林龙信, 谢海斌, 沈林成. 仿生水下机器人的增强学习姿态镇定[J]. 工程科学学报, 2012, 34(1): 76-79. DOI: 10.13374/j.issn1001-053x.2012.01.014
引用本文: 林龙信, 谢海斌, 沈林成. 仿生水下机器人的增强学习姿态镇定[J]. 工程科学学报, 2012, 34(1): 76-79. DOI: 10.13374/j.issn1001-053x.2012.01.014
LIN Long-xin, XIE Hai-bin, SHEN Lin-cheng. Reinforcement learning based attitude stabilization for bionic underwater robots[J]. Chinese Journal of Engineering, 2012, 34(1): 76-79. DOI: 10.13374/j.issn1001-053x.2012.01.014
Citation: LIN Long-xin, XIE Hai-bin, SHEN Lin-cheng. Reinforcement learning based attitude stabilization for bionic underwater robots[J]. Chinese Journal of Engineering, 2012, 34(1): 76-79. DOI: 10.13374/j.issn1001-053x.2012.01.014

仿生水下机器人的增强学习姿态镇定

Reinforcement learning based attitude stabilization for bionic underwater robots

  • 摘要: 针对一类双波动鳍仿生水下机器人的姿态镇定问题,提出一种基于增强学习的自适应PID控制方法.对增强学习自适应PID控制器进行了具体设计,包括PD控制律和基于增强学习的参数自适应方法.基于实际模型参数对偏航角镇定问题进行了仿真试验.结果表明,经过较小次数的学习控制后,仿生水下机器人的偏航角镇定性能得到明显改善,而且能够在短时间内对一般性扰动进行抑制,表现出了较好的适应性.

     

    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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