Publication detail

Design of fuzzy logic controller for DC motor

ANDRŠ, O. BŘEZINA, T. KOVÁŘ, J.

Original Title

Design of fuzzy logic controller for DC motor

Czech Title

Design of fuzzy logic controller for DC motor

English Title

Design of fuzzy logic controller for DC motor

Type

conference paper

Language

en

Original Abstract

This contribution presents a simple and efficient approach to the fuzzy logic controller design and simulation. The proposed controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design meth-od is based on anfis (adaptive neuro-fuzzy inference system) training rou-tine. It utilizes a combination of the least-squares method and the back-propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controller is used for the position control of a linear actuator which is a part of a Stewart platform.

Czech abstract

This contribution presents a simple and efficient approach to the fuzzy logic controller design and simulation. The proposed controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design meth-od is based on anfis (adaptive neuro-fuzzy inference system) training rou-tine. It utilizes a combination of the least-squares method and the back-propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controller is used for the position control of a linear actuator which is a part of a Stewart platform.

English abstract

This contribution presents a simple and efficient approach to the fuzzy logic controller design and simulation. The proposed controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design meth-od is based on anfis (adaptive neuro-fuzzy inference system) training rou-tine. It utilizes a combination of the least-squares method and the back-propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controller is used for the position control of a linear actuator which is a part of a Stewart platform.

Keywords

fuzzy logic, controller

RIV year

2011

Released

21.09.2011

Publisher

Springer

Location

Varšava

ISBN

978-3-642-23243-5

Book

Mechatronics Recent Technological and Scientific Advances

Edition

1

Edition number

1

Pages from

9

Pages to

18

Pages count

10

BibTex


@inproceedings{BUT73751,
  author="Ondřej {Andrš} and Tomáš {Březina} and Jiří {Kovář}",
  title="Design of fuzzy logic controller for DC motor",
  annote="This contribution presents a simple and efficient approach to the fuzzy logic controller design and simulation. The proposed controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design meth-od is based on anfis (adaptive neuro-fuzzy inference system) training rou-tine. It utilizes a combination of the least-squares method and the back-propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controller is used for the position control of a linear actuator which is a part of a Stewart platform.",
  address="Springer",
  booktitle="Mechatronics Recent Technological and Scientific Advances",
  chapter="73751",
  edition="1",
  howpublished="print",
  institution="Springer",
  year="2011",
  month="september",
  pages="9--18",
  publisher="Springer",
  type="conference paper"
}