By Topic

On-line adaptive neural training algorithm for an induction motor flux observer

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Nied, A. ; Dept. of Electron. Eng., Minas Gerais Fed. Univ., Belo Horizonte ; Junior, S.I.S. ; Parma, G.G. ; Menezes, B.R.

This paper presents a new algorithm for induction motor stator flux observation. The novel procedure is based on a neural network with on-line adaptive training. The network topology is a standard multilayer perceptron (MLP) network and the training algorithm is based on sliding mode control (SMC) theory. The main characteristic of this novel observer is the adaptability of the gain (learning rate), which is obtained from sliding surface so that system stability is guaranteed. The neural network stator flux observer employed here does not require previous training or speed measurement. The on-line adaptive training algorithm for the neural network is described, as well as its application to a stator flux observer of an induction motor drive. Neural observer performance is demonstrated by simulations results

Published in:

Power Electronics Specialists Conference, 2005. PESC '05. IEEE 36th

Date of Conference:

16-16 June 2005