中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A Fusion Measurement Method Based on Kalman Filter with Improved State Block and Neural Network for Nanometer Displacement

文献类型:会议论文

作者Zhang ZL(张灼亮)1,2; Du ZM(杜章铭)1,2; Deng L(邓露)3; Zhou C(周超)2; Cao ZQ(曹志强)2; Wang S(王硕)2; Cheng L(程龙)2
出版日期2018-08
会议日期2018-8-5
会议地点Changchun, China
关键词multirate fusion state block nanometer displacement
英文摘要

In the field of nanomanipulation, measurement method for the nano-scale displacement is one of the key technologies, and there are many restrictions on the sensors mounted in microscopic device. In order to reduce the influence of sensors on workspace, we fused the measurements of self-sensing and time-digit-conversion (TDC) method to estimate the measured nanometer displacement. Both of the two methods have a simple measurement circuit and are easy to be integrated. They can reduce the effect of thermal radiation on workspace and work in a vacuum environment (such as SEM chamber). We proposed a method based on Kalman filter with improved state block and neural network to obtain the fusion estimation. Our method achieved a sampling rate equal to that of self-sensing, as well as a precision higher than those of the two source methods. The linearity (R2 ) of our method is 0.9999915 throughout 8 µm range. Finally, we compared our method with the traditional fusion method based on statistics.

语种英语
源URL[http://ir.ia.ac.cn/handle/173211/52126]  
专题复杂系统管理与控制国家重点实验室_水下机器人
通讯作者Deng L(邓露)
作者单位1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People’s Republic of China
2.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China
3.School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
推荐引用方式
GB/T 7714
Zhang ZL,Du ZM,Deng L,et al. A Fusion Measurement Method Based on Kalman Filter with Improved State Block and Neural Network for Nanometer Displacement[C]. 见:. Changchun, China. 2018-8-5.

入库方式: OAI收割

来源:自动化研究所

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