Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system
This work addresses a critical safety issue for electric aircraft by enhancing braking control, though it appears incremental as it builds on existing neural network methods with added uncertainty estimation.
The study tackled the problem of accurately estimating the tire-road friction coefficient for electric aircraft braking systems by proposing a data-driven MLP neural network with a stochastic dropout mechanism to assess uncertainty, resulting in improved robustness as demonstrated in simulations of landing phases.
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.