车轮多分力传感器静态解耦方法

Research on Static Decoupling Methods for Self-developed Multi-component Wheel Force Transducer

从标定方法和解耦算法两方面对车轮力传感器静态耦合特性进行了研究。给出了详细的标定过程和样本获取方法,基于实际标定样本应用3种回归模型对多分力传感器维间耦合进行量化对比分析。结果表明:标定主通道线性特性显著;自行研制轮力传感器静态耦合率与国外产品相当;最小二乘支持向量回归机回归精度高、泛化能力强和算法稳定;对标准正交最小二乘径向基神经网络算法改进回归效果显著。

Both calibration method and static decoupling algorithm were employed to analyze wheel force transducer （WFT）. Firstly, the calibration procedure and extraction of sample data were presented in detail based on the self-developed hydraulic bench. Then, three decoupling methods were utilized respectively to quantify the coupling effects. The main findings are as the follows： the linearity of each main calibration channel is notable; the calculated rate of static coupling of the self-developed WFT is equal to the same-type foreign product; the least square support vector regression （LS-SVR） algorithm owns the characteristics of high regression precision, outstanding generalization and excellent algorithm stability; the method to modify the algorithm of the standard OLS-RBF NN （orthogonal least square radial-basis-function neural network） improves the regression performance significantly.