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2018 | OriginalPaper | Chapter

Neural Network-Based Self-triggered Attitude Control of a Rigid Spacecraft

Authors : Shuai Sun, Mengfei Yang, Lei Wang

Published in: Proceedings of 2017 Chinese Intelligent Automation Conference

Publisher: Springer Singapore

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Abstract

Future spacecraft systems will require careful co-design over both physical and cyber elements to provide a better performance for holistic system. In this paper, a neural network (NN) based self-triggered control approach is introduced for the spacecraft attitude control problem. The self-triggered control is a resource-aware strategy which allows a reduction of the computation and communication demands, while still guaranteeing desirable closed-loop behavior. The NN is used for approximation of the triggered condition. We derive the equations of attitude motion for the spacecraft, and then develop a general procedure leading to NN based self-triggered feedback control implementations on a rigid spacecraft. Finally, the efficiency and feasibility of the obtained results are illustrated by means of a numerical spacecraft example.

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previous chapter Vehicle Tracking Based on Structured Output SVM Using Retinex and Mutual Information
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Metadata
Title
Neural Network-Based Self-triggered Attitude Control of a Rigid Spacecraft
Authors
Shuai Sun
Mengfei Yang
Lei Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Proceedings of 2017 Chinese Intelligent Automation Conference

Print ISBN: 978-981-10-6444-9

Electronic ISBN: 978-981-10-6445-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-6445-6_77