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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master thesis and internship[BR]- Master's thesis : State-of-the-Art Custom and Model Predictive Control Steering Algorithms for Control Moment Gyroscope Clusters[BR]- Internship

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Coco, Andrea ULiège
Promotor(s) : Collette, Christophe ULiège
Date of defense : 5-Sep-2024/6-Sep-2024 • Permalink : http://hdl.handle.net/2268.2/21136
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Title : Master thesis and internship[BR]- Master's thesis : State-of-the-Art Custom and Model Predictive Control Steering Algorithms for Control Moment Gyroscope Clusters[BR]- Internship
Author : Coco, Andrea ULiège
Date of defense  : 5-Sep-2024/6-Sep-2024
Advisor(s) : Collette, Christophe ULiège
Committee's member(s) : Bruls, Olivier ULiège
Kerschen, Gaëtan ULiège
Language : English
Number of pages : 98
Keywords : [en] CMG
[en] MPC
[en] Singularity avoidance
[en] AOCS
Discipline(s) : Engineering, computing & technology > Aerospace & aeronautics engineering
Target public : Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Cours supplémentaires destinés aux étudiants d'échange (Erasmus, ...)
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] This thesis presents an in-depth study of steering algorithms for clusters of Control Moment Gyroscopes (CMGs), focusing on enhancing satellite agility in the competitive landscape of the New Space era. As Earth observation missions become increasingly vital, the
need for more agile satellites—capable of rapidly acquiring multiple targets—has grown.
While CMGs have proven effective on platforms like the International Space Station (ISS),
their adoption in the commercial sector has been limited due to the complexities and singularities inherent in their control laws.
Over the years, various steering algorithms have been developed to manage these singulariities and ensure robust performance. This work critically compares the most recognized
algorithms and introduces custom adaptations to address known challenges and enhance
overall performance. The VEO Control Steering Logic (VCSL),co-developed with the
company is proposed as a hybrid solution, integrating the strengths of state-of-the-art
algorithms to optimize both singularity avoidance and escape.
Beyond traditional steering logic, this thesis explores the potential of Model Predictive
Control (MPC) as a higher-level solution for singularity management. By predicting and
optimizing control inputs over a future horizon, MPC can inherently avoid singularities
during complex manoeuvres. Two MPC variants were developed: Mode 1, a non-linear
MPC, and Mode 2, a convex MPC designed to leverage the computational benefits of
convex optimization. While both approaches show promise, Mode 1 outperforms Mode 2
in terms of pointing accuracy, though the latter offers significant advantages in computational efficiency and robustness of the numerical optimization.
The results demonstrate the potential for these advanced steering algorithms to significantly improve the operational capabilities of satellites equipped with CMGs, paving the
way for more agile, responsive, and capable spacecraft in the commercial space sector.


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Author

  • Coco, Andrea ULiège Université de Liège > conv. Erasmus en sc. appl.

Promotor(s)

Committee's member(s)

  • Bruls, Olivier ULiège Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire des Systèmes Multicorps et Mécatroniques
    ORBi View his publications on ORBi
  • Kerschen, Gaëtan ULiège Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux
    ORBi View his publications on ORBi
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