Population Algorithms for optimal control of BLDC motor drive

Author Name(s): Manoj Kumar Merugumalla, Prema Kumar Navuri
Author Email: mkla2009@gmail.com

Abstract

This paper presents nature-inspired optimization algorithms such as particle swarm optimization (PSO) algorithm and bat algorithm (BA) for tuning PID controller parameters of BLDC motor drive. Both PSO algorithm BA are population based algorithms. Population based algorithms have number of advantages over classical methods for solving complex optimization problems. The position of BLDC rotor is determined by measuring the changes in the Back emf. Sensorless control method reduces the cost of motor as it does not need sensors for the detection of rotor position. The BLDC motor drive is modelled in Matlab/simullink. The simulation results reveals that proposed methods are effective in reducing the time domain parameters steady state error, rise time, settling time and peak overshoot.

Keywords

Brushless direct current motor, particle swarm optimization, bat algorithm, position, velocity, PID controller

Introduction

BLDC motor has wide range of applications because of its advantages such as high efficiency, flat speed-torque characteristics, and high speed range, smaller in size and lighter, longer life, low noise and good dynamic response when compared against brushed direct current motor. BLDC motor is a kind of permanent magnet synchronous motor (PMSM), trapezoidal type of PMSM is known as brushless direct current motor. In a brushless motor, stator contains windings and rotor incorporates permanent magnet. By moving permanent magnets to rotor and driving field coils with power electronic switch can eliminate brushes in dc motor. BLDC motors are often called as electronically commutated motors [1]-[3]. It requires some electronic control mechanism to determine the rotor position continuously. The rotor position can be determined either by measuring changes in back emf at each of the armature coils, which is known as sensorless control [4]-[7] or by using a Hall Effect sensors embedded into the stator on the nondriving end of the motor.

Conclusion

In this paper, the nature-inspired algorithms are proposed to search the PID controller parameters Kp, Ki, Kd for the speed control of BLDC motor drive. The motor drive system has been modeled and simulated in MATLAB/SIMULINK. The tracking performance is tested with change in speed reference speed and several time domain parameter performance measures such as rise time, peak time, settling time, peak overshoot and steady-state error of PSO algorithm and BA are compared. The simulation results of BA shows the less rise time compared to PSO algorithm but PSO results demonstrate the improved performance compared to BA in terms of settling time, peak-overshoot, steady-state error. Therefore, population algorithms are ideal to for optimal control of BLDC motor drive

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