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  • 标题:Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
  • 其他标题:Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
  • 本地全文:下载
  • 作者:Paolo Di Giamberardino ; Maria Laura Aceto ; Oliviero Giannini
  • 期刊名称:Actuators
  • 电子版ISSN:2076-0825
  • 出版年度:2018
  • 卷号:7
  • 期号:4
  • 页码:74
  • DOI:10.3390/act7040074
  • 出版社:MDPI Publishing
  • 摘要:The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification.
  • 关键词:micromanipulation; microgripper; biological samples analysis; visco-elastic characteristic measurement; dynamic parameters estimation micromanipulation ; microgripper ; biological samples analysis ; visco-elastic characteristic measurement ; dynamic parameters estimation
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