A learning algorithm for parameters of automatic disturbances rejection controller

被引：0
|
作者：
Wu, Lei [1 ,2 ]
Bao, Hong [1 ]
Du, Jing-Li [1 ]
Wang, Cong-Si [1 ]
机构：
[1] Electronic Equipment Structure Key Laboratory of Education Ministry, Xidian University, Xi'an 710071, China
[2] 14th Institute of China Electronics Technology Group Corporation, Nanjing 210039, China
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2014年 / 40卷 / 03期
关键词：
Probability density function - Learning algorithms - Probability distributions - Cost functions - Disturbance rejection - Parameter estimation;
D O I：
10.3724/SP.J.1004.2014.00556
中图分类号：
学科分类号：
摘要：
Considering the special characteristics of the automatic disturbance rejection controller (ADRC), with emphasis on the parameters and strong coupling among them, an algorithm is presented in this paper for tuning the parameters of the ADRC automatically. Aiming at the minimization of the control performance function, the algorithm learns an optimal set of controller parameter values of the ADRC by cost function, updating each parameter of the controller within a bounded interval probability density distribution constantly and making the probability density of the optimal control parameter maximum. The algorithm is applied to an open-loop unstable system and the industrial mechatronic drives unit (IMDU), the results of simulation and experiment show its validity. Copyright © 2014 Acta Automatica Sinica. All rights reserved.
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页码：556 / 560
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