Design of BLDCM Control System with Fuzzy Adaptive Controller

2013 ◽  
Vol 765-767 ◽  
pp. 1791-1795 ◽  
Author(s):  
Zheng Zhong Li ◽  
Li Xia Guo ◽  
Guo Fang Gao
Keyword(s):  
Control System ◽  
Pid Control ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Adaptive Controller ◽  
Dc Motor ◽  
System Structure ◽  
Brushless Dc Motor ◽  
Brushless Dc

To handle the shortages of conventional PID control, recur to the high-performance digital signal processor, combine the fuzzy self-adaption controller with brushless dc motor control system. The results show the control system structure is simplified and the performances of control system are improved comparing to the conventional PID control, the performance index is better than that in conventional PID control system, so the stability of brushless dc motor operation is strengthened.

Design and Implementation of Fuzzy PID Controller for Brushless DC Motor Control System

2013 ◽  
Vol 432 ◽  
pp. 472-477
Author(s):  
Wei Fan ◽  
Tao Chen
Keyword(s):  
Control System ◽  
Digital Signal Processor ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Digital Signal ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Fuzzy Pid ◽  
Brushless Dc ◽  
Fuzzy Pid Controller

This paper presents a robust fuzzy proportional-integral-derivative (PID) controller for brushless DC motor (BLDCM) control system. The hardware circuit of the BLDCM control system is designed and implemented using a digital signal processor (DSP) TMS320LF2407A and a monolithic BLDCM controller MC33035 as the core. Furthermore, a fuzzy PID controller, which combines the advantages of good robustness of fuzzy controller and high precision of conventional PID controller, is employed in the hardware system, thereby yielding a digital, intelligent BLDCM control system. Experimental results have shown that the control system can run steadily and control accurately, and have convincingly demonstrated the usefulness of the proposed fuzzy PID controller in BLDCM control system.

Designed Controller of Permanent Magnet Brushless DC Motor Based on DSP

2014 ◽  
Vol 672-674 ◽  
pp. 1210-1213 ◽  
Author(s):  
Gui Yin Zhan
Keyword(s):  
Control System ◽  
Permanent Magnet ◽  
Digital Signal Processor ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Development Environment ◽  
Brushless Dc ◽  
Detection Circuit ◽  
Software Program ◽  
Permanent Magnet Brushless Dc

A design scheme of the control system was put forward to solve the performance control problems of the motor in this paper, combining with the composition and working principle of permanent magnet brushless DC motor. Hardware circuit of the control system was designed with digital signal processor (DSP) as the core, which mainly consists of the power drive circuit, the motor position, speed detection circuit and winding current detection circuit. Software flow of the control system was also designed, and software program debugging was achieved on CCS3.3 DSP integrated development environment, and the rationality of the software program was validated.

Single Neuron PID Adaptive Control for BLDCM Based on Algebraic Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng
Keyword(s):  
Adaptive Control ◽  
Motor Control ◽  
Control System ◽  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Dc Motor Control ◽  
Motor Control System

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.

Back-EMF Position Detection Technology for Brushless DC Motor

2014 ◽  
Vol 492 ◽  
pp. 49-55
Author(s):  
Hong Xing Wu ◽  
Zhi Yuang Qi ◽  
Ji Gui Zheng ◽  
Shou Ming Zhou
Keyword(s):  
Digital Signal Processor ◽  
Digital Signal ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Position Sensor ◽  
Brushless Dc ◽  
Starting Position ◽  
Position Detection ◽  
Detection Technology ◽  
Sensor Control

Without position sensor control technology of brushless DC motor has become the focus of many experts and scholars at home and abroad. The article presented research of without speed and position sensor control for brushless DC motor, including brushless DC motor mathematical model, Back-EMF detection principle and starting position detection method, and designed a hardware control system based dedicated digital signal processor (DSP). For experimental study of the system based experimental prototype had shown: this method can achieve no load starting, load starting and load operation of without position sensor brushless DC motor.

Suspension Force Control System Design and Simulation for Bearingless Brushless DC Motor

2014 ◽  
Vol 703 ◽  
pp. 250-253 ◽  
Author(s):  
Yi Chen Liu ◽  
Huang Qiu Zhu ◽  
Li Dong Zhu
Keyword(s):  
Control System ◽  
Force Control ◽  
High Performance ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Stable Suspension ◽  
Dc Motor Control ◽  
Force Control System ◽  
Control System Model

A bearingless brushless DC motor is a new type of high performance motor, which integrates the function of magnetic bearings into a brushless DC motor. In this paper, the suspension force control system is improved and optimization designed according to the radial suspension force mathematical model. The bearingless brushless DC motor control system model is established with the aid of Matlab/Simulink software. From the simulation results, it is confirmed that the rotor shaft is stably suspended without the mechanical contacts. The proposed suspension control system is found suitable to realize the stable suspension of the rotor in the bearingless brushless DC motor.

Design of BLDCM Fuzzy Control System for Electric Vehicle Based on DSP

2013 ◽  
Vol 274 ◽  
pp. 363-368
Author(s):  
Jia Bin Wen ◽  
Bin Yang
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Electric Vehicle ◽  
Digital Signal Processor ◽  
Torque Ripple ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Loop Control ◽  
Fuzzy Control System ◽  
Brushless Dc

In this paper, the design of BLDCM fuzzy control system for electric vehicle which bases on TMS320F2812 Digital Signal Processor (DSP) is given. The design methods of hardware and software of the control system are described in details. Using of brushless DC motor current and speed double closed loop control, meanwhile, the fuzzy self-tuning PI algorithm is applied in the control system, and achieve the intelligent control of brushless DC motor control system. In this paper, the PWM control of the overlapping commutation method to suppress torque ripple. Experimental results show that this system has such characteristics as sound controlling effects, obvious restrain of torque pulsation and reliable operation.

Application of Fuzzy PI Control to Speed Control System of Brushless DC Motor

2012 ◽  
Vol 516-517 ◽  
pp. 1575-1579
Author(s):  
Fa Huan Hu ◽  
Xiao Tong Qiu ◽  
Jun Tang
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Pid Control ◽  
Speed Control ◽  
Dc Motor ◽  
Control Model ◽  
Brushless Dc Motor ◽  
Pi Control ◽  
Current Loop ◽  
Brushless Dc

Basing on analysis the principle and mathematical model of the brushless DC motor, the paper proposes a control model on the basis of speed loop and current loop, fuzzy PI control model is adopted in the speed loop, and traditional PID control model is used in the current loop. It is showed that when the load or parameters vary, comparing to traditional PID control model, the fuzzy PI control model gets less fluctuation and better robustness.

Sign in / Sign up

Export Citation Format

Share Document