Earticle

초록

영어

Brushless DC motor (BLDCM) has been widely used in industries because of its advantages. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. It is difficult to get satisfied control performance with conventional PID controller. By using self-learning and adaptive ability of neural network to unknown information, an intelligent PID control method based on adaptive radial basis function (RBF) neural network is proposed. Connection weight of neural network is updated according to the motor speed and phase current. The duty ratio of pulse width modulation (PWM) is adjusted to regulate the speed of BLDCM. The effectiveness of the proposed control method was validated with simulation and experiment. Simulation and experiment results showed that compared with the conventional PID controller, the proposed controller has less overshoot, faster response speed, stronger ability of anti-disturbance.

목차

Abstract
 1. Introduction
 2. PID Controller Design Based on RBF Neural Network
  2.1. Adaptive RBF Neural Network Model
  2.2. Adaptive RBF Neural Network PID Tuning Principle
 3. Design of BLDCM Controller Based on Adaptive RBF Neural Network
  3.1. Mathematical Model of BLDCM
  3.2. Design of BLDCM Controller Based on Adaptive RBF Neural Network
 4. Simulation and Experiment Results
  4.1. Simulation Results and Analysis
  4.2. Experimental Results and Analysis
 5. Conclusions
 References

키워드

저자

• Tao Fu [ School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China ]
• Xiaoyuan Wang [ School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605