Geometrical Vertical Trajectory Optimization – Comparative Performance Evaluation of Phase versus Phase and Altitude-Dependent Preferred Gradient Selection

Dancila, Bogdan Dumitru; Beulze, Benoit; Botez, Ruxandra Mihaela

doi:10.1016/j.ifacol.2016.09.004

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…These results are consistent with the findings of the preliminary phases of the investigation. 58,59 The Matlab code corresponding to the two algorithm implementations was executed on a Windows 7, AMD Phenom(tm) II X4, 2.80 GHz platform. The minimum, maximum, and mean execution times for the set of 48 test cases, corresponding to each of the two algorithm implementations (''A'' and ''B''), are presented in Table 6.…”

Section: Resultsmentioning

confidence: 99%

“…The method presented here is part of the authors' efforts to address this limitation by researching new methods capable of geometrically constructing an optimal vertical flight plan 58,59 minimizing the number of vertical plan' segments, and thus the number of flight path angles (slopes). Also, the proposed method aims to reduce the computation workload by eliminating the need for repetitive aircraft performance model-based calculations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments

Dancila

Beulze

Botez

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a new method for the geometrical construction of an optimal vertical flight plan associated to a provided lateral flight plan defined as a succession of waypoints characterized by their along-the-track distance relative to the first waypoint and their constraints. The principal objective of the proposed method is the minimization of the total number of vertical flight plan segments, whose slope values closest match the values set for their corresponding flight phase and altitude. The main advantage of the proposed method is that it constructs the optimized vertical flight plan employing faster—and less-intensive computations than methods based solely on aircraft performance models. Also, the proposed algorithm has the advantage of generating ground-fixed predicted vertical flight plans which, when flown, are less sensitive to varying wind conditions, thus, smaller trajectory deviations than those computed using solely the model-based algorithms. Two implementations corresponding to different trade-offs between conflicting preferred gradient and minimal segment length constraints were compared. The results show that a vertical flight path segment’s construction and resulting configuration is dependent on the configuration of the vertical flight plan segments that precede it. The results also show that for a majority of the test cases, the resulting flight plans computed using the two implementations were identical. Moreover, even when the flight plans were not completely identical, many of the corresponding segments were identical.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments

Dancila

Beulze

Botez

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Geometrical approaches to vertical flight trajectory optimisation are presented in refs. [53,54]. Yu and Zhang (55) presented a survey of Unmanned Aerial System (UAS) flight path planning approaches.…”

Section: Introductionmentioning

confidence: 99%

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

Dancila

Botez

2021

Aeronaut. j.

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This study investigates a new aircraft flight trajectory optimisation method, derived from the Non-dominated Sorting Genetic Algorithm II method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the ‘global’ optimum. The worst variant yielded a maximum of 321kg (0.56%) over the ‘global’ optimum.

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“…Many aerial control tasks are processed by avionics systems. Such tasks might include aircraft trajectory optimization [2] and its application into flight management systems [3], which aim to reduce operational costs [4], fuel consumption, and adverse environmental side effects [5]. A variety of algorithms, such as genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], ant colony [8], bee colony [9], beam search [10], and harmony search [11] have been employed to solve aircraft trajectory optimization problems.…”

Section: Introductionmentioning

confidence: 99%

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

Hashemi¹,

Botez²,

Grigorie

2020

Aerospace

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Two main factors, including regression accuracy and adversarial attack robustness, of six trajectory prediction models are measured in this paper using the traffic flow management system (TFMS) public dataset of fixed-wing aircraft trajectories in a specific route provided by the Federal Aviation Administration. Six data-driven regressors with their desired architectures, from basic conventional to advanced deep learning, are explored in terms of the accuracy and reliability of their predicted trajectories. The main contribution of the paper is that the existence of adversarial samples was characterized for an aircraft trajectory problem, which is recast as a regression task in this paper. In other words, although data-driven algorithms are currently the best regressors, it is shown that they can be attacked by adversarial samples. Adversarial samples are similar to training samples; however, they can cause finely trained regressors to make incorrect predictions, which poses a security concern for learning-based trajectory prediction algorithms. It is shown that although deep-learning-based algorithms (e.g., long short-term memory (LSTM)) have higher regression accuracy with respect to conventional classifiers (e.g., support vector regression (SVR)), they are more sensitive to crafted states, which can be carefully manipulated even to redirect their predicted states towards incorrect states. This fact poses a real security issue for aircraft as adversarial attacks can result in intentional and purposely designed collisions of built-in systems that can include any type of learning-based trajectory predictor.

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Geometrical Vertical Trajectory Optimization – Comparative Performance Evaluation of Phase versus Phase and Altitude-Dependent Preferred Gradient Selection

Cited by 3 publications

References 19 publications

Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments

Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

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