Control and Fault Accommodation for Attitude Control Subsystem of Formation Flying Satellites Subject to Constraints
Bibliographic record
Abstract
Stringent precision requirements, communication limitations and automated fault accommodation are three important considerations that need to be taken into account in design of formation control of satellites. In this work a more accurate relative state modeling for the attitude dynamics is developed and a semi-decentralized control strategy is proposed that is accomplished by the model predictive control (MPC) scheme. The proposed MPC incorporates the effects of the actuator constraints in design of the control laws. Furthermore, a semi-decentralized active system recovery scheme is proposed that uses on-line fault information to compensate for the identified characteristics losses under actuator fault conditions. \n \nSimulation results for a team of four satellites in formation are presented and the formation precision is compared with the centralized scheme. The results verify that the proposed semi-decentralized strategy yields a quite satisfactory formation performance in a sense that the team behaves similar to a centralized MPC control scheme, however without imposing significant computational complexity that is associated with solving the problem of high dimension with stringent communication requirement as in the centralized scheme. \n \nMoreover, the performance of our proposed semi-decentralized recovery scheme is compared with the centralized recovery scheme subject to the reaction wheel (RW) faults in the attitude control subsystem (ACS) of the formation flying satellites. The proposed semi-decentralized recovery scheme satisfies the formation recovery specifications and also imposes lower fault compensation control effort cost as compared with the centralized recovery scheme. It has been validated through multiple fault severity scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".