Reinforcement learning based attitude stabilization for bionic underwater robots
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摘要:
針對一類雙波動鰭仿生水下機器人的姿態鎮定問題,提出一種基于增強學習的自適應PID控制方法.對增強學習自適應PID控制器進行了具體設計,包括PD控制律和基于增強學習的參數自適應方法.基于實際模型參數對偏航角鎮定問題進行了仿真試驗.結果表明,經過較小次數的學習控制后,仿生水下機器人的偏航角鎮定性能得到明顯改善,而且能夠在短時間內對一般性擾動進行抑制,表現出了較好的適應性.
Abstract:A reinforcement learning based adaptive PID controller was presented for the attitude stabilization of a kind of bionic underwater robot with two bionic undulating fins. The scheme of the reinforcement learning based adaptive PID controller was given concretely including the control law and the parameter adaptive method based on reinforcement learning. Simulation experiments of yaw angle stabilization based on actual model parameters were carried out. The results indicate that the stabilization performance of yaw angle is improved distinctly after several iterations of learning control and the controller can overcome ordinary disturbances in short time, exhibiting its preferable adaptability.
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Key words:
- robots /
- bionics /
- underwater vehicles /
- reinforcement learning /
- adaptive control /
- attitude control
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