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Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-06
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
The paper presents an adaptive system for the control of small satellites’ attitude by using a pyramidal cluster of four variable-speed control moment gyros as actuators.
Starting from the dynamic model of the pyramidal cluster, an adaptive control law is designed by means of the dynamic inversion method and a feed-forward neural network-based nonlinear subsystem; the control law has a proportional-integrator component (for the control of the reduced-order linear subsystem) and an adaptive component (for the compensation of the approximation error associated with the function describing the dynamics of the nonlinear system).
The software implementation and validation of the new control architecture are achieved by using the Matlab/Simulink environment.
American Psychological Association (APA)
Lungu, Mihai& Lungu, Romulus. 2019. Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster. Complexity،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1131096
Modern Language Association (MLA)
Lungu, Mihai& Lungu, Romulus. Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster. Complexity No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1131096
American Medical Association (AMA)
Lungu, Mihai& Lungu, Romulus. Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster. Complexity. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1131096
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131096