Test mass capture for drag-free satellite based on RBF neural network adaptive sliding mode control-Reference-Cited by-同舟云学术

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Test mass capture for drag-free satellite based on RBF neural network adaptive sliding mode control

Published:2022-01 Issue:2 Volume:69 Page:1205-1219
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Lian Xiaobin,Zhang Jinxiu,Chang Lantian,Song Jianing,Sun Jun

Funder

Shanghai Academy of Spaceflight Technology

Publisher

Elsevier BV

Subject

Space and Planetary Science,Aerospace Engineering,General Earth and Planetary Sciences,Atmospheric Science,Geophysics,Astronomy and Astrophysics

Reference35 articles.

1. Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors;Armano;Phys. Rev. D,2017

2. LISA Pathfinder test mass injection in geodesic motion: status of the on-ground testing;Bortoluzzi;Class. Quantum Gravity,2009

3. Prediction of the LISA-Pathfinder release mechanism in-flight performance;Bortoluzzi;Adv. Space Res.,2013

4. Drag-Free Control of the GOCE Satellite: Noise and Observer Design;Canuto;IEEE Trans. Control Syst. Technol.,2010

5. All-propulsion design of the drag-free and attitude control of the European satellite GOCE;Canuto;Acta Astronautica,2009

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