Speed Control of Switched Reluctance Motor Using New Hybrid Particle Swarm Optimization

A A

doi:10.3844/jcssp.2012.1473.1477

Abstract

Problem statement: The main objective of this research is to obtain the speed control of switched reluctance motor with minimum settling time and without overshoot. Approach: A new algorithm has been developed with the combination of differential evolution and particle swarm optimization and applied for speed control of switched reluctance motor under sudden change in speed. Also speed control of switched reluctance motor was obtained by other artificial intelligence methods such as fuzzy logic controller, fuzzy PI controller and particle swarm optimization based tuning of fuzzy PI controller. Matlab/Simulink environment was used for the simulation. Results: Results are discussed and tabulated based on the performance of the controllers. Conclusion: From the comparison of all above methods, the algorithm has given better results in speed response than other controllers.

Highlights

Most of the manufacturing units in the world depend on electric motors for their production
The introduction of artificial intelligence applications requiring high performances such as (Krohling et al, 1997; Sousa and Bose, 1994) has variable speed control, servo motor drive, jet engine brought a new era in the industrial drive
The performance of the proposed controllers are investigated using simulation and simulation is applied to switched reluctance motor

Summary

Introduction

Most of the manufacturing units in the world depend on electric motors for their production. The features of SRM include simplicity, robustness, low manufacturing cost, high starting torque, high speed and high efficiency (Miller, 1993; Lawrenson et al, 1980) It requires only a simple converter based heuristic controls have shown a good prospect to bring robustness and adaptive nature in constant speed variable torque or constant torque variable speed drive applications (Kukolj et al, 2000). Fuzzy logic is one of the the output from controller, is derived from u(t) as given in Eq 2: artificial intelligence techniques, but its applications are more recent than other experts systems It gives smooth output control even for huge variations in input variables.

Objectives

Results

Conclusion