Speed control of brushless DC motor by adaptive network-based fuzzy inference

Brushless dc (BLDC) motor provides many advantages such as less power consumption, small volume, good stability, larger torque and simple control. As a result, the industry market gradually replaces the traditional brushed dc motor and induction motor with the BLDCM. The BLDCM is traditionally set with Hall-effect sensors for applying the correct commutation information. Generally speaking, the Hall-effect is apt to be affected easily by noise and its low resolution such that motor operation speed is limited. The encoder has better resolution but higher cost. In this paper, the adaptive network-based fuzzy inference system will be used to improve the motor speed response. The core of the proposed controlled system is dsPIC30F6010A of Microchip, and Hall-effect sensor is used to match six-step squarewave driving to control the motor current. Finally, the experimental results will verify the proposed method.