Model-Free Predictive Current Control Method for High-Speed Switched Reluctance Generator.
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| Title: | Model-Free Predictive Current Control Method for High-Speed Switched Reluctance Generator. |
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| Authors: | Li, Zixin1,2 (AUTHOR), Wang, Shuanghong1,2 (AUTHOR) wshfj@mail.hust.edu.cn, Zhou, Libing1,2 (AUTHOR) |
| Source: | Energies (19961073). Oct2025, Vol. 18 Issue 20, p5501. 21p. |
| Subjects: | Predictive control systems, Electric generators, Adaptive control systems, Current fluctuations, Observability (Control theory), Statistical models |
| Abstract: | To address the issues of excessive current ripple and poor dynamic response in conventional angle position control (APC) for high-speed switched reluctance generator (SRG), this paper proposes an online parameter identification-based model-free predictive control (MFPC) strategy. First, the system dynamics are represented as an ultra-local model (ULM), enabling the design of an extended state observer (ESO) for two-step current prediction to compensate for control delays. Second, an improved Recursive Least Squares (RLS) algorithm with covariance resetting and error clearance is implemented to accurately identify dynamic inductance online, thereby enhancing the prediction accuracy of the ESO. Third, a bus current estimation-based adaptive feedforward compensation (AFC) technique is introduced to accelerate DC-bus voltage regulation and system dynamic response. Finally, simulations conducted on a 250 kW SRG platform demonstrate that the proposed method achieves superior dynamic performance and significantly reduced current ripple compared to conventional APC method. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | To address the issues of excessive current ripple and poor dynamic response in conventional angle position control (APC) for high-speed switched reluctance generator (SRG), this paper proposes an online parameter identification-based model-free predictive control (MFPC) strategy. First, the system dynamics are represented as an ultra-local model (ULM), enabling the design of an extended state observer (ESO) for two-step current prediction to compensate for control delays. Second, an improved Recursive Least Squares (RLS) algorithm with covariance resetting and error clearance is implemented to accurately identify dynamic inductance online, thereby enhancing the prediction accuracy of the ESO. Third, a bus current estimation-based adaptive feedforward compensation (AFC) technique is introduced to accelerate DC-bus voltage regulation and system dynamic response. Finally, simulations conducted on a 250 kW SRG platform demonstrate that the proposed method achieves superior dynamic performance and significantly reduced current ripple compared to conventional APC method. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en18205501 |
