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An introduction to the use of neural networks in control systems
Author(s) -
Hagan Martin T.,
Demuth Howard B.,
Jesús Orlando De
Publication year - 2002
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.727
Subject(s) - overfitting , perceptron , artificial neural network , computer science , generalization , artificial intelligence , control engineering , backpropagation , control (management) , linearization , control system , model predictive control , machine learning , control theory (sociology) , engineering , nonlinear system , mathematics , mathematical analysis , physics , electrical engineering , quantum mechanics
The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system. Copyright © 2002 John Wiley & Sons, Ltd.