New Technologies, Department of Electrical Engineering, University of Larbi Ben M'hidi, Oum El Bouaghi, Algeria ' Laboratory of Electronics and New Technologies, Department of Electrical Engineering, University of Larbi Ben M'hidi, Oum El Bouaghi, Algeria ' Department of Electrical Engineering, University of Larbi Ben M'hidi, Oum El Bouaghi, Algeria ' Research and Development Centre, Vel Tech University, 400 Feet Outer Ring Road, Avadi, Chennai, 600062 Tamil Nadu, India

Abstract: This paper presents a design of an adaptive PID gain tuning based on deep deterministic policy gradient reinforcement learning agent for PID computed-torque control of robot manipulators, taking the presence of unmodelled dynamics and external disturbances into consideration. The proposed approach adaptively computes the outer-loop PID controller gains, that minimise trajectory tracking errors and reject disturbances, while the closed-loop dynamics remain stable. Since the control scheme requires the knowledge of the robot's dynamics, both kinematic and dynamic equations of n-link serial manipulator are developed. The agent is implemented on UR5e robot manipulator model, using the most valid dynamic and kinematic parameters provided by the manufacturer and related works. Simulation results show that the proposed approach is robust against bounded internal and external disturbances, and achieves a good trajectory tracking performance, due to the adaptability of gain tuning over the conventional PID controller.

Keywords: UR5e robot manipulator; adaptive PID control; computed-torque control; CTC; DDPG reinforcement learning; trajectory tracking; disturbance rejection.

DOI: 10.1504/IJMIC.2022.127518

International Journal of Modelling, Identification and Control, 2022 Vol.41 No.3, pp.173 - 182

Received: 15 Oct 2021
Accepted: 16 Dec 2021
Published online: 07 Dec 2022 *

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