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Published on: September 11, 2019
Identification of uncertain nonlinear systems for robust fuzzy control.
D Senthilkumar1, Chitralekha Mahanta
1Department of Electronics and Communication Engineering, Indian Institute of Technology, Guwahati, Guwahati-781039, Assam, India. d.senthil@iitg.ernet.in
ISA Transactions
|August 18, 2009
Summary
This study presents a new fuzzy identification method for uncertain nonlinear systems. The approach, based on linear programming, offers less conservative results for robust fuzzy control design.
Area of Science:
- Control Engineering
- Systems Science
- Fuzzy Logic
Background:
- Uncertain nonlinear systems pose challenges for robust control design.
- Takagi-Sugeno (T-S) fuzzy models are widely used for representing such systems.
- Existing identification methods can be overly conservative.
Purpose of the Study:
- To develop a novel fuzzy identification method for uncertain nonlinear systems in T-S form.
- To enable robust fuzzy control design by accurately modeling system uncertainties.
- To reduce conservatism compared to existing techniques.
Main Methods:
- Utilizing a linear programming approach for fuzzy model identification.
- Identifying the nominal model and bounds of time-varying uncertain matrices.
- Representing uncertain matrices as norm-bounded matrices with constant components.
Main Results:
- The proposed method provides less conservative identification results than prior work.
- Successfully identified fuzzy models for uncertain nonlinear systems.
- Demonstrated robust stability conditions based on the identified fuzzy models.
Conclusions:
- The developed fuzzy identification method is effective for uncertain nonlinear systems.
- The approach facilitates robust fuzzy control design with reduced conservatism.
- Simulation examples validate the utility of the method for identification and control.
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