Adaptive Model-Free Deadbeat Predictive Current Control for Linear Permanent Magnet Synchronous Motor Based on Immune Algorithm

The model free deadbeat predictive current control (MFDPCC) for linear permanent magnet synchronous motor (LPMSM) drive system is emerging as a promising method due to its straightforward structure and strong parameter robustness. However, the conventional MFDPCC controller with the fixed gain leads...

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Bibliographic Details
Published in:IEEE transactions on power electronics Vol. 40; no. 9; pp. 12257 - 12267
Main Authors: Zhang, Yanqing, Yuan, Hongtao, Yin, Zhonggang, Bai, Cong
Format: Journal Article
Language:English
Published: IEEE 01.09.2025
Subjects:
Adaptation models
Computational modeling
Current control
Deadbeat predictive current control (DPCC)
immune algorithm (IA)
linear permanent magnet synchronous motor (LPMSM)
Mathematical models
model free control (MFC)
Parameter estimation
Permanent magnet motors
Prediction algorithms
Predictive models
Stators
Steady-state
ISSN:0885-8993, 1941-0107
Online Access:Get full text
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Summary:The model free deadbeat predictive current control (MFDPCC) for linear permanent magnet synchronous motor (LPMSM) drive system is emerging as a promising method due to its straightforward structure and strong parameter robustness. However, the conventional MFDPCC controller with the fixed gain leads to a contradiction between the dynamic and steady-state performance. To overcome this limitation, an adaptive MFDPCC based on immune algorithm is proposed in this article. First, the impact of the parameters mismatch on traditional DPCC is analyzed. Then, an ultralocal model, known as a typical branch of model free control, is developed to replace the conventional LPMSM model. For the ultralocal model with a typical fixed gain, the control performance is analyzed in detail, and it points out that there exits the contradiction between the dynamic and steady-state performance. Thus, to effectively balance the conflict, the immune algorithm is introduced to optimize the fixed parameter adaptively. Finally, the experimental test is carried out on a 0.9-kW LPMSM test bench, and the experimental results verify the effectiveness of the proposed method.
ISSN:0885-8993
1941-0107
DOI:10.1109/TPEL.2025.3563247