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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorBevilacqua, Riccardo
dc.contributorJulius, Anak Agung
dc.contributorAnderson, Kurt S.
dc.contributorMishra, Sandipan
dc.contributorLovell, Thomas A.
dc.contributor.authorPérez Chaparro, David Andrés
dc.date.accessioned2021-11-03T08:08:16Z
dc.date.available2021-11-03T08:08:16Z
dc.date.created2014-04-14T11:29:03Z
dc.date.issued2013-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1064
dc.descriptionDecember 2013
dc.descriptionSchool of Engineering
dc.description.abstractIn this work atmospheric differential drag based nonlinear controllers are presented that can be used for virtually any planar relative maneuver of two spacecraft, provided that there is enough atmospheric density and that the spacecraft can change their ballistic coefficients by sufficient amounts to generate the necessary differential accelerations. The control techniques are successfully tested using high fidelity Satellite Tool Kit simulations for re-phase, fly-around, and rendezvous maneuvers, proving the feasibility of the proposed approach for a real flight. Furthermore, the atmospheric density varies in time and in space as the spacecraft travel along their orbits. The ability to accurately forecast the density allows for accurate onboard orbit propagation and for creating realistic guidance trajectories for maneuvers that rely on the differential drag.
dc.description.abstractIn this work a localized density predictor based on artificial neural networks is also presented. The predictor uses density measurements or estimates along the past orbits and can use a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbits of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using neural networks, both in prediction accuracy and in the potential for spacecraft onboard implementation. The controllers and the predictor are designed for onboard implementation, and provide spacecraft with the tools necessary for performing propellant-less formation maneuvers using differential drag.
dc.description.abstractAt low Earth orbits, a differential in the drag acceleration between spacecraft can be used to control their relative motion. This drag differential allows for a propellant-free alternative to thrusters for performing relative maneuvers in these orbits. The interest in autonomous propellant-less maneuvering comes from the desire to reduce the costs of spacecraft formations. Formation maneuvering opens up a wide variety of new applications for spacecraft missions, such as on-orbit maintenance and refueling.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectAeronautical engineering
dc.titleAdaptive Lyapunov control and artificial neural networks for spacecraft relative maneuvering using atmospheric differential drag
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid170944
dc.digitool.pid170945
dc.digitool.pid170946
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Mechanical, Aerospace, and Nuclear Engineering


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