基于VFF-RLS的力传感器的动态特性研究

在科学实验与工业生产中，力传感器动态特性会直接影响传感器的精度，因此研究力传感器动态特性具有重要意义。针对应用于手术机器人的应变式力传感器动态特性难以满足精度要求的问题，文中研究了基于最小二乘参数辨识方法在力传感器振动结构中的应用。由于递推最小二乘（RLS）对于二阶振动系统模型辨识难以同时保证快速性和抗干扰性，文中提出了一种基于可变遗忘因子的递推最小二乘参数辨识方法。首先，通过建立随机振动系统模型，对系统的输入/输出特性进行仿真与分析，确定了遗忘因子函数中的参数，仿真结果表明，文中提出的方法在保持更快收敛速度的同时，使参数辨识误差和收敛预测误差相比于RLS有明显的降低，相比于最小二乘有良好的时变性；然后，在阶跃测试标定法基础上对微创外科手术机器人力传感器的动态参数进行辨识，获得该传感器系统的结构动态特性，即固有频率和阻尼比。实验结果表明，文中提出的方法有较好的收敛性和稳定性，有效地提高了辨识精度。

Research on Dynamic Characteristics of Force Sensor Based on VFF-RLS

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.