Soft computing approaches of modeling and control of a DC machine

This paper deals with modelling and control of a DC motor using soft computing techniques namely the exploitation of artificial neural networks and fuzzy logic controller. Direct and inverse neural models have been created in the case of the speed and the position responses. In order to validate the two proposed models, a cascading of the direct and inverse models is developed. The obtained simulations show a satisfactory agreement between responses of the developed neural model and the simulation signal. For the control of the position of the DC motor a fuzzy logic controller is proposed.