论文标题
轮胎道路摩擦估计和不确定性评估，以改善电动飞机制动系统
Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system
论文作者
论文摘要
对于任何先进的制动器控制系统，对道路摩擦系数的准确估计是一项重要功能。在这项研究中，提出了基于MLP神经网的数据驱动方案，以估计最佳摩擦系数作为窗户滑移摩擦测量值的函数。随机NN权重辍学机制用于在线估计估计的最佳摩擦系数的置信区间，从而提供了与NN块相关的认知不确定性的表征。飞机在未知表面上的着陆阶段的开环和闭环模拟用于显示所提出的稳健摩擦估计方法的潜力和功效。
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.
