基于递阶原理的非均匀采样非线性系统的模糊辨识

doi:10.3969/j.issn.0372-2112.2018.04.031

电子学报 ›› 2018, Vol. 46 ›› Issue (4): 1005-1011.DOI: 10.3969/j.issn.0372-2112.2018.04.031

基于递阶原理的非均匀采样非线性系统的模糊辨识

王宏伟, 连捷, 夏浩

大连理工大学控制科学与工程学院, 辽宁大连 116024

收稿日期:2016-09-01 修回日期:2017-02-20 出版日期:2018-04-25
- 作者简介:
- 王宏伟 男.1969年3月出生,吉林长春人.工学博士,教授.1999年毕业于哈尔滨工业大学自动控制系.现在大连理工大学控制科学与工程学院工作.已发表SCI、EI等检索论文110篇,发明专利2项.主持和参与国家和地方项目5项.研究方向:网络控制系统,多采样率系统控制,切换系统控制.E-mail:wanghw@dlut.edu.cn;连捷女.1980年9月出生,吉林白城人.工学博士,教授.2009年毕业于东北大学控制科学与工程系.现在大连理工大学控制科学与工程学院工作.已发表SCI、EI等检索论文30篇.主持和参与国家和地方项目5项.研究方向:网络控制系统,时滞系统控制,切换系统控制.E-mail:jielian@dlut.edu.cn;夏浩男.1971年10月出生,黑龙江哈尔滨人.工学博士,教授.2000年毕业于University of Birmingham.现在大连理工大学控制科学与工程学院工作.已发表SCI、EI等检索论文32篇.主持和参与国家和地方项目5项.研究方向:项目调度、生产运作管理、混杂系统控制等.E-mail:hao.x.xia@dlut.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61004040）

Fuzzy Identification for Non-Uniformly Sampled Nonlinear Systems Based on Hierarchical Principle

WANG Hong-wei, LIAN Jie, XIA Hao

School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China

Received:2016-09-01 Revised:2017-02-20 Online:2018-04-25 Published:2018-04-25
- Supported by:
- National Natural Science Foundation of China (No.61004040)

摘要/Abstract

摘要： 针对非均匀多采样率非线性系统的建模问题，提出了基于递阶原理的模糊辨识方法.首先，分析了非线性系统在输入信号非均匀周期刷新，输出信号周期采样的情况下，非线性系统可以通过提升技术，利用多个局部线性模型加权组合的模糊模型来描述.在此基础上，利用GK模糊聚类确定模糊模型前件结构，利用基于递阶原理的递推最小二乘辨识算法辨识模糊模型后件参数.同时，通过鞅定理对辨识算法的收敛性进行了研究.最后，通过仿真实例证明了本文方法的有效性.

关键词: 模糊辨识, 非均匀采样, 非线性系统, 递阶原理, 系统辨识

Abstract:

For the modeling issue of non-uniformly multi-rates sampled nonlinear systems, a fuzzy identification method based on hierarchical principle is proposed in the paper. First of all, a nonlinear system is described as a weighted combination representation of the multiple local linear models by using lift technology when the non-uniformly updating scheme for input signals and uniformly sampling scheme for output signals are taken in the data sampling process. On this basis, we propose a fuzzy identification algorithm, in which the GK fuzzy clustering method and a recursive least squared method based on hierarchical principle are used to confirm the premise structure and consequence parameters of fuzzy model, respectively. Moreover, the convergence of the identification algorithm is studied by using martingale theorem. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

Key words: fuzzy identification, non-uniformly sampling, nonlinear systems, hierarchical principle, system identification

中图分类号:

TP273

王宏伟, 连捷, 夏浩. 基于递阶原理的非均匀采样非线性系统的模糊辨识[J]. 电子学报, 2018, 46(4): 1005-1011.

WANG Hong-wei, LIAN Jie, XIA Hao . Fuzzy Identification for Non-Uniformly Sampled Nonlinear Systems Based on Hierarchical Principle[J]. Acta Electronica Sinica, 2018, 46(4): 1005-1011.

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基于递阶原理的非均匀采样非线性系统的模糊辨识

Fuzzy Identification for Non-Uniformly Sampled Nonlinear Systems Based on Hierarchical Principle

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