Knowledge Commons of Institute of Automation,CAS
A Fusion Measurement Method for Nano-displacement Based on Kalman Filter and Neural Network | |
Zhang ZL(张灼亮)1,2; Zhou C(周超)1; Du ZM(杜章铭)1,2; Deng L(邓露)3; Cao ZQ(曹志强)1; Wang S(王硕)1; Cheng L(程龙)1; Deng S(邓赛)1 | |
发表期刊 | International Journal of Robotics and Automation |
2021 | |
卷号 | 36页码:1-9 |
摘要 | Nano-displacement measurement is one of the most important aspects of nanomanipulation. However, the narrow working space and the heat sensitivity of the microscope limit the installation of many displacement sensors. The self-sensing and time-digit-conversion (TDC) method can overcome the above limitations, making these two methods ideal for nano-displacement measurement. The former method has a high sampling frequency, but its accuracy is low. In contrast, the TDC method is more accurate, but its sampling rate is low. To solve these problems, a fusion measurement method was proposed, thus allowing us to combine the results of the self-sensing and TDC. Specifically, an improved Kalman filter was used to overcome the asynchronous multi-rate problem. Moreover, we fully utilized the information of the calibration instrument using the neural network. As for the overfitting problem, we adopted a neural network with convolution filtering. Our method achieved a precision of 47.9% higher than the traditional method, as well as a linearity (R2) of 0.99990 throughout 3,500 nm range. |
关键词 | multi-rate fusion state block convolution filtering nanoscale measurement |
收录类别 | SCI |
语种 | 英语 |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 水下仿生机器人 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52125 |
专题 | 复杂系统认知与决策实验室_水下机器人 |
通讯作者 | Zhou C(周超) |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang ZL,Zhou C,Du ZM,et al. A Fusion Measurement Method for Nano-displacement Based on Kalman Filter and Neural Network[J]. International Journal of Robotics and Automation,2021,36:1-9. |
APA | Zhang ZL.,Zhou C.,Du ZM.,Deng L.,Cao ZQ.,...&Deng S.(2021).A Fusion Measurement Method for Nano-displacement Based on Kalman Filter and Neural Network.International Journal of Robotics and Automation,36,1-9. |
MLA | Zhang ZL,et al."A Fusion Measurement Method for Nano-displacement Based on Kalman Filter and Neural Network".International Journal of Robotics and Automation 36(2021):1-9. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
IJRA.pdf(3806KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论