基于神经网络的超磁致伸缩传感执行器磁滞模型

Study on Hysteresis Model of Giant Magnetostrictive Sensing Actuator Based on Neural Network

依据超磁致伸缩材料Joule效应和Villari效应之间的耦合关系,提出一种超磁致伸缩传感执行器,该执行器利用Villari效应和Faraday效应产生的感应电动势驱动超磁致伸缩材料发生Joule效应而产生应变,给出了该传感执行器的结构和工作原理。为了解决材料的磁滞对超磁致伸缩传感执行器输出特性的影响,测量了在不同预紧力和最大工作电流作用下的磁滞回线,采用BP神经网络建立了磁化滞回模型。计算结果表明该模型能很好地描述在任意预紧力和最大工作电流等工作条件下的磁滞特性。

Based on the coupling relationship between Joule effect and Villari effect of the giant magnetostrictive materials,it proposed a giant magnetostrictive sensing actuator which used induced electromotive force generated by Villari effect and Faraday effect to drive giant magnetostrictive materials occurring Joule effect and generating strain.Its structure and working principle were presented herein.Meanwhile,in order to solve the hysteresis characteristics of the material in giant magnetostrictive sensing actuator,hysteresis loop under different pre-tightening force and maximum working current were measured.Moreover,it put forward establishing the magnetization hysteresis model of the giant magnetostrictive sensing actuator by BP neural networks.The calculation results show that the model can describe hysteresis characteristics under arbitrary pre-tightening force and maximum working current well.It provides evidence for hysteresis compensation of giant magnetostrictive sensing actuator.