Neural Networks for Control
Martin T. Hagan School of Electrical & Computer Engineering Oklahoma State University firstname.lastname@example.org
Howard B. Demuth Electrical Engineering Department University of Idaho email@example.com
The purpose of this tutorial 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 en- sure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are dis- cused. The tutorial also presents several control ar- chitectures, such as model reference adaptive control, model predictive control, and internal model control, in which multilayer perceptron neural net- works can be used as basic building blocks.