Closed-loop Identification of Hammerstein Systems Using Hybrid Neural Model Identified by Genetic Algorithms

In this paper we present an approach for the closed loop identification of Hammerstein systems. In this approach we propose modelling the system to be identified by a hybrid neural model, which is composed of a neural network (NN), connected in series with a linear model. To optimize the proposed model, genetic algorithms are used. The system to be identified is in closed-loop with variable structure controller (CSV) in order to have a command signal rich in commutations and consequently a good identification. A simulation example is given in order to show the effectiveness of the proposed approach