Paper Info
Title
State Feedback Controller Design via T-S Fuzzy Model
Abstract
In this paper, a state feedback controller is designed for a non-linear system using a technique in which, the fuzzy model consists of a set of linear fuzzy local models, combined to get overall fuzzy inference mechanism. This approach utilizes a certain type of fuzzy systems that are based on Takagi-Sugeno (T-S) fuzzy models to approximate nonlinear systems. A fuzzy inference mechanism is designed by the integration of these local models to give overall fuzzy model of the system. The proposed control algorithm employs the fuzzy controller that was designed by considering the linear state feedback controller for each of the fuzzy local model. Individual firing strength amongst that of different rules is used for the design of gain matrix of state feedback controller. The output of the FLC, which is a function of the degrees of membership of the fuzzy rules, is made by weighted average defuzzification method. In this paper, sufficient condition for the stability of T-S fuzzy system is investigated for the non-linear system which has complex stability analysis by obtaining a common positive definite matrix P that would satisfy a set of Lyapunov inequalities. To show the effectiveness of the proposed technique, inverted pendulum, a well known non-linear system is used.
Year
DOI
Venue
2009
10.1109/FSKD.2009.825
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference
Keywords
Field
DocType
Lyapunov matrix equations,control system synthesis,fuzzy control,fuzzy set theory,linear systems,nonlinear control systems,stability,state feedback,Lyapunov inequalities,T-S fuzzy system stability,Takagi-Sugeno fuzzy model,fuzzy inference mechanism,gain matrix,inverted pendulum,linear fuzzy local model,linear state feedback controller design,nonlinear control system,weighted average defuzzification method,Takagi-Sugeno (T-S) fuzzy models,linear state feedback controller
Mathematical optimization,Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Control theory,Adaptive neuro fuzzy inference system,Fuzzy control system,Fuzzy associative matrix,Fuzzy number
Conference
Volume
ISBN
Citations 
4
978-0-7695-3735-1
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Bhasker Sharma100.34
Neeta Awasthy200.34