Motor rotating-speed tracking control method based on self-adaptive fuzzy neural network

A fuzzy neural network and self-adaptive fuzzy technology, applied in the direction of AC motor control, control system, electrical components, etc.

Active Publication Date: 2014-04-02
TIANJIN UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problems of control accuracy and anti-disturbance performance in the motor speed control system, and propose a fuzzy neural network adaptive control method for the motor speed control system

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  • Motor rotating-speed tracking control method based on self-adaptive fuzzy neural network
  • Motor rotating-speed tracking control method based on self-adaptive fuzzy neural network
  • Motor rotating-speed tracking control method based on self-adaptive fuzzy neural network

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Embodiment Construction

[0030] The present invention is applicable to the speed tracking control of motors such as brushless DC motors and permanent magnet synchronous motors. The following only takes the brushless DC motor as an example, and further describes the present invention in detail in conjunction with the drawings and embodiments.

[0031] The invention designs double-closed-loop control of rotating speed and current for the brushless DC motor speed regulating system. For the speed loop, a fuzzy neural network controller based on the sliding mode control theory is designed, and the parameter correction method designed by the sliding mode control theory is used for online real-time learning. The load disturbance has strong robustness, and has high following performance for a given speed.

[0032] The overall control block diagram of the brushless DC motor speed control system is as follows: figure 1 shown. The brushless DC motor uses the Hall signal obtained by the Hall sensor to calculate...

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Abstract

The invention relates to a motor rotating-speed tracking control method based on a self-adaptive fuzzy neural network, wherein rotating-speed and current double closed-loop control is adopted, an outer ring is a rotating-speed ring, a sliding-mode control theory-based fuzzy neural-network controller (SMFNN) is designed, an inner ring is a current ring, and a PI (Proportional-Integral) controller is adopted; a fuzzy neural-network rotating-speed controller comprises two parts, wherein one part is a PID (Proportional-Integral-Derivative) controller, and the other part is the fuzzy neural network, online real-time learning is carried out through the fuzzy neural network by utilizing a parameter correcting method designed on the basis of the sliding-mode control theory, and the two parts jointly act to obtain the output ir of the rotating-speed controller, i.e. a difference obtained by subtracting the output iFNN of the fuzzy neural network from the output iPID of the PID controller is used as the output ir of the rotating-speed controller. The control precision and the anti-interference performance of a motor speed-adjusting system can be improved through the control strategy of the motor rotating-speed tracking control method.

Description

Technical field [0001] The invention relates to a motor speed regulation method, in particular to the field of motor intelligent speed regulation control. Background technique [0002] The motor speed control system is widely used in elevators, medical equipment, inverter air conditioners, electric vehicles and many other fields, and people's requirements for the control accuracy of the motor speed control system are also getting higher and higher. Therefore, the motor speed tracking control method has become a research hotspot in the scientific research circle. [0003] At present, the motor speed control system usually adopts the proportional-integral (PI) control strategy. However, in practical applications, the time-varying, nonlinear, and strong coupling characteristics of the motor make the traditional control strategy unable to meet the high-precision dynamic and static conditions of the system. Performance. The latest achievements such as optimal control, model ref...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02P23/00
Inventor 史婷娜肖竹欣夏长亮
Owner TIANJIN UNIV
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