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
Coco, Andrea
Promotor(s) : Collette, Christophe
Date of defense : 5-Sep-2024/6-Sep-2024 • Permalink : http://hdl.handle.net/2268.2/21136
Details
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 |
Date of defense : | 5-Sep-2024/6-Sep-2024 |
Advisor(s) : | Collette, Christophe |
Committee's member(s) : | Bruls, Olivier
Kerschen, Gaëtan |
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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