基于模糊神经网络的多电机同步控制技术  

Research and Experiment of Multi-Motor Synchronous Control Based on Fuzzy Neural Network

作　　者：徐玲 

机构地区：[1]烟台工程职业技术学院,山东烟台264006

出　　处：《实验室研究与探索》2017年第4期16-19,共4页Research and Exploration In Laboratory

基　　金：国家自然科学基金项目(31672348)

摘　　要：针对多电机同步驱动伺服系统因不同电机特性、不平衡扭矩等因素引起的同步偏差问题,以数控机床双电机驱动系统为研究对象,以提高机床加工精度为目标,对双电机同步控制方法展开研究。传统PID控制参数无法自动调节,只能在特定工况实现最优控制。基于此,利用模糊规则推导神经网络的权重系数,实现系统参数满足不同作业条件下的需要。为验证算法的先进性,分别对传动轴的进给速度、控制电机的输入电流以及传动轴的位置误差进行仿真分析和测试。研究结果显示,与传统控制方法相比,本控制算法可有效实现从动电机对主动电机的跟随,减小两个传动轴的同步误差,从而大大提高工件的加工精度,抗干扰能力强,为多电机同步控制理论的研究提供一定参考。For multi-motor servo system, deviations caused by the characteristics of the motors, torque imbalance and other factors make synchronization control difficult. Hence, an NC machine with double motors driving system is selected as research object, and the machining accuracy as the research goal. Control parameters of traditional PID are determined by optimal control of some special cases, hence cannot be automatically adjusted. This paper uses the fuzzy rules to derive the weight coefficients of the neural network, and makes the system parameters to meet the needs of different operating conditions. In order to verify the advantages of the algorithm, the feed rate of the driving shaft, the input current of the motor and the position error of the drive shaft are simulated and tested, respectively. The results show that compared with the traditional control method, this control algorithm can effectively realize the active motor to follow driven motor, the synchronization error is reduced by two drive shafts. It greatly improves the machining accuracy, has strong anti-interference ability. The results provide some reference for the research of multi-motor synchronous control theory.

关 键 词：主从控制 模糊神经控制 双电机同步 机床 

分 类 号：TM341[电气工程—电机]