Neural-network-based adaptive control for induction servomotor drive system

被引：55

作者：

Lin, CM ^{[1
]}

Hsu, CF ^{[1
]}

机构：

[1] Yuan Ze Univ, Dept Elect Engn, Chungli 320, Taiwan

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2002年 / 49卷 / 01期

关键词：

adaptive control; neural network; servomotor;

D O I：

10.1109/41.982255

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A neural-network-based adaptive control (NNAC) design method is proposed to control an induction servomotor. In this NNAC design, a neural network (NN) controller is investigated to mimic a feedback linearization control law; and a compensation controller is designed to compensate for the approximation error between the feedback linearization control law and the NN controller. The interconnection weights of the NN can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the control system can be guaranteed. Additionally, in this NNAC system design, an error estimation mechanism is investigated to estimate the bound of approximation error so that the chattering phenomenon of the control effort can be reduced. Simulation and experimental results show that the proposed NNAC servomotor control systems can achieve favorable tracking and robust performance with regard to parameter variations and external load disturbances.

引用

页码：115 / 123

页数：9

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