  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.
