An intuitively-derived decoupling and calibration model to the multi-axis force sensor using polynomials basis

Precise decoupling and calibration of multi-axis force sensor (MFS) is crucial in engineering applications. This work presents a novel nonlinear decoupling and calibration approach to meet the physical coupling characteristics of the structural strain-deformation transducers. It deals with the most force-voltage responses by the linear prime modeling and the gross error deviation by the nonlinear error modeling. Such a prime-error framework is naturally derived from the conventional least-square decoupling model with the delicate nonlinear error modeling by the multivariate Bernstein polynomials. The two-axis and three-axis force sensor are tested and compared with the proposed Bernstein-based Prime-Error Model (BPEM), the Least-Square-based Method (LSM), and error-based Neural Network learning model (eNN), Extreme Learning Machine (ELM) as well as the error-based Support Vector Model (e-SVM), the Interpretable Nonlinear Decoupling model (IND). Results demonstrate that the proposed BPEM provides an accurate, practical, and effective scheme for modeling and calibrating MFSs.