Probabilistic and learning approaches through concurrent parameter estimation and adaptive control for in-space robotic assembly
Name
1011358354-MIT.pdf
Description
Full printable version
Size
21.4 MB
Format
Adobe PDF
Checksum (MD5)
252a9137dc6503543c696a982f9a89f8
Author(s)
Terán Espinoza, Antonio
Advisor(s)
Alvar Saenz-Otero.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Autonomous 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.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 143-148).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
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