This paper develops a new algorithm for large-angle pitch maneuvers of deformable solar sails in minimum time that avoids overshooting the target angle given a set of model uncertainties. The paper uses a simplified Euler- Bernoulli beam to model the sail's flexible booms. Model uncertainties include the flexural rigidity of the sail's booms, the effectiveness of the sail's reflectivity, and its moment of inertia about the pitch axis. The effect of each of these sources of uncertainty is investigated for a set of three sails of increasing size. The algorithm relies on trajectory-based reachability techniques to obtain a distribution of final states at the end of a large-angle maneuver that depend on the estimated model uncertainties. Using a tunable measure of statistical safety, the algorithm determines the required buffer angle to avoid overshooting the target attitude. Machine learning is used for reducing the parameter uncertainties based on a calibration maneuver. In addition, it is used to obtain a fast-access relationship between the current uncertainty in the model parameters and the required buffer angle.
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Copyright © 2017 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
ASJC Scopus subject areas
- Aerospace Engineering
- Space and Planetary Science