Direct adaptive fuzzy controller with small number of fuzzy rules for nanaffine nonlinear system

2004 ◽  
Author(s):  
Jang-Hyun Park ◽  
Seong-Hwan Kim ◽  
Dong-Won Kim ◽  
Gwi-Tae Park
Keyword(s):  
Nonlinear System ◽  
Fuzzy Controller ◽  
Fuzzy Rules ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

Observer Based Adaptive Fuzzy Controller with Modulated Membership Functions for Nonlinear System

2016 ◽  
Vol 24 (01) ◽  
pp. 137-159 ◽  
Author(s):  
Leehter Yao ◽  
Jen-nan Jiang ◽  
Tung-bin Lin
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Supervisory Control ◽  
Fuzzy Controller ◽  
Optimal Solutions ◽  
Membership Functions ◽  
Lyapunov Theorem ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
On Line

An observer based adaptive fuzzy controller for nonlinear system is proposed. Parameters in the proposed adaptive fuzzy controller are tuned on-line by the genetic algorithm (GA). For on-line tuning, a parsimonious parameterization scheme for fuzzy controller called orthogonal modulated membership functions (mmf)35 is utilized. A simplified GA called micro GA that greatly improves the learning efficiency is applied. The valid range of mmf parameters will be proved in this paper. With the valid range of mmf parameters, the search by MGA for the optimal parameterization of the adaptive fuzzy controller is more focused resulting in fast convergence to the optimal solutions. A Lyapunov theorem based supervisory control is added to the fuzzy controller assuring that close loop stability is always maintained for the adaptive fuzzy controller during the learning process.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

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