An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives
Abstract
:1. Introduction
- (1)
- A hybrid ESN-MPC framework is proposed. The trained ESN replaces only the prediction model in the MPC controller framework and maintains the MPC-based cost function by keeping the core MPC structure.
- (2)
- The proposed strategy enhances the robustness of the parameters. The ESN-MPC strategy is initially trained offline using data collected from a real PMSM-MPC control environment. With an online learning algorithm, the proposed controller can make rapid adjustments to mitigate parameter mismatches under real-time deployment.
- (3)
- Compared with traditional MPC, the proposed ESN-MPC approach demonstrates equivalent dynamic performance while achieving superior steady-state performance under parameter mismatch conditions.
2. Model Predictive Control for PMSM Drives
3. Proposed ESN-MPC Strategy for PMSM
3.1. Structure of ESN
3.2. Offline Training of ESN
3.3. Proposed ESN-MPC Controller and Online Learning Process
3.4. Computational Complexity Analyze
4. Simulation Validation
4.1. Dynamic Response to Torque Load Change
4.2. Inductance Parameter Mismatch Investigation
4.3. Flux and Resistance Parameter Mismatch Investigation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Strategies | Estimate FLOPs | Estimate Resource Utilization (Control Frequency: 20 kHz) |
---|---|---|
Conventional FCS-MPC | ||
1 prediction step | 272 | 1.36% |
2 prediction step | 2176 | 10.88% |
3 prediction step | 17408 | 87.04% |
Proposed ESN-MPC | ||
NH = 5, with/without activate online learning | 708/453 | 3.54%/2.26% |
NH = 10, with/without activate online learning | 1615/1080 | 8.07%/5.40% |
NH = 15, with/without activate online learning | 3078/2163 | 15.39%/10.81% |
NH = 20, with/without activate online learning | 4863/3468 | 24.31%/17.34% |
NH = 30, with/without activate online learning | 9441/6786 | 47.21%/33.93% |
Parameter | Symbol | Value |
---|---|---|
Inverter parameter | ||
inverter type | / | 2L-3P VSI |
sampling delay | / | 1.5 µs |
dead time delay | / | 2.2 µs |
DC-side capacitor | Vdc | 400 V |
control frequency | fsw | 20 k Hz |
PMSM parameter | ||
stator resistance | R | 0.1 Ω |
q-axis inductance | Lq | 0.8 mH |
d-axis inductance | Ld | 0.6 mH |
pole pairs | p | 4 |
flux | ψf | 0.985 Wb |
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Zeng, X.; Li, J.; Yang, P.; Cai, H.; Zhou, Y.; Li, D. An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives. Appl. Sci. 2025, 15, 6288. https://doi.org/10.3390/app15116288
Zeng X, Li J, Yang P, Cai H, Zhou Y, Li D. An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives. Applied Sciences. 2025; 15(11):6288. https://doi.org/10.3390/app15116288
Chicago/Turabian StyleZeng, Xiao, Jing Li, Pengcheng Yang, Hongda Cai, Yongzhi Zhou, and Daren Li. 2025. "An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives" Applied Sciences 15, no. 11: 6288. https://doi.org/10.3390/app15116288
APA StyleZeng, X., Li, J., Yang, P., Cai, H., Zhou, Y., & Li, D. (2025). An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives. Applied Sciences, 15(11), 6288. https://doi.org/10.3390/app15116288