Control of Large Angle Attitude Maneuvers for Rigid Bodies Using Sum of Squares
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Bibliographic record
Abstract
The main contribution of this paper is the development of a two step integrated and systematic approach for modeling and control of large angle maneuvers of a rigid body through convex optimization. In the first step, the attitude dynamics of the rigid body are represented by modified Rodriguez parameters (MRP). In the second step, a Lyapunov- based controller synthesis is performed based on the Sum of Squares (SOS) technique. The key observation that makes rigid body control amenable to an efficient (convex) design technique such as SOS is that both quaternions and modified Rodriguez parameters enable the representation of the attitude dynamics of a rigid body by polynomial vector fields. The model proposed in this paper will use MRP instead of quaternions to represent the rigid body attitude to yield a minimal parameterization. The effectiveness of the proposed control technique is shown in simulations for a large angle maneuver corresponding to a satellite rotation about a fixed axis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it