Nonlinear predictive functional control based on T-S fuzzy model

被引：0

作者：

Duan Ming-liang ^{[1
]}

Li Ping ^{[1
]}

Zhou Shan-shan ^{[1
]}

机构：

[1] Liaoning Univ Petr & Chem Technol, Sch Informat Engn, Fushun 113001, Peoples R China

来源：

Proceedings of 2006 Chinese Control and Decision Conference | 2006年

关键词：

fuzzy clustering; T-S fuzzy model; nonlinear plant; predictive functional control;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In view of the defect that the basic predictive functional control(PFC) is only applicable for linear plant, a nonlinear predictive functional control based on T-S fuzzy model is presented. The T-S fuzzy model of nonlinear plant is identified by the fuzzy clustering algorithm and the weighted least square method. Because the each rule of T-S fuzzy model is a linear model, the entire model may be regarded as a time-dependent system. Using the T-S fuzzy model to the predictive functional control, it solves the nonlinear optimization problem. Simulation results show that the algorithm has good control effect.

引用

下载

页码：227 / 230

页数：4

共 6 条

[1] Gustafson D. E., 1979, Proceedings of the 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, P761
[2] Fuzzy model-based predictive control using Takagi-Sugeno models
Roubos, JA
Mollov, S
Babuska, R
Verbruggen, HB
[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 1999, 22 (1-2) : 3 - 30
[3] FUZZY IDENTIFICATION OF SYSTEMS AND ITS APPLICATIONS TO MODELING AND CONTROL
TAKAGI, T
SUGENO, M
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1985, 15 (01): : 116 - 132
[4] Wang Yin, 2002, Control Theory & Applications, V19, P599
[5] Zhang Quan-Ling, 2000, Information and Control, V29, P431
[6] ZHU J, 2002, INTELLIGENCE FORCAST

← 1 →