Observer-Based Prescribed Performance Speed Control for PMSMs: A Data-Driven RBF Neural Network Approach

被引：41

作者：

Lin, Xinpo ^{[1
]}

Xu, Ruiqi ^{[1
]}

Yao, Weiran ^{[1
]}

Gao, Yabin ^{[1
]}

Sun, Guanghui ^{[1
]}

Liu, Jianxing ^{[1
]}

Peretti, Luca ^{[2
]}

Wu, Ligang ^{[1
]}

机构：

[1] Harbin Inst Technol, Dept Control Sci & Engn, Harbin 150001, Peoples R China

[2] KTH Royal Inst Technol, Div Elect Power & Energy Syst, S-11428 Stockholm, Sweden

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2024年 / 20卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Disturbance observer; motor drive; permanent magnet synchronous motor (PMSM); prescribed performance control (PPC); radial basis function neural network (RBFNN); DISTURBANCE REJECTION CONTROL; CONNECTED NPC CONVERTERS; ADAPTIVE-CONTROL; SYSTEMS; DESIGN;

D O I：

10.1109/TII.2024.3357194

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

引用

页码：7502 / 7512

页数：11

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