基于模糊神经网络PID的PMSM速度控制算法研究  

Research on PMSM Speed Control Algorithm Based on Fuzzy Neural Network PID

作　　者：白国振[1] 马宇航 王双园[1] 沈先成 BAI Guo-zhen;MA Yu-hang;WANG Shuang-yuan;SHEN Xian-cheng(University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学,机械工程学院,上海200093

出　　处：《电力电子技术》2024年第11期13-16,共4页Power Electronics

基　　金：海洋智能装备与系统教育部重点实验室开放基金(MIES-2020-05)。

摘　　要：针对永磁同步电机(PMSM)控制系统中存在的内部参数难以确定、动态变化适应性差等问题,提出了一种模糊神经网络PID控制算法,实现对PMSM速度的精确控制。首先,该控制算法采用模糊神经网络对PID控制参数进行自适应整定,并利用反向传播(BP)算法动态调整模糊神经网络的参数。然后,利用Matlab/Simulink仿真了不同频率和大小的负载干扰下,该控制算法对PMSM响应速度和控制精度的影响,结果显示速度波动维持在极小范围,最大响应时间小于13ms;最后,在电机实验平台上进行了测试,实验结果表明该控制算法具有较好的响应速度和抗干扰能力,且对正弦波信号具有良好的跟随效果,可以适应不同的工况。A fuzzy neural network-based PID control algorithm is developed to precisely regulate the speed of a permanent magnet synchronous motor(PMSM).This approach addresses challenges associated with the difficulty in determining internal parameters and the motor's suboptimal adaptability to dynamic changes.Initially,the fuzzy neural network adjusts the PID control parameters,while the backpropagation(BP)algorithm dynamically fine-tunes these parameters.Subsequently,Matlab/Simulink is employed to simulate the impact of this control algorithm on both the response speed and control precision of the PMSM under varying load interference frequencies and magnitudes.The findings indicate that speed fluctuations remain confined to a minimal range,with the maximum response time being less than 13 ms.Ultimately,the control algorithm's efficacy is validated on a motor test platform.Experimental results demonstrate its commendable response speed,robust anti-interference capabilities,and effective tracking of the sine wave signal across diverse operational conditions.

关 键 词：永磁同步电机 控制算法 模糊神经网络 抗干扰 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]