Position tracking control for chaotic permanent magnet synchronous motors via indirect adaptive neural approximation

被引：30
|
作者：
Yu, Jinpeng [1 ,2 ]
Chen, Bing [1 ]
Yu, Haisheng [1 ]
Lin, Chong [1 ]
Ji, Zhijian [1 ]
Cheng, Xiaoqing [2 ]
机构：
[1] Qingdao Univ, Coll Automat Engn, Qingdao 266071, Peoples R China
[2] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 156卷
基金：
中国博士后科学基金;
关键词：
Neural networks; Permanent magnet synchronous motor; Chaos control; Nonlinear control; Backstepping; OUTPUT-FEEDBACK CONTROL; BACKSTEPPING CONTROL; NONLINEAR-SYSTEMS; NETWORKS; OBSERVER; DELAYS; DRIVE;
D O I：
10.1016/j.neucom.2014.12.054
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Position tracking control for the chaotic permanent magnet synchronous motor drive system is addressed in this paper. Neural networks are used to approximate the nonlinearities and indirect adaptive backstepping technique is employed to construct controllers. The designed indirect adaptive neural controllers can suppress chaos in the permanent magnet synchronous motor and guarantee that the position tracking error converges to a small neighborhood of the origin. Compared with the classical backstepping method, the proposed neural controllers' structure is very simple. Simulation results illustrate its effectiveness. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：245 / 251
页数：7
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