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Identification and control of induction machines using artificial neural networks

Wishart, M. T. & Harley, R. G. (1993) Identification and control of induction machines using artificial neural networks. In Conference Record of the 1993 IEEE Industry Applications Society Annual Meeting, 1993.

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    Abstract

    The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

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    ID Code: 37933
    Item Type: Conference Paper
    Keywords: adaptive control, electric current control, identification, induction motors, machine control, nonlinear control systems, velocity control, artificial neural networks, current controller, induction motor control, nonlinear controller, rotor speed control, standard vector control scheme, stator currents control
    Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Industrial Electronics (090603)
    Copyright Owner: Copyright 1993 IEEE
    Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Deposited On: 14 Oct 2010 11:20
    Last Modified: 11 Aug 2011 00:31

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