Performance analysis of BLDC SRM and PMS motor using controllers and Optimization techniques

Abstract

The work describes the Comparative study on Design of newlineControllers for Brushless DC motor Switched Reluctance motor and newlinePermanent Magnet Synchronous motor using Soft computing techniques such newlineas Genetic Algorithm Ant colony Search Algorithm and Particle Swarm newlineOptimization The controllers are designed in such a way that PID Fuzzy newlinePID Neuro and Neuro Fuzzy and the transient specifications are analyzed newlineand compared The proposed approach has good features including easy newlineimplementation stable convergence characteristic and good computational newlineefficiency Brushless DC BLDC motors are widely used in many industrial newlineapplications because of their high efficiency high torque and low volume newlineThis work proposes a set of controllers to control the Brushless DC motor and newlinethe characteristics such as Torque Voltage Current and Speed It is difficult newlineto tune the parameters and get satisfied control characteristics by using newlinenormal conventional PID controller As Fuzzy has the ability to satisfy control newlinecharacteristics it is easy for computing In order to control the BLDC motor newlinea Fuzzy PID controller with PSO is designed as the controller of the BLDC newlinemotor newline newline