Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning

TAN Junkai; XUE Shuangsi; GUO Zihang; CAO Hui

doi:10.11959/j.issn.2096-6652.202523

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Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning

Papers and Reports | 更新时间：2025-11-18

- Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 3, Pages: 304-315(2025)
- 作者机构：
  
  1.西安交通大学电气工程学院，陕西西安 710049
  2.西安交通大学电力设备电气绝缘国家重点实验室，陕西西安 710049
- 作者简介：
- 基金信息：
  
  The China Postdoctoral Science Foundation(2024M762602)
- DOI：10.11959/j.issn.2096-6652.202523
  CLC： TP39
- Received：07 March 2025，
  
  Revised：2025-05-26，
  
  Published：15 September 2025
- 稿件说明：
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谭浚楷,薛霜思,郭子航等.融合无模型强化学习的永磁同步电机混沌抗扰控制[J].智能科学与技术学报,2025,07(03):304-315.

TAN Junkai,XUE Shuangsi,GUO Zihang,et al.Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):304-315.
谭浚楷,薛霜思,郭子航等.融合无模型强化学习的永磁同步电机混沌抗扰控制[J].智能科学与技术学报,2025,07(03):304-315. DOI： 10.11959/j.issn.2096-6652.202523.

TAN Junkai,XUE Shuangsi,GUO Zihang,et al.Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):304-315. DOI： 10.11959/j.issn.2096-6652.202523.

摘要

针对永磁同步电机（permanent magnet synchronous motor，PMSM）控制中出现的由非线性震荡引发的混沌行为，提出了一种利用电机历史运行数据迭代获取最优控制策略的无模型强化学习方法，该方法解决了不同工作环境下由负载扰动不确定、电机模型参数不确定带来的问题。首先针对外部负载扭矩的不确定性，建立电机最优控制与外部最坏扰动之间的零和博弈，设计迭代形式的黎卡提方程，基于该方程进一步构建了一种基于模型的抗干扰最优控制器。在该控制器基础上，引入无模型强化学习思想，设计了PMSM混沌现象的数据驱动稳定方法，利用电机历史运行数据实现优化求解无模型抗扰动稳定控制器，实现对电机混沌行为的抗扰稳定控制。最后通过多个数值仿真实验对比验证了所设计方法的性能。实验结果显示，在外界负载扰动不确定的情况下，提出的方法相较传统有限时间控制方法在累计资源消耗损失性能方面提升了39.04%，在电机模型参数不确定的情况下，提出的方法相比线性二次型调节器的控制成功率提高10.71%。

Abstract

For the chaotic behavior of nonlinear oscillations occurring in the control of permanent magnet synchronous motor (PMSM)

a data-driven model-free reinforcement learning method was investigated

which only utilized the historical state data of the motor to obtain the optimal controller. This method solved the problem of uncertain external load and uncertain motor dynamic under varying and unknown working conditions. Firstly

to solve the uncertainty of external torque load

a zero-sum game between the controller and external disturbance was constructed. By redesigning the Riccati equation in iteration form

a model-based robust optimal controller was obtained. Based on this controller

by introducing the model-free reinforcement learning method

a data-driven stabilization method for chaotic phenomena in PMSM was proposed

which obtained a model-free robust optimal controller by learning from the historical operation data. Finally

the performance of the designed method was verified by comparing several simulations

and the results showed that the energy effort saving performance of our proposed method was improved by 39.04% compared with the traditional finite-time control method under the uncertainty of external load perturbation

and the success rate was improved by 10.71% compared with the linear quadratic regulator under the uncertainty of the motor model parameters.

关键词

Keywords

references

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