Design of a class of self-learning fuzzy controller and its robustness analysis

被引：0
|
作者：
Zhang, Enqin [1 ]
Shi, Songjiao [1 ]
Xu, Lihong [1 ]
机构：
[1] Shanghai Jiaotong Univ, Shanghai, China
来源：
Kongzhi yu Juece/Control and Decision | 2000年 / 15卷 / 03期
关键词：
Knowledge based systems - Learning algorithms - Membership functions - Robustness (control systems);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
To improve the performance of fuzzy rules, a class of self-learning fuzzy controller is presented. Firstly, the fuzzy variable with Gauss membership function is represented by two parameters. Based on it, the analysis of the relation between fuzzy variable and precise variable is given. Then, with the new representation of fuzzy variable, the fuzzy rules are simply parametrized. According to the simple structure of fuzzy rules, a new algorithms of rules' adjustment is given, including rule-adding, rule-deleting and rule-modification. The self-learning algorithm is much simpler and more systematic than based on linguistic rules. Experiments for two order plants show that the new self-learning fuzzy controller has better robustness than common fuzzy controller and PID controller.
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页码：286 / 289
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