Nonlinear momentum transfer control of a gyrostat with a discrete damper using neural networks

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An adaptive feedback linearization technique combined with neural networks is addressed for the momentum transfer control of a torque-free gyrostat with an attached spring-mass-dashpot damper. The neural network is used to adaptively compensate for the model error uncertainties of internal dynamics. The total spacecraft angular momentum component of the wheel spin axis is selected as an output function for the feedback linearization. Thus, a desired output function is predefined for which the total angular momentum of the spacecraft is absorbed into the wheel spin direction at the steady state with nutation angle converging to zero. The ultimate boundedness of the tracking error is proved by the Lyapunov stability theory. We also investigate the effect of rotor misalignment on the steady spin of the spacecraft and the initial stability condition to overcome the inverted turn due to unstable mass moment of inertia configuration. The effectiveness of the proposed control law is verified through a simulation study. (c) 2008 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2008-03
Language
English
Article Type
Article
Keywords

OUTPUT-FEEDBACK CONTROL; DUAL-SPIN SPACECRAFT; ATTITUDE ACQUISITION; FLIGHT CONTROL; CONTROL LAW; SYSTEMS; DYNAMICS; LINEARIZATION; ENERGY

Citation

ACTA ASTRONAUTICA, v.62, no.6-7, pp.357 - 373

ISSN
0094-5765
DOI
10.1016/j.actaastro.2008.01.014
URI
http://hdl.handle.net/10203/15878
Appears in Collection
AE-Journal Papers(저널논문)
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