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Aerospace
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Machine Learning in Aerospace Trajectory Optimization, Guidance and Control
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Machine Learning in Aerospace Trajectory Optimization, Guidance and Control

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 10885

Special Issue Editor

Prof. Dr. Kouamana Bousson

E-Mail Website
Guest Editor
Department of Aerospace Sciences, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
Interests: 4D trajectory optimization, guidance and control; nonlinear filtering; statistical machine learning in aerospace systems; chaotic systems

Special Issue Information

Dear Colleagues,

The automation of guidance, navigation, and control (GNC) systems in aerospace vehicles provides the means to alleviate operator workload, monitor energy systems, and track desired trajectory settings during specific phases of flight missions. Traditional approaches rely on first principle models based on dynamic equations of the underlying systems; meanwhile, these models suffer from their intrinsic limitations such as parametric uncertainties, unknown disturbance dynamics, and runtime complexities with respect to critical mission conditions. These limitations may be circumvented by machine learning (ML) concepts. Typically, ML methods enable systems to learn from data, identify and recognize patterns, and make decisions comparable to those of humans and often even more efficiently than humans do. As for GNC systems, ML may provide appropriate ways to come up with uncertainties, unmodeled dynamics, complex big data analyses, and online processing times.

This Special Issue copes with recent advances in ML methodologies and applications to GNC systems for aerospace vehicle missions. The expected topics include surveys, design, analyses, and applications of ML concepts in the framework of (but not limited to) the following activities:  

  • GNC data analysis;
  • Space mission design;
  • Orbit determination;
  • Trajectory optimization, guidance, and control;
  • Fuel saving in aerospace missions;
  • Trajectory design for aerospace vehicles;
  • 4D trajectory control;
  • Robust flight controller design;
  • Trajectory forecasting;
  • Unmanned aerial vehicles operations.

Prof. Dr. Kouamana Bousson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • guidance, navigation, and control
  • trajectory optimization
  • neural networks for flight control
  • aerospace mission data analysis

Published Papers (3 papers)

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Research

23 pages, 11026 KiB  
Article
Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism
by Weijie Ding, Jin Huang, Guanyu Shang, Xuexuan Wang, Baoqiang Li, Yunfei Li and Hourong Liu
Aerospace 2022, 9(8), 464; https://doi.org/10.3390/aerospace9080464 - 20 Aug 2022
Cited by 2 | Viewed by 1822
Abstract
Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in [...] Read more.
Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in overcoming the short-term memory of time series in existing trajectory prediction, a trajectory prediction model based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) network combined with dual attention and genetic algorithm (GA) optimisation is proposed. First, to autonomously mine the data association between input features and trajectory features as well as highlight the influence of important features, an attention mechanism was added to a conventional CNN architecture to develop a feature attention module. An attention mechanism was introduced at the output of the BiLSTM network to form a temporal attention module to enhance the influence of important historical information, and GA was used to optimise the hyperparameters of the model to achieve the best performance. Finally, a multifaceted comparison with other typical time-series prediction models based on real flight data verifies that the prediction model based on hyperparameter optimisation and a dual attention mechanism has significant advantages in terms of prediction accuracy and applicability. Full article
(This article belongs to the Special Issue Machine Learning in Aerospace Trajectory Optimization, Guidance and Control)
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19 pages, 1202 KiB  
Article
A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters
by Charalampos Papakonstantinou, Ioannis Daramouskas, Vaios Lappas, Vassilis C. Moulianitis and Vassilis Kostopoulos
Aerospace 2022, 9(3), 164; https://doi.org/10.3390/aerospace9030164 - 17 Mar 2022
Cited by 5 | Viewed by 2051
Abstract
This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and [...] Read more.
This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data. Full article
(This article belongs to the Special Issue Machine Learning in Aerospace Trajectory Optimization, Guidance and Control)
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34 pages, 1321 KiB  
Article
A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments
by Abdul Majeed and Seong Oun Hwang
Aerospace 2021, 8(11), 343; https://doi.org/10.3390/aerospace8110343 - 13 Nov 2021
Cited by 23 | Viewed by 4460
Abstract
This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often [...] Read more.
This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments. Full article
(This article belongs to the Special Issue Machine Learning in Aerospace Trajectory Optimization, Guidance and Control)
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