Development and Implementation of a New Adaptive Intelligent Speed Controller for IPMSM Drive

被引：26

作者：

Chy, Md Muminul Islam ^{[1
]}

Uddin, M. Nasir ^{[2
]}

机构：

[1] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ H3A 2A7, Canada

[2] Lakehead Univ, Dept Elect Engn, Thunder Bay, ON P7B 5E1, Canada

来源：

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS | 2009年 / 45卷 / 03期

关键词：

Artificial neural network (ANN); field control; fuzzy-logic control; interior permanent-magnet motor; real-time implementation; speed control; torque control; vector control; SYNCHRONOUS MOTOR DRIVE;

D O I：

10.1109/TIA.2009.2018918

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

For controlling nonlinear, time-varying, or ill-defined systems, artificial intelligent controllers have proven to be superior to conventional controllers in design and performance. This paper presents a novel adaptive-network-based fuzzy inference system (ANFIS) for the speed control of interior permanent-magnet synchronous motor (IPMSM) drives. By utilizing a learning technique, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. The back-propagation technique is used for the online tuning of ANFIS parameters in order to optimize the performance of the proposed drive. The proposed control technique also provides flux control to control the motor over a wide speed range. The complete drive has been successfully implemented in real time using digital signal processor board DS1104. The performance of the proposed ANFIS-based IPMSM drive is investigated both in simulation and experiment at different operating conditions.

引用

页码：1106 / 1115

页数：10

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