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Position control of a planar cable-driven parallel robot using reinforcement learning

Published online by Cambridge University Press:  17 March 2022

Caner Sancak Open the ORCID record for Caner Sancak [Opens in a new window] ,
Fatma Yamac  and
Mehmet Itik
Caner Sancak*
Affiliation:
Department of Mechanical Engineering, Karadeniz Technical University, Trabzon, Turkey
Fatma Yamac
Affiliation:
Department of Mechanical Engineering, Tarsus University, Mersin, Turkey
Mehmet Itik
Affiliation:
Department of Mechanical Engineering, Izmir Democracy University, Izmir, Turkey
*
*Corresponding author. E-mail: csancak@ktu.edu.tr
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Abstract

This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.

Keywords

cable-driven parallel robotreinforcement learningposition controltorque distributionMIMO system

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3378 - 3395
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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