Novel static decoupling algorithm for the multi-axis wheel force sensor based on the Informer network

Ensuring the stability of heavy multi-axle vehicles necessitates the accurate calibration and decoupling of Multiaxis Wheel Force Sensors (MWFS). Traditional methods often neglect the temporal coupling present in MWFS output data, leading to reduced accuracy. This paper introduces an Improved Decoupling Algorithm (IDI) based on the Informer network, designed to temporally decouple MWFS and enhance precision. The Decoupling embedding layer (DE) performs linear decoupling of the MWFS, while the Token embedding layer (TE) and Informer encoder extract timing coupling features. The highway network and linear fully-connected layer then provide nonlinear decoupling compensation. Experimental results demonstrate that the IDI algorithm significantly outperforms traditional methods like Extreme Learning Machine (ELM) and Back Propagation Network (BPNN), achieving at least a 41.12% improvement in accuracy in the highly coupled Mz channel. In conclusion, the IDI algorithm not only achieves high-precision decoupling of MWFS but also presents a robust framework for modeling various sensor types.