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

SANCAK C., Yamac F., Itik M.

ROBOTICA, vol.40, no.10, pp.3378-3395, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 10
  • Publication Date: 2022
  • Doi Number: 10.1017/s0263574722000273
  • Journal Name: ROBOTICA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.3378-3395
  • Keywords: cable-driven parallel robot, reinforcement learning, position control, torque distribution, MIMO system, DISTRIBUTION ALGORITHM
  • Karadeniz Technical University Affiliated: Yes


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.