Hybrid Heuristic-based Fuzzy Gain Scheduling PI Controller for Speed Control and Torque Ripple Reduction of SRM

Abstract

Switched Reluctance Motor (SRM) use current control approaches at low voltage and speed control algorithms at faster speed. Though, the existing approaches often fail for restraining the torque ripple that is generally correlated through SRM machine. The control strategies consider the electro-mechanical behavior with non-linear nature of SRM that is attained using the intelligent choice of diverse operating parameters like operating current and voltage, turn off and on angle and shaft load. In existing studies, different approaches have aimed to reduce the torque ripple by adapting the torque profile, which results in modulating the current waveform. This paper is to plan for the speed control with torque ripple reduction model based on the Hybrid Heuristic-based Fuzzy Gain Scheduling PI Controller (HH-FGS-PI). The developed HH-FGS fine tunes the parameters of PI controller. Here, the hybrid heuristic approach solves the multi-objective function concerning with the speed and torque ripple of SRM by optimizing the significant parameters like input parameters of fuzzy (error and differential error), and the output parameters of fuzzy (proportional gain, and integral gain). The hybrid algorithm termed Electric Fish-Colliding Bodies Optimization (EF-CBO) algorithm is proposed by integrating the Colliding Bodies Optimization (CBO) and Electric Fish Optimization (EFO) algorithms for enhancing the performance of FGS to control the PI controllers. Finally, the comparative analysis over the existing models confirms the helpfulness of the proposed HH-FGS-PI for reduction of speed and torque ripple in FGS.