Abstract

The position of Brushless Direct Current (BLDC) rotor is determined by measuring the changes in the Back-EMF. Sensorless control method reduces the cost of the motor as it does not need sensors. This paper presents Bio-inspired optimization technique (Particle Swarm Optimization algorithm) and classical methods of tuning PID control parameters for the automatic speed tracking of BLDC motor. The BLDC is modelled in Simulink in Matlab and Back-EMF waveforms are modelled as a function of rotor position. The proposed methods are effective in reducing the steady state error, rise time, settling time, and peak overshoot. The classical methods such as Ziegler-Nichols (Z-N), Tyreus-Luyben (T-L) methods, and Particle Swarm Optimization (PSO) techniques based on effective objective function Integral Absolute Error (IAE) are proposed for the optimal tuning of PID controller parameters. The results obtained from Particle Swarm Optimization technique are compared with the classical methods.

Keywords
Brushless Direct Current Motor, Sensorless Control, Particle Swarm Optimization, PID Controller.

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