Nonlinear Modelling of Switched Reluctance Motors Using Soft Computing Techniques

摘要

Switched Reluctance Motors (SRM) is almost always operated within the saturation region for very large operation region. This yields very strong non linearity, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This study develops and compares fuzzy logic, neuro- fuzzy logic and neural network techniques for the modelling of a Switched Reluctance Motor (SRM) in view of its nonlinear magnetisation characteristics. All the models are simulated and applied for nonlinear modelling, especially for finding the rotor angle positions at different currents, from a suitable measured data set for an associated SRM. The data comprised flux linkage, current and rotor position. The model evaluation results are compared with the measured values and the error analyses are given to determine the performance of the developed model. The error analyses have shown great accuracy and successful modelling of SRMs using soft computing techniques.