Modeling of Fuzzy Control Design for Nonlinear Systems Based on Takagi-Sugeno Method

被引：0

作者：

Tsai, Pu-Sheng ^{[1
]}

Wu, Ter-Feng ^{[2
]}

Hu, Nien-Tsu ^{[3
]}

Chen, Jen-Yang ^{[4
]}

机构：

[1] China Univ Sci & Technol, Dept Elect Engn, Taipei 11581, Taiwan

[2] Natl Ilan Univ, Dept Elect Engn, Ilan 26047, Taiwan

[3] Chung Shan Inst Sci & Technol, Integrated Logist Support Ctr, Taoyuan 32599, Taiwan

[4] Ming Chuan Univ, Dept Elect Engn, Taoyuan 33348, Taiwan

来源：

2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014) | 2014年

关键词：

Takagi-Sugeno fuzzy systems; nonlinear dynamic systems; fuzzy modeling; LYAPUNOV STABILITY; IDENTIFICATION;

D O I：

10.1109/IIH-MSP.2014.243

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we first develop a procedure for constructing Takagi-Sugeno fuzzy systems from input-output pairs to identify nonlinear dynamic systems. The fuzzy system can approximate any nonlinear continuous function to any arbitrary accuracy that is substantiated by the Stone Weierstrass theorem. A learning-based algorithm is proposed in this paper for the identification of T-S (Takagi-Sugeno) models. Our modeling algorithm contains four blocks: fuzzy C-Mean partition block, LS coarse tuning, fine turning by gradient descent, and emulation block. The ultimate target is to design a fuzzy modeling to meet the requirements of both simplicity and accuracy for the input-output behavior.

引用

页码：970 / 973

页数：4

共 10 条

[1] Lyapunov stability of complementarity and extended systems [J].

Camlibel, M. Kanat ;

Pang, Jong-Shi ;

Shen, Jingali .

SIAM JOURNAL ON OPTIMIZATION, 2006, 17 (04) :1056-1101

[2]

Cotter N E, 1990, IEEE Trans Neural Netw, V1, P290, DOI 10.1109/72.80265

[3] Adaptive fuzzy control of uncertain stochastic nonlinear systems with unknown dead zone using small-gain approach [J].

Li, Yongming ;

Tong, Shaocheng ;

Li, Tieshan ;

Jing, Xingjian .

FUZZY SETS AND SYSTEMS, 2014, 235 :1-24

[4]

Soltanpour MR, 2013, INT J INNOV COMPUT I, V9, P2713

[5] SUCCESSIVE IDENTIFICATION OF A FUZZY MODEL AND ITS APPLICATIONS TO PREDICTION OF A COMPLEX SYSTEM [J].

SUGENO, M ;

TANAKA, K .

FUZZY SETS AND SYSTEMS, 1991, 42 (03) :315-334

[6] FUZZY IDENTIFICATION OF SYSTEMS AND ITS APPLICATIONS TO MODELING AND CONTROL [J].

TAKAGI, T ;

SUGENO, M .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1985, 15 (01) :116-132

[7] FUZZY BASIS FUNCTIONS, UNIVERSAL APPROXIMATION, AND ORTHOGONAL LEAST-SQUARES LEARNING [J].

WANG, LX ;

MENDEL, JM .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1992, 3 (05) :807-814

[8] GENERATING FUZZY RULES BY LEARNING FROM EXAMPLES [J].

WANG, LX ;

MENDEL, JM .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1992, 22 (06) :1414-1427

[9] Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation [J].

Wu, T. -F. ;

Tsai, P. -S. ;

Chang, F. -R. ;

Wang, L. -S. .

IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS, 2006, 153 (06) :647-657

[10] Lyapunov Stability and Strong Passivity Analysis for Nonlinear Descriptor Systems [J].

Yang, Chunyu ;

Sun, Jing ;

Zhang, Qingling ;

Ma, Xiaoping .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2013, 60 (04) :1003-1012

← 1 →