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Proceedings Paper

Neuro-vector-based electrical machine driver combining a neural plant identifier and a conventional vector controller
Author(s): Kurosh Madani; Gilles Mercier; Mohammad Dinarvand; Jean-Charles Depecker
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Paper Abstract

One of the most important problems, for a machine control process is the system identification. To identify varying parameters which are dependent from other system's parameters (speed, voltage and currents, etc.), one must have an adaptive control system. Synchronous machines conventional vector control's implementation using PID controllers have been recently proposed presenting the best actual solution. It supposes an appropriated model of the plant. But real plant's parameters vary and the P.I.D. controller is not suitable because of the parameters variation and non-linearity introduced by the machine's physical structure. In this paper, we present an on-line dynamic adaptive neural based vector control system identifying the motor's parameters of a synchronous machine. We present and discuss a DSP based real- time implementation of our adaptive neuro-controller. Simulation and experimental results validating our approach have been reported.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342905
Show Author Affiliations
Kurosh Madani, Univ. Paris XII (France)
Gilles Mercier, Univ. Paris XII (France)
Mohammad Dinarvand, Univ. Paris XII (France)
Jean-Charles Depecker, Univ. Paris XII (France)

Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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