Permanent magnet synchronous motor random command filtering neural network control method capable of considering iron losses

A technology of permanent magnet synchronous motor and neural network control, which is applied in the direction of model/simulation for control purposes

Active Publication Date: 2019-06-21
QINGDAO UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to propose a permanent magnet synchronous motor stochastic command filter neural network control method considering iron loss, to

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  • Permanent magnet synchronous motor random command filtering neural network control method capable of considering iron losses
  • Permanent magnet synchronous motor random command filtering neural network control method capable of considering iron losses
  • Permanent magnet synchronous motor random command filtering neural network control method capable of considering iron losses

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Embodiment Construction

[0192] The basic idea of ​​the present invention is: use the neural network system to approach the unknown random nonlinear function in the permanent magnet synchronous motor drive system, and at the same time based on the Lyapunov function, use the backstepping method to construct the intermediate virtual control signal, and obtain the control law step by step, so as to ensure The voltage and current are stabilized in a bounded area, reducing errors and improving control accuracy.

[0193] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0194] Such as figure 1 As shown, the neural network control method of permanent magnet synchronous motor stochastic command filter considering iron loss mainly includes the random command filter neural network controller 1 of permanent magnet synchronous motor considering iron loss, coordinate transformation unit 2, SVPWM inverter Device 3, speed detection unit 4 ...

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Abstract

The invention belongs to the technical field of permanent magnet synchronous motor position tracking control, and particularly discloses a permanent magnet synchronous motor random command filtering neural network control method capable of considering iron losses. The method introduces a command filtering technology into a traditional backstepping method by aiming at the control accuracy requirements of the permanent magnet synchronous motor and random disturbance and nonlinear problems in a driving system so as to solve the problem of ''calculation explosion'' in a calculation process. Meanwhile, a nonlinear function in a neural network approximation system is used for constructing a neural network adaptive position tracking controller. By use of the method, the tracking error of a systemcan be guaranteed to converge into a small-enough neighbourhood of an origin point. Compared with an existing method, the method disclosed by the invention is higher in working efficiency and anti-jamming capability and has a better control effect.

Description

technical field [0001] The invention belongs to the technical field of position tracking control of permanent magnet synchronous motors, and in particular relates to a random command filter neural network control method of permanent magnet synchronous motors considering iron loss. Background technique [0002] In recent years, permanent magnet synchronous motors (PMSM) have been widely used in agriculture, industry and other fields due to their simple structure, high efficiency, long service life and strong practical applicability. However, the system of PMSM is highly nonlinear, strongly coupled and multivariable, and the motor system will be disturbed by some uncertain factors in practical applications, such as parameter uncertainty and load disturbance. In order to solve these problems, relevant scientific and technological workers have proposed some advanced nonlinear control methods and achieved good results, such as backstepping control, sliding mode control, robust co...

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Application Information

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IPC IPC(8): H02P6/34
CPCH02P6/34
Inventor 于金鹏马玉梅刘占杰程帅付程王孟孟
Owner QINGDAO UNIV
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