Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms

Abstract

The Air Traffic Management (ATM) system in the busiest airspaces in the world is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental challenges. One major pillar of this process is the shift towards a concept of operations centered on aircraft trajectories (called Trajectory-Based Operations or TBO in Europe) instead of rigid airspace structures. However, its successful implementation (and, thus, the realization of the associated improvements in ATM performance) rests on appropriate understanding and management of uncertainty. Due to its complex socio-technical structure, the design and operations of the ATM system are heavily impacted by uncertainty, proceeding from multiple sources and propagating through the interconnections between its subsystems.One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic nature, a number of meteorological phenomena of interest cannot be forecasted with complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in individual air and ground operations that propagates to all ATM processes. Therefore, in order to achieve the goals of SESAR and similar programs, it is necessary to deal with meteorological uncertainty at multiple scales, from the trajectory prediction and planning processes to flow and traffic management operations. This thesis addresses the problem of single-aircraft flight planning considering two important sources of meteorological uncertainty: wind prediction error and convective activity. As the actual wind field deviates from its forecast, the actual trajectory will diverge in time from the planned trajectory, generating uncertainty in arrival times, sector entry and exit times, and fuel burn. Convective activity also impacts trajectory predictability, as it leads pilots to deviate from their planned route, creating challenging situations for controllers. In this work, we aim to develop algorithms and methods for aircraft trajectory optimization that are able to integrate information about the uncertainty in these meteorological phenomena into the flight planning process at both pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in order to generate more efficient and predictable trajectories.To that end, we frame flight planning as an optimal control problem, modeling the motion of the aircraft with a point-mass model and the BADA performance model. Optimal control methods represent a flexible and general approach that has a long history of success in the aerospace field. As a numerical scheme, we use direct methods, which can deal with nonlinear systems of moderate and high-dimensional state spaces in a computationally manageable way. Nevertheless, while this framework is well-developed in the context of deterministic problems, the techniques for the solution of practical ix x Abstract optimal control problems under uncertainty are not as mature, and the methods proposed in the literature are not applicable to the flight planning probl...

Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

“…Therefore, with the continuous growth of demand for air transportation, increasing the fuel efficiency of aircraft has become a key goal in the aviation industry. Aircraft technology and design improvements [3,4], improvement of the socioeconomic policies related to air transportation [5,6], optimization of aircraft operation processes [7,8], and the search for alternative fuels [9,10] Trajectory-based operation is one of the basic characteristics of the new generation of air transportation systems. It aims to realize four-dimensional (4D) trajectory planning and control before takeoff and during flight through continuous information exchange between various stakeholders.…”

Thunderstorms Prediction Using Satellite Images

“…These models have been used, Weather Research and Prediction (WRF), the mesoscale convective system (MCS) with three parameterization systems (Betts-Miller-Janjic, Grell-Freitas, and Kain-Kain-Fritsch). However [54] Rapidly Developing Thunderstorms, a satellite-based product, provided the data using a robust optimal control mechanism methodology developed for storms ranging from 1000 to 6000 kilometers and durations of one or two weeks. An accuracy of roughly 90 percent was achieved by using this methodology.…”

Data-driven fault-tolerant control for unmanned aerial vehicles without using identification model

“…Based on equation (7), select control δ e ¼ K _ X , in which K _ is the control gain which is obtained by solving the augmented ARE in equation ( 16), and thus…”

“…Over the past few decades, there has been a lot of research on model-based flight control. Such as, gain scheduling control, 3,4 optimal and inverse optimal control, 1,[5][6][7] adaptive control techniques, 2,8 robust control [9][10][11] and sliding mode control, [12][13][14][15][16] and so on. However, these controller designs is based on the benchmark dynamic model of UAVs, new uncertainties induced by aerodynamic damage will seriously affect flight control performance.…”