Improved Continuous Descent Operations considering environmental and mission related uncertainties

在大规模的单一欧洲天空（SES）项目中，EUROCONTROL于2012年发布了一份ATM总体规划，为2020年后的下一代ATM系统铺平道路。除了整合机场流程和发展全系统信息管理（SWIM）外，总体规划的一个主要目标是实施低能耗、经济的持续下降操作（CDO），以提高下降和进近程序的操作效率。然而，目前实施的CDO无法证明预期的效率提高，主要原因是飞行员和空管人员在确定正确的下降许可时间方面的飞行技术表现不佳。这反过来又依赖于许多输入和干扰变量的影响，这些变量与每个CDO的精确行为有关，但尚未正式知道。因此，对于飞行员来说，还没有一个可靠而精确的降落点（ToD）预测。本研究旨在克服这一缺陷，对因果确定性输入变量和随机干扰变量以及错过飞行计划航路点（特别是ToD）的影响进行更精确的建模和描述。在通常情况下，由于任何原因ToD可能不匹配，飞行员将获得一个被称为改进下降顾问的可靠决策支持，以判断是否应该更晚或更早开始下降，始终考虑当前的环境条件和飞行的个人成本函数。为了提供与空中交通管制合作的鲁棒性解决方案，将采用随机优化技术求解个体成本函数。因此，顾问不仅要描述最佳情况，还要描述一个可接受的4d飞行制度，在该制度下，最低解算器质量将被授予。目标函数将首次同时考虑经济效率和生态效率。在七个工作包中，我们从最先进的优化方法程序开始，重点关注现有CDO程序的限制，以及由部分未知环境条件、飞机配置数据及其预测技术导致的不确定性处理。因此，我们的目标是确定如何通过随机建模来建立最佳的能量优化CDO模型。在第一步之后，使用创新的OR方法对所有理论CDO解决方案进行随机优化，从而生成次优CDO解决方案。随后，可视化概念“改进下降顾问”将被开发并应用到德累斯顿工业大学的空中交通模拟环境中，该环境包括一架A320航班和一个实验性空中交通管制模拟器。

Along the large scale project Single European Sky (SES), EUROCONTROL issued an ATM masterplan in 2012 to pave the way for the next generation ATM System beyond 2020. Next to the integration of airport processes and the development of a system-wide information management (SWIM), a major pillar of the masterplan targets the implementation of low energy, economic Continuous Descent Operations (CDO) to increase operational efficiency of descent and approach procedures. However, so far implemented CDO cannot demonstrate the expected efficiency gains mainly due to poor flight technical performance of pilots an ATC controllers in determining the right time to issue descent clearances. This in turn relies on the effects of numerous input and disturbance variables, which are relevant for the precise conduct of each CDO, but not yet formally known. A reliable and precise prediction of the Top of Descent (ToD) is therefore not yet available to pilots. The present study aims at overcoming this deficiency towards a more precise modeling and description of causal deterministic input variables and stochastic disturbance variables and the effect of missed flightplan waypoints (especially the ToD). In those often cases where the ToD may not be matched by any reason, the pilot will be given a sound decision support called Improved Descent Advisor to judge whether a rather late or early start of descent should be preferred, always considering current ambient conditions and the individual cost function of that flight. In order to provide robust solutions to the pilot in cooperation with ATC, stochastic optimization techniques will be applied to solve the individual cost function. Consequently the advisor will not only depict the best case but an acceptable 4 D flight regime within which a minimum solver quality will be granted. The objective functions will for the first time consider both economic and ecological efficiency. Along seven work packages, we start with a state-of-the-art for optimized approach procedures focusing on restrictions for existing CDO procedures, and uncertainty handling resulting from partly unknown ambient conditions, aircraft configuration data and their forecast techniques. We so aim at identifying on how to model best energy-optimized CDO through stochastic modeling. Following this first step, a stochastic optimization of all theoretical CDO solutions is undertaken using innovative OR methods, so generating second best CDO solutions. Subsequently, the visualization concept Improved Descent Advisor will be developed and implemented into the air traffic simulation environment at TU Dresden comprising an A320 flight and an experimental air traffic control simulator.