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基于多智能体强化学习的滑模控制器参数整定
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国家自然科学基金项目(51875380;62063010;51375323);泰州市科技支撑项目(TG202117)


Parameter Tuning of Sliding Mode Controller Based on Reinforcement Learning of Multiple Intelligences
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    摘要:

    针对永磁同步电机系统中滑模控制器参数多且范围大难以整定,从而导致永磁同步电机控制效果不佳的问题,提出利用多智能体强化学习对滑模控制器参数进行整定的方法。该方法通过多个智能体共享奖赏的方式对控制器每个参数进行独立寻优,有效避免了不同参数选取范围差别较大而导致智能算法多参数同步寻优时产生的维度灾难问题。通过 Python 与MATLAB联合仿真,并与采用遗传算法整定参数的控制器进行比较,结果表明:多智能体的多臂老虎机算法较遗传算法整定的速度滑模控制器在超调量、响应速度、抗干扰能力和鲁棒性方面具有明显的优势,验证了该方法能够有效地解决滑模控制器参数难以整定的问题。

    Abstract:

    The method of using multi-intelligent reinforcement learning to rectify the parameters of the sliding-mode controller was proposed for the problem that it is difficult to rectify the parameters of the sliding-mode controller in the permanent magnet synchronous motor system with many parameters and a large range,which leads to poor control of the permanent magnet synchronous motor.The method was based on a shared reward for each parameter of the controller,which effectively avoided the dimensional catastrophe caused by the large difference in the range of different parameters selected by the intelligent algorithm.The results show that the multi-arm slot machine algorithm with multiple intelligences has obvious advantages over the genetic algorithm in terms of overshoot,response speed,anti-interference ability and robustness in tuning the speed sliding mode controller.It is verified that this method can effectively solve the problem that the parameters of sliding mode controller are difficult to be adjusted.

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缪刘洋,朱其新,朱永红.基于多智能体强化学习的滑模控制器参数整定[J].机床与液压,2024,52(11):160-166.
MIAO Liuyang, ZHU Qixin, ZHU Yonghong. Parameter Tuning of Sliding Mode Controller Based on Reinforcement Learning of Multiple Intelligences[J]. Machine Tool & Hydraulics,2024,52(11):160-166

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  • 在线发布日期: 2024-06-21
  • 出版日期: 2024-06-15