New fuzzy inference method for symbolic stability analysis of fuzzy control system

2000 ◽  
Author(s):  
H. Yamamoto ◽  
T. Furnhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Inference Method ◽  
Fuzzy Control System

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

2000 ◽  
Vol 12 (6) ◽  
pp. 664-674
Author(s):  
Hidehiro Yamamoto ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Continuous System ◽  
Asymptotically Stable ◽  
Membership Functions ◽  
Symbolic System ◽  
Inference Method

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Study on Adaptive Fuzzy Control System of Settlement Technology

2014 ◽  
Vol 1061-1062 ◽  
pp. 904-907
Author(s):  
Xiang Ping Chen
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Control Method ◽  
Dynamic Control ◽  
Adaptive Fuzzy Control ◽  
Fuzzy Control System ◽  
Adaptive Fuzzy ◽  
The Mathematical Model ◽  
Clear Solution

Considering the production status of red mud at present, an adaptive fuzzy control system, according to fuzzy control and genetic algorithm, has been focused on. With the control of flocculants, the system fuzzy control clarity of clear solution. Adaptive neural network fuzzy inference theory is adopted to establish the mathematical model of controlled object "black box", and MATLAB for simulation, showing that the control method has good accuracy and dynamic control quality. Satisfy the requirements of practice work.

Stability Analysis of Nonlinear Dynamic Systems by Nonlinear Takagi–Sugeno–Kang Fuzzy Systems

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Zahra Namadchian ◽  
Assef Zare ◽  
Ali Namadchian
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Limit Cycle ◽  
Phase Margin ◽  
Fuzzy Control System ◽  
Gain Margin ◽  
The Stability ◽  
Takagi Sugeno ◽  
System 1

This paper proposes a systematic procedure to address the limit cycle prediction of a Nonlinear Takagi–Sugeno–Kang (NTSK) fuzzy control system with adjustable parameters. NTSK fuzzy can be linearized by describing function method. The stability of the equivalent linearized system is then analyzed using the stability equations and the parameter plane method. After that the gain–phase margin (PM) tester has been added, then gain margin (GM) and phase margin for limit cycle are analyzed. Using NTSK fuzzy control system can help to have fewer rules. In order to analyze the stability with the same technique of stability analysis, the results of NTSK fuzzy control system will be compared with Dynamic fuzzy control system [1]. Computer simulations show differences between both systems.

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