Robust control of induction motor with a neural-network load torqueestimator and a neural-network identification
Chich-Yi Huang
Tien-Chi Chen
Ching-Lien Huang
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Oct 1999
Volume: 46,
Issue: 5
On page(s): 990-998
ISSN: 0278-0046
References Cited: 15
CODEN: ITIED6
INSPEC Accession Number: 6383641
Digital Object Identifier: 10.1109/41.793348
Current Version Published: 2002-08-06
Abstract
This paper presents a control scheme for an induction motor drive
which consists of a compensator, neural network identification (NNI),
and neural network load torque estimator (NNLTE) based on the
conventional proportional-integral controller. The NNI is a two-layer
neural network which uses a projection algorithm to estimate the
parameters of the induction motor and to regulate the gain of the
compensator such that the response of the induction motor follows that
of the nominal plant. The NNLTE is a two-layer neural network which uses
the steepest descent algorithm to estimate the load disturbance and
forward feed, resulting in equivalent control such that the speed
response of the induction motor is robust against the load disturbance.
Computer simulations and experimental results demonstrate that the
proposed control scheme can obtain a robust speed control
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