Asynchronous motor drive system control method based on extreme learning machine

A technology of extreme learning machine and asynchronous motor, which is applied in the direction of motor generator control, electronic commutation motor control, control system, etc.

Active Publication Date: 2017-05-31
QINGDAO UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional neural network learning algorithms (such as BP algorithm) need to artificially set a

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  • Asynchronous motor drive system control method based on extreme learning machine
  • Asynchronous motor drive system control method based on extreme learning machine
  • Asynchronous motor drive system control method based on extreme learning machine

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Embodiment Construction

[0093] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0094] combine figure 1 As shown, the control method of asynchronous motor drive system based on extreme learning machine, the asynchronous motor drive system used mainly includes asynchronous motor drive system controller 1 based on extreme learning machine, coordinate transformation unit 2, SVPWM inverter 3 and speed detection Unit 4 and current detection unit 5. The rotational speed detection unit 4 and the current detection unit 5 are mainly used to detect the current value and rotational speed related variables of the asynchronous motor, and the actual measured current and rotational speed variables are used as input, and the voltage is determined by the control controller 1 of the asynchronous motor drive system based on the extreme learning machine. Control, and finally converted to three-phase electricity to control the speed of the asy...

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Abstract

The invention discloses an asynchronous motor drive system control method based on an extreme learning machine. According to the control method, aimed at the nonlinear problems existing in an asynchronous motor drive system, a command filtering wave technology is introduced in a traditional backstepping design method, by introducing a compensation mechanism, the error generated by the filtering wave is reduced, and the problem of 'calculating explosion' caused by continuous derivation in a traditional backstepping control is successfully overcome. According to the control method, an extreme learning machine algorithm is utilized to approach a nonlinear function in the system, and the algorithm is combined with a command filtering wave technology and a self-adaption backstepping method. According to the control method, the operation of motor can quickly reach a stable state, the method is more suitable for control objects of an electric automobile drive system and the like which need fast dynamic response, a simulation result shows that the asynchronous motor drive system control method can overcome the influences of the uncertainties of parameters and is beneficial to the guarantee of an ideal control effect, and fast and stable response on the rotating speed is achieved.

Description

technical field [0001] The invention belongs to the technical field of motor speed regulation control, and in particular relates to a control method of an asynchronous motor drive system based on an extreme learning machine. Background technique [0002] Asynchronous motor is a kind of AC motor, also called induction motor, which is mainly used as a motor. Asynchronous motors are widely used in industrial and agricultural production, such as machine tools, water pumps, metallurgy, mining equipment and light industrial machinery, etc., all use it as a prime mover, and its capacity ranges from several kilowatts to several thousand kilowatts. The increasingly popular household appliances, such as washing machines, fans, refrigerators, and air conditioners, use one-way asynchronous motors with a capacity ranging from several watts to several kilowatts. In high-tech fields such as aerospace and computers, asynchronous motors can also be used as generators. For example, small hyd...

Claims

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Application Information

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IPC IPC(8): H02P21/00H02P21/18
CPCH02P21/0017H02P21/0025
Inventor 于金鹏马玉梅于海生牛浩山炳强李伟
Owner QINGDAO UNIV
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