NDLI: State Feedback Controller Design via T-S Fuzzy Model

Content Provider	IEEE Xplore Digital Library
Author	Sharma, B. Awasthy, N.
Copyright Year	2009
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.
Starting Page	176
Ending Page	181
File Size	353864
Page Count	6
File Format	PDF
ISBN	9780769537351
DOI	10.1109/FSKD.2009.825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Takagi-Sugeno (T-S) fuzzy models State feedback Fuzzy control Fuzzy sets Inference mechanisms Linear feedback control systems linear state feedback controller Nonlinear control systems Stability analysis Takagi-Sugeno model Nonlinear systems Fuzzy systems
Content Type	Text
Resource Type	Article

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