Stochastic and neural models of an induction motor

The input/output representations of non-linear discrete time MISO-systems of an induction motor are discussed. To acquire the system data for estimation the motor was stimulated by a random binary speed reference signal. Different stochastic model structures (ARMAX and LARMAX) based on local models have been tested. Furthermore, two kinds of artificial Neural Networks, the Multilayer Perceptron Network (MLP) and the Radial Basis Function Network (RBF) have been used to model the process dynamics. Finally the model structures are compared with regard to the modeling validity and the computational expense on a transputer system.