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Improved PSO-BP neural network-based brushless DC motor control method

A BP neural network, PSO-BP technology, applied in the direction of motor generator control, electronic commutation motor control, control system, etc., can solve the problems of slow convergence speed, difficult to meet high precision, easy to fall into local minimum point, etc., to achieve Good modern intelligent control, reduce the effect of speed error

Inactive Publication Date: 2018-08-03
TIANJIN UNIV
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Problems solved by technology

Among them, BP neural network is a multi-layer forward network with one-way propagation, which has good generalization performance, but its shortcomings such as slow convergence speed and easy to fall into local minimum make it difficult to meet the high-precision requirements of function approximation.

Method used

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  • Improved PSO-BP neural network-based brushless DC motor control method
  • Improved PSO-BP neural network-based brushless DC motor control method
  • Improved PSO-BP neural network-based brushless DC motor control method

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Embodiment Construction

[0048] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0049] Such as figure 2 As shown, the overall process of the brushless DC motor control method based on the improved PSO-BP neural network of the present invention includes two parts of algorithms, namely 2 (a) improved PSO algorithm off-line training BP neural network initial weight algorithm process and 2 ( b) Parameter online self-tuning algorithm flow of BP neural network controller. Firstly, the initial weights of the BP neural network are trained offline by improving the PSO particle swarm optimization algorithm, and then the optimal network weights obtained by offline training are used as the initial network weights of the BP neural network, and the network weights are determined through the self-learning of the BP neural network. The adjustment is to adjust the three control parameters of the PID online, and then control the brushless...

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Abstract

The invention discloses an improved PSO-BP neural network-based brushless DC motor control method. The improved PSO-BP neural network-based brushless DC motor control method comprises the steps of firstly, performing off-line training on an initial weight value of a BP neural network by an improved particle swarm optimization (PSO); and secondly, taking optimal network weight value obtained by off-line training as the initial weight value of the BP neutral network, adjusting the network weight value by self learning of the BP neutral network so that on-line adjustment is performed on three control parameters of PID and a brushless DC motor is further controlled. Compared with the prior art, the improved PSO-BP neural network-based brushless DC motor control method has the advantage that arotational speed error in brushless DC motor control is greatly reduced.

Description

technical field [0001] The invention relates to the field of automatic control, in particular to a control method of a brushless direct current motor. Background technique [0002] Brushless DC motor is a new type of motor that has developed rapidly with the development of new permanent magnet materials, microelectronics technology, automatic control technology and power electronics technology, especially high-power switching devices. It replaces the mechanical commutator with an electronic commutator. It has good control performance of DC motor, and also has the advantages of simple structure, reliable operation, high power density, good speed regulation performance, strong anti-electromagnetic interference ability, long life and high operation reliability. It is used in industrial automation systems, communication Equipment, aerospace, consumer electronics, medical electronics, automotive industry and other fields have been widely used. Since the brushless DC motor is a n...

Claims

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Application Information

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IPC IPC(8): H02P21/00
CPCH02P21/0014
Inventor 张淑芳朱彬华
Owner TIANJIN UNIV
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