摘要
由压电陶瓷驱动的快速反射镜(FSM)现已被广泛用于自适应光学系统的执行环节,为了对其迟滞效应精确建模,该文提出了一种针对FSM的IDE-BPNN建模方法。基于Madelung法则以最小二乘法构建称迟滞算子作为迟滞运动的基本描述,扩展训练用的数据集,并采用改进的差分进化算法(IDE)对BP神经网络(BPNN)进行训练。实验表明,当输入30 Hz衰减的正弦信号时,IDE-BPNN模型的单轴最大误差为0.745μrad,归一化最大误差为0.87%,归一化均方根误差为0.36%。相较于最小二乘建模法,相对于最小二乘模型误差大幅缩小,有较好的使用价值。
Fast steering mirror(FSM)driven by piezoelectric ceramics has been widely used in the execution of adaptive optical systems.In order to accurately model its hysteresis effect,this paper proposes an IDE-BPNN modeling method for FSM.Based on Madelung’s rule,a symmetric hysteresis operator is constructed by least squares method as the basic description of hysteresis motion,and the training dataset is extended.The improved differential evolution algorithm(IDE)is used to train BP neural network(BPNN).The experiment shows that when a 30 Hz attenuated sine signal is input,the single-axis maximum error of the IDE-BPNN model is 0.745μrad,the normalized maximum error is 0.87%,and the normalized root mean square error is 0.36%.Compared with the least squares model,the error of this model is greatly reduced and has good practical value.
作者
陈子楠
秦文虎
姚红权
CHEN Zinan;QIN Wenhu;YAO Hongquan(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;Nanjing Institute of Advanced Laser Technology,Nanjing 210038,China)
出处
《压电与声光》
CAS
北大核心
2023年第3期454-459,共6页
Piezoelectrics & Acoustooptics
基金
江苏现代农业产业关键技术创新资助项目(CX(20)2013)
江苏省重点研发计划项目(BE2019311)。
关键词
快速反射镜
压电陶瓷
迟滞效应
差分进化
神经网络
fast steering mirror
piezoelectric ceramics
hysteresis
differential evolution
neural network
