@article{YAO2023, 
author = {Bin YAO and Zihao ZHANG and Yu DAI and Jianxun ZHANG},
title = {Research on Dynamic Characteristics of Force Sensor Based on VFF-RLS},
year = {2023},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {51},
number = {5},
pages = {86-94},
keywords = {forgetting factor, vibration model, dynamic performance calibration, recursive least squares, parameter identification},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.220374},
doi = {10.12141/j.issn.1000-565X.220374},
abstract = {In scientific experiments and industrial production, the dynamic characteristics of the force sensor will directly affect the accuracy, so it is of great significance to research the dynamic characteristics of the force sensor. Aiming at the practical problem that the dynamic characteristics of strain gauge force sensor used in surgical robots are difficult to meet the accuracy requirements, this paper studied the application of least square parameter identification method in the vibration structure of force sensor. Because recursive least squares (RLS) is difficult to ensure the rapidity and anti-interference of the second order vibration system model identification, therefore, this paper proposed a recursive least squares parameter identification method based on variable forgetting factor. Firstly, the parameters of the forgetting factor function were determined by establishing the random vibration system model, simulating and analyzing the input/output characteristics of the system. The simulation results show that the proposed method in the paper can significantly reduce the parameter identification error and convergence prediction error compared with RLS while maintaining a faster convergence speed, and has better time variability compared with the least squares. Furthermore, the dynamic parameters of the force sensor used in minimally invasive surgical robot were identified based on the step test calibration method to obtain the structural dynamic characteristics (i. e. natural frequency and damping ratio) of the sensor system. The experimental results show that the proposed method in the paper has good convergence and stability, and can effectively improve the identification accuracy.}
}