A novel approach to adapting the weights of a CMAC neural network for torque ripple reduction in switched reluctance motors is proposed, using a variable learning rate function within the standard LMS algorithm. Simulation results demonstrate that training CMAC networks following this approach affords low torque ripple with high power efficiency.
|Number of pages||3|
|Publication status||Published - 6 Jun 1996|
- Reluctance motors