论文标题
在不同的道路条件下，有效制动控制的数据驱动的滑动估计方法
A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions
论文作者
论文摘要
机器人平台制动控制系统的性能，例如辅助和自动驾驶汽车，飞机和无人机，受到操纵期间经历的道路摩擦的深刻影响。因此，准确的估计算法的可用性在高级控制方案的开发中至关重要。本文的重点是估计问题。特别是，基于多层神经网络提出了一种新颖的估计算法。该培训基于一个合成数据集，该数据集来自广泛使用的摩擦模型。在许多模拟方案中评估了所提出算法的开放循环性能。此外，使用不同的控制方案来测试闭环方案，其中估计的最佳滑移用作设定点。实验结果以及与基于模型的基线的比较表明，所提出的方法可以提供有效的最佳滑移估计。
The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.
