Integrated Structure/Control Optimisation Applied to the BIOMASS Earth Observation Mission
Bibliographic record
Abstract
A new approach of an integrated structure/control co-design methodology is developed based on the recognition that a high degree of coupling exists between the control and structural disciplines in the control of flexible space structures. A unified computational framework is developed gathering methodologies and tools coming from robust control theory and advanced worst case analysis techniques together with mechanical modelling engineering tools. Within this environment, design iterations consist in updating critical control and structure design variables by assessing controlled performance while minimizing structural mass. The optimisation process utilises a Differential Evolution algorithm. Multiobjective optimisation is also supported highlighting the compromise between mechanical and control objectives. The Linear Fractional Transformation formalism provides an uncertain representation of the spacecraft dynamics which is considered during the controller synthesis and analysis processes together, managed in the H∞/μ setting. Traditional Monte-Carlo simulations evaluate the robust performance of the controller design whilst optimisation-based worst-case analysis has been implemented to increase the efficiency of the worst-case extraction. This paper presents the work supported by the European Space Agency in the scope of the robust AOCS technology program initiated to support the BIOMASS mission; a candidate for the Earth Explorer Core 7 missions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".