Modeling and Analysis of Runway Friction Coefficient Prediction Methods Based on Multivariable Coupling

Friction, generated in the contact patch between tires and runway, has long been recognized as a key issue to a safe landing. Moreover, soft sensing technology is also considered to be one of the effective means to improve measurement accuracy and reduce measurement cost of friction coefficient. This article presents a study on the prediction methods of runway friction coefficient based on multivariable coupling. Firstly, the relationships between friction coefficient and tread frictional stress under different working conditions are investigated using finite element analysis; meanwhile, the modeling dataset and test datasets for multivariate coupling are established. Secondly, regression analysis and BP neural network (BPNN) are used to develop prediction models between independent variables (inflation pressure, load, velocity, slip, and frictional stress) and dependent variable (friction coefficient). Finally, the prediction models are evaluated based on internal and external verification. The results demonstrate the superior performance of both prediction models. After further comparison, it is concluded that the performance of BPNN is even better combining the error range and model evaluation indicators including MAE, RMSE, and
${R}^{2}$
.