Position control of a planar cable-driven parallel robot using reinforcement learning
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Cambridge Univ Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
cable-driven parallel robot, reinforcement learning, position control, torque distribution, MIMO system
Kaynak
Robotica
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
40
Sayı
10
