Detail publikace

Design of fuzzy logic controller for DC motor

Originální název

Design of fuzzy logic controller for DC motor

Anglický název

Design of fuzzy logic controller for DC motor

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}