Fuzzy neural network control for dc-motor micromaneuvering

Antonios Tzes, Pei Yuan Peng

Research output: Contribution to journalArticle

Abstract

The application of a fuzzy neural network controller for compensatingthe effects induced by the friction in a DC-motormicromaneuvering system is considered in this article. A backpropagationneural network is employed to decrease the effectsof the system nonlinearities. The input vector to the neuralnetwork controller consists of the time history of the motorangular shaft velocity within a prespecified time window. Afuzzy cell space controller supervises the overall scheme andreduces the amplitude and repetitions of control switchings.Simulation studies are presented to indicate the effectiveness ofthe proposed algorithm.

Original languageEnglish (US)
Pages (from-to)312-315
Number of pages4
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume119
Issue number2
DOIs
StatePublished - Jan 1 1997

Fingerprint

network control
Fuzzy neural networks
controllers
Controllers
Control nonlinearities
repetition
friction
direct current
nonlinearity
histories
Friction
cells
simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Fuzzy neural network control for dc-motor micromaneuvering. / Tzes, Antonios; Peng, Pei Yuan.

In: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 119, No. 2, 01.01.1997, p. 312-315.

Research output: Contribution to journalArticle

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