Dynamical system studies and analysis can only be achieved if the mathematical model of the system is known. This model can be constructed using the physical modeling approach which leads to the white box models. This approach consists in decomposing the considered system in different parts (electrical part, mechanical part, heat conduction part, etc.) and then applying the appropriate first principles that govern each part. This approach can clearly be very complex since it requires detailed specialist knowledge in many different domains of science. In addition, the first principle models are often non linear

and non stationary and consequently the obtained models are too complex for most practical purposes. System identification represents an interesting solution of constructing models, known as black box models, since it allows to overcome most of these problems. Furthermore, it can be applied to all physical systems. This approach consists in constructing mathematical models from observed input and output data based on five main steps : data collection, model class selection, model structure estimation , model parameter estimation and model validation. The model class selection represents the corner stone toward the objective of system identification because the model accuracy depends on the used model class.