Continuous Control Set Predictive Control with Affine Registration Technique for Permanent Magnet Synchronous Motor Drive

Wentao Zhao, Jianxiong Ye () and Min Yang
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Wentao Zhao: School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
Jianxiong Ye: School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China
Min Yang: School of Digital Commerce and Trade, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China

Energies, 2024, vol. 17, issue 18, 1-14

Abstract: This article introduces an affine registration (AR) algorithm to improve the performance of ultra-local (UL) model-free predictive control (MFPC) methods. The UL model is extensively utilized in continuous control set (CCS)-MFPC to estimate lumped parameters. However, in these cases, variations in inductance during operation can significantly impact the input gain coefficient of the UL model. Little work has focused on this aspect, which makes the motivation and novelty of this paper. The AR technique, derived from image processing methodologies, is employed to establish a mapping between predicted states and actual states during the commissioning process. Subsequently, during actual operation, the resolved voltage reference is modified based on the AR matrix to compensate for errors in the UL model. The theoretical control model is rigorously derived, and experimental results are presented to validate the superior performance of the proposed method.

Keywords: continuous control set; model predictive control; robustness; permanent magnet synchronous motor; ultra-local model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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