Tracking control of uncertain DC server motors using genetic fuzzy system

Authors:
Wei-Min Hsieh;Yih-Guang Leu;Hao-Cheng Yang;Jian-You Lin
Affiliations:
Department of Electronic Engineering, Hwa Hsia Institute of Technology;Department of Applied Electronics Technology, National Taiwan Normal University;Department of Applied Electronics Technology, National Taiwan Normal University;Department of Applied Electronics Technology, National Taiwan Normal University
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 9
Cited 0

Neurofuzzy adaptive modelling and control

Neurofuzzy adaptive modelling and control
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems

Neurocomputing
GA-based fuzzy reinforcement learning for control of a magneticbearing system

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Selection of relevant features in a fuzzy genetic learningalgorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic optimal learning rates of a certain class of fuzzy neuralnetworks and its applications with genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic algorithm-based optimal fuzzy controller design in the linguistic space

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A controller of uncertain DC server motor is presented by using the fuzzy system with a real-time genetic algorithm The parameters of the fuzzy system are online adjusted by the real-time genetic algorithm in order to generate appropriate control input For the purpose of on-line evaluating the stability of the closed-loop system, an energy fitness function derived from backstepping technique is involved in the genetic algorithm According to the experimental results, the genetic fuzzy control scheme performs on-line tracking successfully.