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dc.contributor.advisorAlvar Saenz-Otero.en_US
dc.contributor.authorTerán Espinoza, Antonioen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.en_US
dc.date.accessioned2017-12-05T19:14:42Z
dc.date.available2017-12-05T19:14:42Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/112480
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 143-148).en_US
dc.description.abstractAutonomous and multi-agent space operations within the context of in-space robotic servicing, assembly, and debris removal have received particular attention from both research and industry communities. The presence of uncertainties and unknown system parameters amongst these missions is prevalent, as they primarily deal with unknown or uncooperative target objects, e.g., asteroids or unresponsive, unsupervised tumbling spacecraft. To lower the inherent risk associated with these types of operations, possessing an accurate knowledge of the aforementioned characteristics is essential. In order to achieve this, approaches that employ a unified framework between parameter estimation and learning methodologies through a Composite Adaptation (CA) structure are presented. Furthermore, to evaluate the likelihood of mission success or objective completion, a probabilistic approach upon the system's operations is introduced; by employing probability distributions to model the control system's response and pairing these with the analysis of objectives' requirements and agents' characteristics, the calculation of on-board feasibility and performance assessments is presented. A formulation for the estimator and the controllers is developed, and results for the adaptive approach are demonstrated through hardware implementation using MIT's Synchronized Position Hold Engage Reorient Experimental Satellites (SPHERES) ground testing facilities. On-orbit test session data is analyzed, and further improvements upon the initial learning approach are verified through simulations.en_US
dc.description.statementofresponsibilityby Antonio Terán Espinoza.en_US
dc.format.extent148 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectAeronautics and Astronautics.en_US
dc.titleProbabilistic and learning approaches through concurrent parameter estimation and adaptive control for in-space robotic assemblyen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
dc.identifier.oclc1011358354en_US


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