A Multi-objective Optimization of Switched Reluctance Motor using a Hybrid Analytic-ANFIS Model Considering the Vibrations

Switched reluctance motors have a rugged construction which makes them suitable for many applications. But, high torque ripple, vibration, and acoustic noise are the main drawbacks of them. In this paper, the vibration is considered in optimization besides the other desired considerations. The radial force is the main origin of vibration in switched reluctance motor, and a torque-to-force ratio is introduced for considering the low vibration in optimization. Also, modeling of switched reluctance motor is somehow challenging. In this paper, a hybrid method based on analytic and adaptive-neuro-fuzzy inference system methods is presented for modeling SRM and the obtained static characteristics are used in a dynamic simulation method. Discussion on simulation results is presented at the final part of the paper which shows the lower vibrations lead to bigger air gap length; so, to prevent the deterioration of other features, an optimization should be done. A multi-objective particle swarm optimization is used to find the dominant design, and the best design is obtained by means of weighted cost functions.