関節柔軟性と時間変動遅延を有する遠隔操作マニピュレータの制御における適応ゲイン調整のための深層強化学習
Deep Reinforcement Learning for Adaptive Gain Tuning in Control of Teleoperation Manipulators with Joint Flexibility and Time-Varying Delays
関節の柔軟性と時間変動する通信遅延を持つ遠隔操作システムに対し、安定なP+dコントローラとTD3強化学習エージェントを組み合わせ、遠隔側の比例・減衰ゲインをリアルタイム調整して振動低減と追従性能向上を実現した。
著者: Armin Attarzadeh, Mohammad Ali Ghaemifar, Alireza Khanzadeh, Soheil Ganjefar
分類: eess.SY
原文アブストラクト
Bilateral teleoperation systems that include joint flexibility better reflect real robotic systems used in surgery, space, and rehabilitation. However, joint flexibility together with time-varying communication delays makes it difficult to maintain stable and coordinated motion between the master and slave robots. To address this, we propose a hybrid control method that combines a stable Proportional-plus-Damping (P+d) controller with a model-free deep reinforcement learning agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The P+d controller provides basic stability under bounded delays, while the learning agent adjusts and tunes the remote-side proportional and damping gains in real time to reduce vibrations and improve tracking. Stability is guaranteed for bounded time-varying delays using Lyapunov-Krasovskii analysis. The approach provides a practical solution for teleoperation systems facing both joint flexibility and uncertain network delays.