Brushless DC motor fuzzy control system based on genetic algorithm and control method thereof

Abstract

The present invention discloses a brushless DC motor fuzzy control system based on a genetic algorithm and a control method thereof. The control system comprises a drive system used for driving a brushless DC motor, a speed observer used for collecting the rotation speed of the brushless DC motor, a rotation speed reference model which provides the reference data of a motor rotation speed and compares the reference data of a motor rotation speed with the collected motor rotation speed so as to obtain a rotation speed error and an error change rate, a parameter fuzzification module which receives the rotation speed error and the error change rate, carries out quantification on the rotation speed error and the error change rate, and maps the rotation speed error and the error change rate to a fuzzy set discourse domain, a genetic algorithm optimization module which uses the genetic algorithm to carry out online optimization on a module control rule, adjusts related parameters in the fuzzy set discourse domain and makes a fuzzy decision, and a defuzzification module which maps the output amount of the fuzzy decision to a basic discourse domain. According to the system and the method, the genetic algorithm is used to carry out online adjustment of the parameter of the fuzzy controller, and the controller can have good static and dynamic performance in different operation environments.

Description

technical field [0001] The invention belongs to the field of motor control or regulation, in particular to a brushless DC motor fuzzy control system and control method based on genetic algorithm. Background technique [0002] The brushless DC motor (brushless DC motor) has been widely used in the industrial field, and its control system is a typical nonlinear, multivariable coupling system. The traditional PID control algorithm is not easy to meet the control requirements of high-precision servo control system, and it is difficult to realize the high-precision operation of the motor. [0003] The nonlinear control method based on modern control theory and intelligent control theory has laid the foundation for realizing high-quality dynamic and steady-state performance of the controlled system, and has been fully utilized in the control of brushless DC motors. Many advanced control strategies such as fuzzy control, neural network control, variable structure control, robust c...

AI Technical Summary

Problems solved by technology

However, in practical applications, for nonlinear systems with time-varying parameters, the control rules of the fuzzy control system are difficult to determine. not ideal

[0005] The existing methods of combining fuzzy control and PID control to control brushless DC motors have certain deficiencies in the control of nonlinear systems with time-varying parameters such as brushless DC motors: the control rules that fuzzy PID control relies on have certain limitations. Insufficient, the control system parameters lack self-adjustment ability, there are still deficiencies in the optimization of fuzzy PID control rules and online adjustment of fuzzy control system parameters.

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