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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/6288 |
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| Summary: | 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. |
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| ISSN: | 2076-3417 |