Genetic-based fuzzy clustering for DC-motor friction identification and compensation

Antonios Tzes, Pei Yuan Peng, John Guthy

Research output: Contribution to journalArticle

Abstract

A fuzzy-logic-based model describing the friction present in a dc-motor system is derived in this paper. Based on fuzzy clustering techniques, the structure, as well as the premise and consequence parameters are inferred in an off-line manner. The fine-tuning of these parameters is accomplished through a genetic algorithm which minimizes a system modeling relevant functional. This genetic algorithm encodes these parameters as chromosomes, and creates the next generation of fuzzy models through natural selection and survival of the fittest chromosome. This model is used as a feedforward term for tracking purposes of the dc-motor's angular velocity. The proposed feedforward compensation scheme, coupled to a classical feedback controller improves the system's response in typical dc-motor micromaneuvers. Experimental results are offered to validate the performance of the proposed friction's fuzzy model and the control technique.

Original languageEnglish (US)
Pages (from-to)462-472
Number of pages11
JournalIEEE Transactions on Control Systems Technology
Volume6
Issue number4
DOIs
StatePublished - Dec 1 1998

Fingerprint

DC motors
Fuzzy clustering
Identification (control systems)
Friction
Chromosomes
Genetic algorithms
Angular velocity
Fuzzy logic
Tuning
Feedback
Controllers
Compensation and Redress

Keywords

  • Fuzzy logic
  • Genetic algorithms
  • Mechanical factors
  • Motor drives
  • Variable structure systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Genetic-based fuzzy clustering for DC-motor friction identification and compensation. / Tzes, Antonios; Peng, Pei Yuan; Guthy, John.

In: IEEE Transactions on Control Systems Technology, Vol. 6, No. 4, 01.12.1998, p. 462-472.

Research output: Contribution to journalArticle

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