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.
翻译:机器人平台(如辅助和自主车辆、飞机和无人驾驶飞机)的制动控制系统的性能受到操纵期间经历的长途摩擦的深刻影响,因此,准确估算算法的可用性对于制定先进的控制计划具有重大意义。本文件的重点是估算问题。特别是,根据多层神经网络提出了新的估算算法。培训基于一套合成数据,该套数据来自广泛使用的摩擦模型。一些模拟情景对拟议算法的公开循环性能进行了评估。此外,还采用不同的控制方案测试闭路环情景,其中将估计的最佳误差用作设定点。实验结果和与基于模型的基线的比较表明,拟议方法能够提供有效的最佳滑动估计。