ANFIS Based Speed and Current Controller for Switched Reluctance Motor

Switched Reluctance motor (SRM) are accepting critical consideration from industries, due to its straight forward structure; low-cost manufactur ability and dependability make it better than other electric machines. For adjustable speed tasks, the drive of SRM is most applicable. Additionally, the performance of the current and speed controller for SRM driver framework can be negatively influenced by noise, disturbances, and inactivity of load torque. To solve this difficulty, this paper developed an ANFIS based speed and current controller for SRM. The key objective of this article is to obtain the preferred speed and current performance of switched reluctance motor. The Neuro-fuzzy structure includes the assistance of both neural-systemand fuzzy scheme. Adaptive Neuro-Fuzzy Inference System(ANFIS)controller is more efficient than the neural system and fuzzy logic-based controller. In designed ANFIS method for speed and current control of SRM, the neural network methodology is applied for a selection of suitable rule base with the support of the back propagation (BP) algorithm. Finally, the experimental outcomes show that the effectiveness in the aspects of better speed and current performance compared to other controllers.