High Performance Model Predictive Control for PMSM System Using Bayesian Ascent and Gaussian Process

被引：1

作者：

Wang, Fengxiang ^{[1
]}

Li, Zheng ^{[1
]}

Yu, Xinhong ^{[1
]}

Ke, Dongliang ^{[1
]}

Grimm, Ferdinand ^{[2
]}

Kennel, Ralph ^{[3
]}

机构：

[1] Chinese Acad Sci, Quanzhou Inst Equipment Mfg, Haixi Inst, Jinjiang 362200, Peoples R China

[2] UCL, Dept Mech Engn, London WC1E 6BT, England

[3] Tech Univ Munich, Chair High Power Converter Syst, D-80333 Munich, Germany

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2024年 / 39卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Bayesian optimization; model predictive control (MPC); permanent magnet synchronous motor (PMSM); TORQUE CONTROL; FLUX CONTROL; DRIVES; ALGORITHM; DESIGN;

D O I：

10.1109/TEC.2023.3338456

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Model predictive control has been widely developed for electrical drive systems. The weighting factors are key parameters affecting the control performance of the motor. This paper proposes a Bayesian ascent and Gaussian process method for the optimized weighting factors calculation. The root mean square error of the current in the d-q axis is taken as a criterion. A data-driven probabilistic proxy model is formed based on the Gaussian process, and the optimal weighting factor is obtained by Bayesian ascent. Grid sampling, random sampling, and sampling in the maximum possible region are integrated to improve sampling reliability. Furthermore, the maximum torque per ampere calibration and motor parameter identification can be implemented by the proposed method as well. Finally, a new perspective of global parameter optimization based on data probability is proposed. The effectiveness of the proposed methods is verified by experimental results.

引用

页码：851 / 861

页数：11

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