Magnetic Attitude Control with Impulsive Thrusting Using the Hybrid Passivity Theorem
Bibliographic record
Abstract
Passivity-based design approaches for hybrid attitude control of spacecraft using continuous magnetic torques and impulsive thrusts are proposed. The classical passivity notions, the passivity theorem, and the Kalman–Yakubovich–Popov conditions are extended to hybrid systems and used for linear passivity-based controller design. The plant’s output dynamics are manipulated such that the hybrid extended (time-varying) Kalman–Yakubovich–Popov conditions are satisfied, hence establishing the plant’s passivity. Then, evoking the hybrid passivity theorem that states the negative feedback interconnection of a passive hybrid plant and an input strictly passive hybrid controller is input–output stable, two such controllers are proposed: a proportional feedback controller with constant positive gains; and a dynamic compensator, developed using the hybrid algebraic (time-invariant) Kalman–Yakubovich–Popov conditions, that actively adjusts the gains based on the system’s dynamics and response. Numerical simulations validate the proposed controllers’ functionality and suggest performance improvements gained via hybrid control. The effects of random sensor noise are also studied, and the results suggest enhanced immunity in terms of performance arising from the use of the dynamic compensator instead of the constant-gain controller.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".