基于增加动量项的神经网络左逆张力辨识策略  

Tension identification based on momentum algorithm of ANNI left-inverse

作　　者：蔡振南 陈杰[1] 瞿沥[1] 

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《信息技术》2016年第5期68-72,77,共6页Information Technology

基　　金：教育部博士点基金资助(20050299009);江苏省自然科学基金资助(BK2007094);江苏省研究生创新计划

摘　　要：以两电机张力控制系统为研究对象,为实现两电机张力控制系统的高性能控制及无传感器运行,张力的准确检测是其中的关键。文中提出了基于人工神经网络和左逆系统理论的两电机张力系统的一个新的识别方法。考虑到系统的参数是时变的和张力易受负载变化影响。神经网络左逆辨识被用在该系统中中,这是很容易实现的左逆模型。针对运用中传统BP网络的收敛速度慢,易陷入极小值的缺点,提出了增加动量项的改进神经网络左逆辨识策略,通过仿真模型对在负载扰动下两电机的张力进行辨识。仿真结果表明,辨识准确,策略可行。Tension detection is a key to improve performance of two-motor system under sensorless operation. This paper presents a new identification method for two-motor system based on artificial neural network and the left-inverse theory. Considering that the system parameters are time-variant and the mathematic model of left-inverse identification is complex,BP neural network is used to build the leftinverse model in this method,which is easy to implement. On this basis,the disadvantage of the traditional BP network convergence speed is slow,has fallen into a very small value,the proposed increase in momentum of improvement left inverse neural network identification strategy. The simulated results verify the proposed method. By using this control strategy,the tension can be identified quickly and accurately,in which satisfactory robustness is offered.

关 键 词：神经网络 逆系统 增量项改进 左逆辨识 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]