scholarly journals Nonlinear Model Following Control via Takagi-Sugeno Fuzzy Model

1999 ◽  
Vol 3 (2) ◽  
pp. 68-74 ◽  
Author(s):  
Tadanari Taniguchi ◽  
◽  
Kazuo Tanaka
Keyword(s):  
Nonlinear Model ◽  
Fuzzy Controller ◽  
Reference Model ◽  
Fuzzy Model ◽  
Feedback System ◽  
Linear Matrix ◽  
Model Following Control ◽  
Model Following ◽  
Special Cases ◽  
Takagi Sugeno

This paper presents a unified approach toward regulation and servocontrol problems as special cases of a nonlinear model following control via the Takagi-Sugeno fuzzy model. New parallel distributed compensation (PDC) is presented for realizing a nonlinear model following control. The new PDC fuzzy controller mirrors the structures of two Takagi-Sugeno fuzzy models representing a nonlinear system and nonlinear reference model. First, we derive linear matrix inequality (LMI) conditions to linearize the error system between the feedback system and the nonlinear reference model. A controller is designed using LMI conditions. Design examples verify the usefulness of nonlinear model following control.

Nonlinear model-following control application to airplane control

1994 ◽  
Vol 17 (3) ◽  
pp. 570-577 ◽  
Author(s):  
Wayne C. Durham ◽  
Frederick H. Lutze ◽  
M. Remzi Barlas ◽  
Bruce C. Munro
Keyword(s):  
Nonlinear Model ◽  
Model Following Control ◽  
Model Following ◽  
Control Application

scholarly journals Fuzzy Model Following Control

2000 ◽  
Vol 36 (2) ◽  
pp. 204-210 ◽  
Author(s):  
Tadanari TANIGUCHI ◽  
Kazuo TANAKA
Keyword(s):  
Fuzzy Model ◽  
Model Following Control ◽  
Model Following

scholarly journals Adaptive Control of MEMS Gyroscope Based on T-S Fuzzy Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yunmei Fang ◽  
Shitao Wang ◽  
Juntao Fei ◽  
Mingang Hua
Keyword(s):  
Nonlinear Model ◽  
Reference Model ◽  
Fuzzy Model ◽  
Lyapunov Theory ◽  
Mems Gyroscope ◽  
Adaptive Law ◽  
Control Scheme ◽  
Estimation Scheme ◽  
Linear State ◽  
Takagi Sugeno

A multi-input multioutput (MIMO) Takagi-Sugeno (T-S) fuzzy model is built on the basis of a nonlinear model of MEMS gyroscope. A reference model is adjusted so that a local linear state feedback controller could be designed for each T-S fuzzy submodel based on a parallel distributed compensation (PDC) method. A parameter estimation scheme for updating the parameters of the T-S fuzzy models is designed and analyzed based on the Lyapunov theory. A new adaptive law can be selected to be the former adaptive law plus a nonnegative in variable to guarantee that the derivative of the Lyapunov function is smaller than zero. The controller output is implemented on the nonlinear model and T-S fuzzy model, respectively, for the purpose of comparison. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme and the correctness of the T-S fuzzy model.

Sign in / Sign up

Export Citation Format

Share Document