An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives
Parameter mismatch in model predictive control (MPC) strategies presents significant challenge in permanent magnet synchronous motor (PMSM) control, often leading to reduced tracking accuracy and compromised system stability under dynamic operating conditions. To address above issue, this article pr...
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MDPI AG
2025-06-01
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| author | Xiao Zeng Jing Li Pengcheng Yang Hongda Cai Yongzhi Zhou Daren Li |
| author_facet | Xiao Zeng Jing Li Pengcheng Yang Hongda Cai Yongzhi Zhou Daren Li |
| author_sort | Xiao Zeng |
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| description | Parameter mismatch in model predictive control (MPC) strategies presents significant challenge in permanent magnet synchronous motor (PMSM) control, often leading to reduced tracking accuracy and compromised system stability under dynamic operating conditions. To address above issue, this article proposes a modified parameter robust FCS-MPC framework that integrates an online learning echo state network (ESN) for real-time compensation of parameter deviations. By leveraging the structural simplicity and application efficiency of ESNs during training, the proposed approach is well-suited to tackling complex parameter variation challenges via online learning. Initially, the ESN is trained offline using data derived from a PMSM-MPC control environment. Subsequently, the trained ESN replaces the predictive model of the MPC controller, enabling online learning under varying PMSM driving conditions. The incorporation of an online ESN allows the proposed controller to achieve real-time adjustments that mitigate the effects of parameter mismatch. Plenty of simulation studies are available and demonstrate that the proposed ESN-MPC controller exhibits enhanced robustness against parameter mismatch compared to the traditional FCS-MPC method. |
| format | Article |
| id | doaj-art-fd53a136a74641d8a3c5835ea36816e9 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fd53a136a74641d8a3c5835ea36816e92025-08-20T02:23:01ZengMDPI AGApplied Sciences2076-34172025-06-011511628810.3390/app15116288An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM DrivesXiao Zeng0Jing Li1Pengcheng Yang2Hongda Cai3Yongzhi Zhou4Daren Li5School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaSchool of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaSchool of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaSchool of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaPolytechnic Institute, Zhejiang University, Hangzhou 310027, ChinaState Grid Zhejiang Power Co., Ltd., Wenzhou Power Company, Hangzhou 310007, ChinaParameter mismatch in model predictive control (MPC) strategies presents significant challenge in permanent magnet synchronous motor (PMSM) control, often leading to reduced tracking accuracy and compromised system stability under dynamic operating conditions. To address above issue, this article proposes a modified parameter robust FCS-MPC framework that integrates an online learning echo state network (ESN) for real-time compensation of parameter deviations. By leveraging the structural simplicity and application efficiency of ESNs during training, the proposed approach is well-suited to tackling complex parameter variation challenges via online learning. Initially, the ESN is trained offline using data derived from a PMSM-MPC control environment. Subsequently, the trained ESN replaces the predictive model of the MPC controller, enabling online learning under varying PMSM driving conditions. The incorporation of an online ESN allows the proposed controller to achieve real-time adjustments that mitigate the effects of parameter mismatch. Plenty of simulation studies are available and demonstrate that the proposed ESN-MPC controller exhibits enhanced robustness against parameter mismatch compared to the traditional FCS-MPC method.https://www.mdpi.com/2076-3417/15/11/6288model predictive controlpermanent magnet synchronous motorneural networkecho state networkparameter robustness |
| spellingShingle | Xiao Zeng Jing Li Pengcheng Yang Hongda Cai Yongzhi Zhou Daren Li An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives Applied Sciences model predictive control permanent magnet synchronous motor neural network echo state network parameter robustness |
| title | An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives |
| title_full | An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives |
| title_fullStr | An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives |
| title_full_unstemmed | An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives |
| title_short | An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives |
| title_sort | echo state network approach for parameter variation robustness enhancement in fcs mpc for pmsm drives |
| topic | model predictive control permanent magnet synchronous motor neural network echo state network parameter robustness |
| url | https://www.mdpi.com/2076-3417/15/11/6288 |
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