Adaptive control to parameter variations in a DC motor control system using fuzzy rules

Author(s):  
C.K. Lee
2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng

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.


2011 ◽  
Vol 411 ◽  
pp. 250-254
Author(s):  
Feng Sun ◽  
Niu Wang ◽  
Guo Feng Zhang

According to the relation of inertia moment and electromechanical time constant, dynamic structure and inertia moment of double loop DC motor control system is proposed. The effect of the moment of inertia on the dynamic response process of the double-loop DC control system is analyzed. In addition, the mathematical relation of the inertia moment and the rotation acceleration for the double-loop DC control system is discussed. Lastly, the effectiveness of the mathematical relation is verified through the experiment of the actual system.


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