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Enregistrement W4323668888 · doi:10.2514/1.g007314

Fast Model Predictive Control for Spacecraft Rendezvous and Docking with Obstacle Avoidance

2023· article· en· W4323668888 sur OpenAlexafffundabout
Courtney Bashnick, Steve Ulrich

Notice bibliographique

RevueJournal of Guidance Control and Dynamics · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Control Systems Optimization
Établissements canadiensCarleton University
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésSpacecraftRendezvousObstacle avoidanceModel predictive controlCollision avoidanceComputer scienceControl theory (sociology)Docking (animal)ObstacleAerospace engineeringControl engineeringControl (management)EngineeringArtificial intelligenceMobile robotRobotGeography

Résumé

récupéré en direct d'OpenAlex

No AccessEngineering NotesFast Model Predictive Control for Spacecraft Rendezvous and Docking with Obstacle AvoidanceCourtney Bashnick and Steve UlrichCourtney BashnickCarleton University, Ottawa, Ontario K1S 5B6, Canada and Steve UlrichCarleton University, Ottawa, Ontario K1S 5B6, CanadaPublished Online:9 Mar 2023https://doi.org/10.2514/1.G007314SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations About References [1] Nolet S., "Development of a Guidance, Navigation and Control Architecture and Validation Process Enabling Autonomous Docking to a Tumbling Satellite," Ph.D. Thesis, Massachusetts Inst. of Technology, Cambridge, MA, 2007. Google Scholar[2] Lopez I. and Mclnnes C. 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P., Szmuk M., Lew T., Bonalli R., Pavone M. and Acikmese B., "Convex Optimization for Trajectory Generation," arXiv Preprint, June 2021. https://doi.org/10.48550/arXiv.2106.09125 Google Scholar[15] Lu P. and Liu X., "Autonomous Trajectory Planning for Rendezvous and Proximity Operations by Conic Optimization," Journal of Guidance, Control, and Dynamics, Vol. 36, No. 2, 2013, pp. 375–389. https://doi.org/10.2514/1.58436 LinkGoogle Scholar[16] Liu X. and Lu P., "Robust Trajectory Optimization for Highly Constrained Rendezvous and Proximity Operations," AIAA Guidance, Navigation, and Control (GNC) Conference, AIAA Paper 2013-4720, Aug. 2013. https://doi.org/10.2514/6.2013-4720 Google Scholar[17] Liu X. and Lu P., "Solving Nonconvex Optimal Control Problems by Convex Optimization," Journal of Guidance, Control, and Dynamics, Vol. 37, No. 3, 2014, pp. 750–765. https://doi.org/10.2514/1.62110. LinkGoogle Scholar[18] Petersen C., Jaunzemis A., Baldwin M., Holzinger M. and Kolmanovsky I., "Model Predictive Control and Extended Command Governor for Improving Robustness of Relative Motion Guidance and Control," 24th AAS/AIAA Spaceflight Mechanics Meeting, AAS Paper 14-249, Univelt, San Diego, CA, Jan. 2014, pp. 701–718. Google Scholar[19] Weiss A., Baldwin M., Erwin R. S. and Kolmanovsky I., "Model Predictive Control for Spacecraft Rendezvous and Docking: Strategies for Handling Constraints and Case Studies," IEEE Transactions on Control Systems Technology, Vol. 23, No. 4, 2015, pp. 1638–1647. https://doi.org/10.1109/TCST.2014.2379639 CrossrefGoogle Scholar[20] Park H., Di Cairano S. and Kolmanovsky I., "Linear Quadratic Model Predictive Control Approach to Spacecraft Rendezvous and Docking," Proceedings of 21st AAS/AIAA Space Flight Mechanics Meeting, Spaceflight Mechanics, Part III of Advances in the Astronautical Sciences, Vol. 140, AAS Paper 11-142, Univelt, Inc., Escondido, CA, Feb. 2011, pp. 565–584. 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T., "On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming," Mathematical Programming, Vol. 106, No. 1, 2006, pp. 25–57. https://doi.org/10.1007/s10107-004-0559-y CrossrefGoogle Scholar[24] Virgili-Llop J., Zagaris C., Park H., Zappulla R. and Romano M., "Experimental Evaluation of Model Predictive Control and Inverse Dynamics Control for Spacecraft Proximity and Docking Maneuvers," CEAS Space Journal, Vol. 10, No. 1, 2018, pp. 37–49. https://doi.org/10.1007/s12567-017-0155-7 CrossrefGoogle Scholar[25] Richards A., Schouwenaars T., How J. P. and Feron E., "Spacecraft Trajectory Planning with Avoidance Constraints Using Mixed-Integer Linear Programming," Journal of Guidance, Control, and Dynamics, Vol. 25, No. 4, 2002, pp. 755–764. https://doi.org/10.2514/2.4943 LinkGoogle Scholar[26] Wang Y. and Boyd S., "Fast Model Predictive Control Using Online Optimization," IEEE Transactions on Control Systems Technology, Vol. 18, No. 2, 2009, pp. 267–278. https://doi.org/10.1109/TCST.2009.2017934 Google Scholar[27] Hartley E. N. and Maciejowski J. M., "Field Programmable Gate Array Based Predictive Control System for Spacecraft Rendezvous in Elliptical Orbits," Optimal Control Applications and Methods, Vol. 36, No. 5, 2015, pp. 585–607. https://doi.org/10.1002/oca.2117 CrossrefGoogle Scholar[28] Mammarella M., Lorenzen M., Capello E., Park H., Dabbene F., Guglieri G., Romano M. and Allgöwer F., "An Offline-Sampling SMPC Framework with Application to Autonomous Space Maneuvers," IEEE Transactions on Control Systems Technology, Vol. 28, No. 2, 2020, pp. 388–402. https://doi.org/10.1109/TCST.2018.2879938 CrossrefGoogle Scholar[29] Mayne D. Q., Rawlings J. B., Rao C. V. and Scokaert P. O., "Constrained Model Predictive Control: Stability and Optimality," Automatica, Vol. 36, No. 6, 2000, pp. 789–814. https://doi.org/10.1016/S0005-1098(99)00214-9 CrossrefGoogle Scholar[30] Clohessy W. H. and Wiltshire R. S., "Terminal Guidance System for Satellite Rendezvous," Journal of the Aerospace Sciences, Vol. 27, No. 9, 1960, pp. 653–658. https://doi.org/10.2514/8.8704 LinkGoogle Scholar[31] Boyd S. and Vandenberghe L., Convex Optimization, Cambridge Univ. Press, Cambridge, England, U.K., 2004, pp. 531–535. Google Scholar Previous article Next article FiguresReferencesRelatedDetails What's Popular Volume 46, Number 5May 2023 CrossmarkInformationCopyright © 2023 by Courtney Bashnick and Steve Ulrich. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3884 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsApplied MathematicsControl SystemsControl TheoryGeneral PhysicsGuidance and Navigational AlgorithmsGuidance, Navigation, and Control SystemsMathematical OptimizationSpacecraft Guidance and Control KeywordsRendezvous and Docking AlgorithmsModel Predictive ControlConvex OptimizationReal-Time OptimizationAutonomous Guidance and ControlSpacecraft Proximity OperationsAcknowledgmentsThis research was financially supported in part by the Natural Sciences and Engineering Research Council of Canada Alexander Graham Bell Canada Graduate Scholarship-Master's award and through the New Technologies for Canadian Observatories Collaborative Research and Training Experience program.PDF Received24 October 2022Accepted29 January 2023Published online9 March 2023

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,202
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations47
Publié2023
Routes d'admission3
Résumé présentoui

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