Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system

TL;DR

This paper presents a data-driven neural network approach for real-time tire-road friction estimation and uncertainty quantification to enhance electric aircraft braking systems, demonstrated through simulation results.

Contribution

It introduces a neural network-based method with dropout-based uncertainty estimation for online friction coefficient prediction in aircraft braking.

Findings

Effective friction estimation in simulations

Uncertainty quantification improves robustness

Potential for safer aircraft braking systems

Abstract

The accurate online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a MLP Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. A stochastic NN weights drop-out mechanism is used to online estimate the confidence interval of the estimated best friction coefficient thus providing a characterization of the epistemic uncertainty associated to the NN block. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.