An Intuitively Derived Decoupling and Calibration Model to the Multiaxis Force Sensor Using Polynomials Basis

Precise decoupling and calibration of multiaxis 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 (LS) decoupling model with the delicate nonlinear error modeling by the multivariate Bernstein polynomials. The two- and three-axis force sensors are tested and compared with the proposed Bernstein-based prime-error model (BPEM), the LS decoupling model (LSM), and error-based neural network (eNN) learning model, extreme learning machine (ELM), as well as the error-based support vector machine (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.