智能科学与技术学报2025，Vol.7Issue(3)：304-315,12.DOI:10.11959/j.issn.2096-6652.202523
融合无模型强化学习的永磁同步电机混沌抗扰控制
Chaotic disturbance rejection control of permanent magnet synchronous motor integrating model-free reinforcement learning
摘要
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 un-certain 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 equa-tion 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 pro-posed,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 en-ergy 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
永磁同步电机/混沌现象/强化学习/零和博弈/前馈补偿控制器Key words
PMSM/chaos/reinforcement learning/zero-sum game/feedforward compensation controller分类
信息技术与安全科学引用本文复制引用
谭浚楷,薛霜思,郭子航,曹晖..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,2025,7(3):304-315,12.基金项目
中国博士后科学基金(No.2024M762602)The China Postdoctoral Science Foundation(No.2024M762602) （No.2024M762602）
