基于改进最小二乘法的MEMS振镜模型参数辨识
Parameter Identification of the MEMS Micromirror Model Based on Improved Least Squares Method
王紫蕊 1冯志辉 1雷铭 1武泽1
作者信息
- 1. 中国科学院光电技术研究所,四川 成都 610209;中国科学院大学,北京 100049;中国科学院空间光电精密测量技术重点实验室,四川 成都 610209
- 折叠
摘要
针对应用于激光雷达的电磁驱动微机电系统(MEMS)振镜数学模型建立的问题,采用机理分析法和输入输出法相结合的建模方法,建立了电磁驱动MEMS振镜的离散化模型,提出了一种带可变遗忘因子的递推最小二乘辨识电磁驱动MEMS振镜模型参数的方法.通过将遗忘因子动态化,解决了"数据饱和"的问题,更多的输入输出数据在参数辨识中发挥作用,提高了参数辨识的精度.对该方法进行仿真和实验验证,结果表明,带可变遗忘因子的递推最小二乘法辨识得到的模型相比于传统递推最小二乘法辨识得到的模型误差降低了9.2%.
Abstract
Aiming at the problem of establishing the mathematical model of the electromagnetic-driven micro-electro-mechanical system(MEMS)micromirror applied to the laser radar,the discrete model of the electromagnetic-driven MEMS micromirror is established by combining the mechanism analysis method with the input-output method.A recursive least squares method with variable forgetting factor is proposed to identify the model parameters of electromagnetic-driven MEMS micromirror.By making the forgetting factor dynamic,the problem of"data saturation"is solved,so that as much input and output data as possible can play a role in parameter identification,and the accuracy of parameter identification is improved.Through the simulation and experimental verification of this method,the results show that the error of the model obtained by recursive least squares identification with variable forgetting factor is reduced by 9.2%compared with traditional recursive least squares identification.
关键词
光学微机电装置/激光雷达/可变遗忘因子/参数辨识/电磁驱动Key words
optical microelectromechanical devices/lidar/variable forgetting factor/parameter identification/electromagnetical-driven引用本文复制引用
出版年
2024
NETL
NSTL
万方数据