Designing a Fuzzy PID Controller for Brushless DC Motor

2012 ◽  
Vol 588-589 ◽  
pp. 1650-1653
Author(s):  
Yu Hao Qian

Based on the mathematical model of the brushless DC motor (BLDCM), a self-adaptive fuzzy PID controller is designed to achieve high-precision speed control of motor by adopting fuzzy control principle, simulation is conducted in MATLAB /SIMULINK, the result shows that the controller can work well with quick response, no overshoot output and high control precision, has strong robustness under the circumstances of various disturbances and parameter variations, whose static and dynamic performance with the self-adaptive fuzzy PID control are both better than conventional PID control.

2011 ◽  
Vol 305 ◽  
pp. 173-176
Author(s):  
Jian Bo Cao ◽  
Ming Qiang Mao ◽  
Wan Lu Xu ◽  
Jia Ji ◽  
Jia Jiang ◽  
...  

To deal with the control problem of brushless DC motor (BLDCM), based on analyzing the work principle of BLDCM, the fuzzy-PID control was studied, and the fuzzy-PID controller of BLDCM was designed. The experimental results show that the fuzzy-PID controller is superior to the PID controller at steady-state tracking error. Additionally, the current and torque undulation of BLDCM were also improved.


2014 ◽  
Vol 644-650 ◽  
pp. 179-183
Author(s):  
Ya Juan Chen ◽  
Yue Hong Zhang

In this paper, an adaptive fuzzy PID controller based on genetic algorithm is designed. Brushless DC motor uses double closed loop control system. The adaptive fuzzy PID controller based on genetic algorithm is applied to outer ring speed ring, and PI controller is applied to inner ring. The simulation results show that, the designed brushless DC motor control system based on genetic algorithm optimization has a short rise time and no overshoot, small steady-state error and other advantages. And the system has strong robustness and adaptability.


2013 ◽  
Vol 432 ◽  
pp. 472-477
Author(s):  
Wei Fan ◽  
Tao Chen

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.


2012 ◽  
Vol 246-247 ◽  
pp. 838-841
Author(s):  
Gong She Shi ◽  
Lei Huang ◽  
Wei Hu

The brushless DC motor (BLDCM) non-linear and the complexity of the working conditions are likely to cause the conventional PID servo control performance is not satisfactory. In order to improve the performance of the BLDCM servo control system and PID parameter tuning efficiency, this paper designs an adaptive fuzzy PID controller. Fuzzy logic PID controller parameters Kp, Ki, Kd are adjusted online real time to achieve the effect of optimal control, the BLDCM speed is as to the control object, and in the Matlab of Simulink toolbox simulation is used to achieve speed closed loop of BLDCM. According to comparative analysis of the conventional PID and adaptive fuzzy PID of Dynamic response curve, adaptive Fuzzy PID quick start for brushless DC motors, anti-disturbance has better control effect.


2013 ◽  
Vol 365-366 ◽  
pp. 847-852
Author(s):  
Li Fang Xu ◽  
Yong Jie Han

Based on the basic characteristics of fuzzy control theory and the general PID controller, a fuzzy PID controller is proposed for brushless DC motor (Brushless Direct Current Motor-BLDCM) control.The output signal PD-type is used as the input signal of fuzzy controller.Fuzzy PID controller can be adaptively adjust the PID control parameters to achieve operational control of BLDCM control system. Simulation results show that, compared with the general PID controller, fuzzy PID controller shows better characteristics on the respects of dynamic response overshoot and stability regulation time.


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