Fuzzy neural network control for a single flexible-link manipulator

Antonios Tzes, Pei Yuan Peng, Farshad Khorrami

Research output: Contribution to journalArticle

Abstract

In this article, a fuzzy neural network controller for a single flexible-link manipulator is considered. A backpropagation neural network operating in the specialized learning mode is employed to decrease the effects of the inherent system nonlinearities, like the motor static friction and the saturation of the electronic amplifier. The neural network output resembles that of a Pulse Width Modulated controller. A fuzzy cell space controller supervises the overall scheme and reduces the amplitude and repetitions of control switchings. The fuzzy controller rules are extracted from a rulebase parameterized in terms of the 12 and Ix norms of the output error. Simulation studies are presented to indicate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Pages (from-to)319-334
Number of pages16
JournalJournal of Intelligent and Fuzzy Systems
Volume1
Issue number4
DOIs
StatePublished - 1993

Fingerprint

Neural Network Control
Fuzzy neural networks
Fuzzy Neural Network
Manipulator
Manipulators
Controller
Controllers
Switching Control
Rule Base
Back-propagation Neural Network
Output
Fuzzy Controller
Neural networks
Saturation
Friction
Backpropagation
Simulation Study
Electronics
Nonlinearity
Neural Networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Engineering(all)
  • Statistics and Probability

Cite this

Fuzzy neural network control for a single flexible-link manipulator. / Tzes, Antonios; Peng, Pei Yuan; Khorrami, Farshad.

In: Journal of Intelligent and Fuzzy Systems, Vol. 1, No. 4, 1993, p. 319-334.

Research output: Contribution to journalArticle

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