Adaptive control of a nonlinear dc motor drive using recurrent neural networks

被引：50

作者：

Nouri, Khaled

Dhaouadi, Rached ^{[1
]}

Braiek, Naceur Benhadj

机构：

[1] Amer Univ Sharjah, Sch Engn, Dept Elect Engn, Sharjah, U Arab Emirates

[2] Ecole Polytech, LECAP, La Marsa 2078, Tunisia

来源：

APPLIED SOFT COMPUTING | 2008年 / 8卷 / 01期

关键词：

recurrent neural network; nonlinear system; motor drive; Kalman filter; inverse model;

D O I：

10.1016/j.asoc.2007.03.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system. (C) 2007 Elsevier B. V. All rights reserved.

引用

页码：371 / 382

页数：12

共 25 条

[1] A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics [J].