NEURAL NETWORKS FOR LINEAR-CONTROL - AN ANALYSIS

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback cont rol schemes in a discrete-time environment. A close look is taken at t he model structure selected and the extent of the resulting parameteri zation. A comparison is made with standard, nonneural network algorith ms, e.g. self-tuning control.