Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2012 Oct 29;12(11):14537-55. doi: 10.3390/s121114537.
Coupling errors are major threats to the accuracy of 3-axis force sensors. Design of decoupling algorithms is a challenging topic due to the uncertainty of coupling errors. The conventional nonlinear decoupling algorithms by a standard Neural Network (NN) are sometimes unstable due to overfitting. In order to avoid overfitting and minimize the negative effect of random noises and gross errors in calibration data, we propose a novel nonlinear static decoupling algorithm based on the establishment of a coupling error model. Instead of regarding the whole system as a black box in conventional algorithm, the coupling error model is designed by the principle of coupling errors, in which the nonlinear relationships between forces and coupling errors in each dimension are calculated separately. Six separate Support Vector Regressions (SVRs) are employed for their ability to perform adaptive, nonlinear data fitting. The decoupling performance of the proposed algorithm is compared with the conventional method by utilizing obtained data from the static calibration experiment of a 3-axis force sensor. Experimental results show that the proposed decoupling algorithm gives more robust performance with high efficiency and decoupling accuracy, and can thus be potentially applied to the decoupling application of 3-axis force sensors.
耦合误差是三轴力传感器准确性的主要威胁。由于耦合误差的不确定性，解耦算法的设计是一个具有挑战性的课题。由于过拟合，传统的基于标准神经网络 (NN) 的非线性解耦算法有时不稳定。为了避免过拟合以及最小化标定数据中随机噪声和粗大误差的负面影响，我们提出了一种新的基于耦合误差模型建立的非线性静态解耦算法。与传统算法将整个系统视为黑盒不同，耦合误差模型是根据耦合误差原理设计的，其中每个维度的力和耦合误差之间的非线性关系是分别计算的。由于其能够进行自适应、非线性数据拟合的能力，我们使用了六个独立的支持向量回归 (SVR)。通过利用三轴力传感器静态标定实验获得的数据，比较了所提出算法与传统方法的解耦性能。实验结果表明，所提出的解耦算法具有高效、高精度的解耦性能，具有更稳健的性能，因此可以潜在地应用于三轴力传感器的解耦应用。
