Performance Investigation of Model Predictive Control for Brushless DC Motor

Abstract

Brushless DC motors (BLDCM) have been used in various fields because of their advantages. BLDCM need good control systems to achieve excellent performance with more energy efficient, better speed, current and torque response. Six commutation method inserted with Pulse Width Modulation (PWM) duty cycle as control signal is commonly used. Common control methods ignore other crucial factors like current, torque, power, and only concentrate on controlling the motor speed. PWM in this method can also result in noise and ripple which can deteriorate BLDC performance. More advanced controls have been studied to improve control performance such as Model Predictive Control (MPC) which has become popular in electric drive. This paper aims to investigate Finite Control Set MPC (FCSMPC), class of MPC, in controlling BLDCM. FCSMPC is simulated in Simulink MATLAB platform and compared to Linear Quadratic with Integrator (LQI) control. The results demonstrate that compared to LQI, FCSMPC has better in both transient and steady state performance. FCSMPC exhibits faster transient response and higher load handling with smaller speed drop. Furthermore, it also demonstrates higher mechanical power and torque with lower ripple and noises which lead to energy efficiency.