Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model
Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model
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DOI：
10.1016/j.eng.2021.01.009
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发表时间：
2021-03
期刊：
影响因子：
12.8
通讯作者：
O. Obajemu;M. Mahfouf;Lohithaksha M. Maiyar;Abrar Al-Hindi;Michal Weiszer;Jun Chen
中科院分区：
文献类型：
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作者：
O. Obajemu;M. Mahfouf;Lohithaksha M. Maiyar;Abrar Al-Hindi;Michal Weiszer;Jun Chen
飞机地面运动在提高机场效率方面发挥着关键作用，因为它是所有其他地面运营的纽带。寻找新颖的方法来协调机场机队的运动，以通过增强自主性来提高系统对中断的恢复能力，这是机场空侧运营许多关键研究的核心。此外，自动滑行被认为是未来数字化机场的关键组成部分。然而，最先进的机场地面运动路线和调度算法在主动和被动规划阶段都没有考虑高保真飞机模型。大多数此类算法不会主动寻求优化燃油效率和减少有害温室气体排放。本文提出了一种基于高保真飞机模型和增益调度控制策略生成高效四维轨迹（4DT）的新方法。所提出的方法与确定滑行路线、航路点和时间期限的路线和调度算法结合使用，可以实时生成节能的 4DT，同时尊重操作限制。所提出的方法可用于两种情况：①作为反应性决策支持工具，生成可以解决前所未有的事件的新轨迹； ② 作为自动驾驶系统，用于部分和完全自主滑行。所提出的方法是现实且易于实施的。此外，模拟研究表明，所提出的方法能够使大型波音 747-100 大型喷气式飞机滑行期间的燃油消耗减少高达 11%。
Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gain-scheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.