An adaptable control system within and on-board a network of interacting satellites is an important module which is desired for unmanned space vehicles. However, such a control system is not easy to develop since it is the core of the network’s operation and all the earth-linked operational information is reviewed and analyzed through it. Due to multipurpose missions of satellites, several decisions are made simultaneously about necessary changes to the satellite’s operational parameters (i.e., orbit, inclination, etc.). In this paper, a neural network model-based control scheme is developed for a network of interacting satellites. The proposed neural control scheme refers to a methodology in which the controller is assumed as a neural network and the dynamical model of the system is identified through the training stages of the neural model. The simulation results show that the neural control method can be effectively applied in monitoring and controlling the satellite networks, without the necessity of determining the mathematical model of the system.
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ASME 2013 International Mechanical Engineering Congress and Exposition
November 15–21, 2013
San Diego, California, USA
Conference Sponsors:
- ASME
ISBN:
978-0-7918-5640-6
PROCEEDINGS PAPER
Modeling and Control of a Network of Cooperative Satellites Using Neural Networks
Atefeh Einafshar,
Atefeh Einafshar
University of British Columbia, Vancouver, BC, Canada
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Farrokh Sassani
Farrokh Sassani
University of British Columbia, Vancouver, BC, Canada
Search for other works by this author on:
Atefeh Einafshar
University of British Columbia, Vancouver, BC, Canada
Farrokh Sassani
University of British Columbia, Vancouver, BC, Canada
Paper No:
IMECE2013-65962, V011T06A005; 6 pages
Published Online:
April 2, 2014
Citation
Einafshar, A, & Sassani, F. "Modeling and Control of a Network of Cooperative Satellites Using Neural Networks." Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition. Volume 11: Emerging Technologies. San Diego, California, USA. November 15–21, 2013. V011T06A005. ASME. https://doi.org/10.1115/IMECE2013-65962
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