An experiment in automatic modeling an electrical drive system using fuzzy logic

Electrical drives are usually modeled using circuit theory, with currents or linking fluxes chosen as state variables for its electrical part and rotor speed or position chosen for its mechanical part. Often, its internal structure contains nonlinear relations which are difficult to model such as dead-time, hysteresis, and saturation effects. On the contrary, if the available model is accurate enough, its parameter values are generally difficult to obtain and/or be estimated in real time. Therefore, the paper investigates the use of fuzzy logic for the automatic modeling of electrical drive systems. An experimental system composed of a DC motor supplied from a DC-DC converter is used. The authors underline the unsupervised learning characteristics of the fuzzy algorithm, its memory and generalization capabilities. Some learning situations with critical effects on model performance are presented and discussed, pointing out some results and conclusions concerning the fuzzy modeling process in practice