A methodology for characterizing fault tolerant switched reluctance motors using neurogenetically derived models

This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched-reluctance-motor (SRM) drive systems. The results of using the ANN-GA-based (neurogenetic) model to predict the performance characteristics of a prototype SRM drive motor under normal and abnormal operating conditions are presented and verified by comparison to test data.