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Method for optimizing brushless DC motor fuzzy controller based on improved particle swarm algorithm

A fuzzy controller and improved particle swarm technology, applied in the field of control, can solve problems such as local optimization, slow convergence speed, premature convergence, etc.

Inactive Publication Date: 2016-01-27
GUANGXI NORMAL UNIV
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Problems solved by technology

However, although the above algorithm can better optimize the fuzzy controller, it also has slow convergence speed, easy to fall into local optimization, and it is easy to cause premature convergence and other phenomena.

Method used

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  • Method for optimizing brushless DC motor fuzzy controller based on improved particle swarm algorithm
  • Method for optimizing brushless DC motor fuzzy controller based on improved particle swarm algorithm
  • Method for optimizing brushless DC motor fuzzy controller based on improved particle swarm algorithm

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] In 1995, Kennedy and Eberhart jointly proposed the particle swarm optimization algorithm, and the particle swarm optimization algorithm (PSO) was born. The standard particle swarm optimization algorithm can regard the solution space as a particle swarm, and treat the effective solutions in the D-dimensional solution space as particles without mass and volume. Suppose there are m particles flying in D-dimensional space, and the position of particle i is expressed as X i , the flight speed is denoted as V i , the optimal position found by itself is P i , the entire population finds the optimal position P g . During the iterative calculation, the position and velocity of the particles are optimized by the following equations:

[0043] V i d k + ...

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Abstract

The invention discloses a method for optimizing a brushless DC motor fuzzy controller based on an improved particle swarm algorithm. The steps include that the whole solution space is divided into seven areas; adaptation degree of each particle is calculated according to a target function; the individual extremum of the particles and the global extremum are updated according to adaptation degree; the updated individual extremum and the global extremum are assigned to quantifying factors Ka and Kb and scaling factors Kp, Ki and Kd; and input and output performance indicators are assessed, if the input and output performance indicators meet the target function, the process ends, and if the input and output performance indicators do not meet the target function, Pi and Pg are substituted in a quantum particle swarm formula by applying the improved particle swarm algorithm, continuous optimization of the particles is performed in the space areas until the particles meet the target function and new particle swarms are generated. The globally optimal solution can be found out at the highest speed based on the improved particle swarm algorithm, and a motor stably operates under the rated rotating speed and is rapid in response without overshoot basically so that the method has great follow-up performance and dynamic and static characteristics.

Description

technical field [0001] The invention relates to the technical field of control, in particular to a method for optimizing a fuzzy controller of a brushless DC motor based on an improved particle swarm algorithm. Background technique [0002] Brushless DC motor has been a research and development hotspot in recent years. It not only has good speed regulation characteristics, but also has the characteristics of reliable operation, simple structure, multi-variable, strong coupling, nonlinear and other characteristics. It is widely used in industrial control, aerospace, automotive and home appliances And other fields have a wide range of applications and research optimization value. BLDCM generally adopts PID control. The traditional PID control method is simple and mature, with convenient parameter setting and good stability. However, there are also defects such as poor control accuracy and slow adaptability, which make it difficult to meet the precise requirements of today's in...

Claims

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Application Information

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IPC IPC(8): H02P6/00G06N3/00
Inventor 王国宇黄植功戴明朱天顺
Owner GUANGXI NORMAL UNIV
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