AI 中文总结
针对含有关节灵活性和时变延迟的遥操作机器人控制问题，提出结合稳定P+d控制器与基于TD3算法的深度强化学习智能体的混合控制方法，可实时调整增益减少振动，为相关遥操作系统提供实用方案。
AI 中文摘要
包含关节灵活性的双边遥操作系统能更好地反映手术、太空和康复中使用的实际机器人系统。然而，关节灵活性及时变通信延迟使得主从机器人之间难以维持稳定协调的运动。为此，我们提出一种混合控制方法，将稳定的比例加阻尼（P+d）控制器与基于双延迟深度确定性策略梯度（TD3）算法的无模型深度强化学习智能体相结合。P+d控制器在有界延迟下提供基本稳定性，学习智能体实时调整远程侧比例和阻尼增益以减少振动并改善跟踪。通过李雅普诺夫 - 克拉索夫斯基分析保证有界时变延迟下的稳定性。该方法为面临关节灵活性和不确定网络延迟的遥操作系统提供了实用解决方案。
英文摘要
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.
Comments7 pages, 6 figures. Source code available at: https://github.com/ArminAttarzadeh/DRL-Controller-Gain-Tuner
