Identification of uncertain nonlinear systems for robust fuzzy control

In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi–Sugeno (T–S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe t...

Full description

Saved in:
Bibliographic Details
Published in:ISA transactions Vol. 49; no. 1; pp. 27 - 38
Main Authors: Senthilkumar, D., Mahanta, Chitralekha
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 2010
Elsevier
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Computer science; control theory; systems
Control system analysis
Control system synthesis
Control theory. Systems
Data Interpretation, Statistical
Electronics
Exact sciences and technology
Fuzzy Logic
Interval fuzzy model
Linear programming
Modelling and identification
Nonlinear Dynamics
Parametric uncertainties
Takagi–Sugeno (T–S) fuzzy model
Uncertainty
Linear programming
Takagi–Sugeno (T–S) fuzzy model
Interval fuzzy model
Parametric uncertainties
Robust stability
Non linear control
Control synthesis
Non linear system
Modeling
Robust control
Fuzzy logic
Fuzzy control
Time varying system
Uncertain system
Robustness
System identification
Time constant
Takagi-Sugeno (T-S) fuzzy model
ISSN:0019-0578, 1879-2022, 1879-2022
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi–Sugeno (T–S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by S˘krjanc et al. (2005)  [11,12]. With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2009.07.005