模糊神经网络在机电调平系统上的应用  

Application of Fuzzy Neural Network in Electromechanical Leveling System

作　　者：张盼盼 郭彦青[1] 吴志伟 洪楚桐 Zhang Panpan;Guo Yanqing;Wu Zhiwei;Hong Chutong(School of Mechanical Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学机械工程学院,太原030051

出　　处：《煤矿机械》2025年第2期218-221,共4页Coal Mine Machinery

基　　金：山西省自然科学基金项目(20210302123054)。

摘　　要：针对负载和转速变化引起的直流无刷电机控制精度不足导致机电调平系统效率低的问题,提出了一种基于模糊神经网络的直流无刷电机转速控制算法,克服了传统PID控制算法超调量大、精度低、调节时间长的缺点,从而提高机电调平系统的调平效率。在Simulink中搭建直流无刷电机模糊神经网络控制系统仿真模型,仿真结果表明:模糊神经网络相比于传统PID和模糊PID,控制系统超调量分别降低14.6%和10.2%,稳定时间分别缩短0.013 s和0.01 s,具有更好的动态特性和抗干扰性,电机控制精度得到很大提高,能有效提高机电调平系统的调平效率。Aiming at the problem that the control precision of brushless DC motor is insufficient due to the change of load and speed,which leads to the low efficiency of electromechanical levelling system,a speed control algorithm of brushless DC motor based on fuzzy neural network was proposed,which overcomes the shortcomings of traditional PID control algorithm such as large overdrive,low precision and long adjustment time,so as to improve the levelling efficiency of electromechanical levelling system.The simulation model of fuzzy neural network control system of brushless DC motor was built in Simulink.The simulation results show that compared with the traditional PID and fuzzy PID,fuzzy neural network can reduce the overdrive of the control system by 14.6%and 10.2%,and shorten the stability time by 0.013 s and 0.01 s,respectively.It has better dynamic characteristics and anti-interference,greatly improves the control accuracy of the motor,and can effectively improve the leveling efficiency of the electromechanical leveling system.

关 键 词：直流无刷电机 机电调平 模糊神经网络 转速控制 

分 类 号：TM301.2[电气工程—电机]