Nonlinear modeling, control, and simulation of flexible spacecrafts for attitude recovery
Bibliographic record
Abstract
The problem of Attitude Recovery of rigid and flexible spacecraft/satellite is investigated using the feedback linearization control approach. The attitude and flexible dynamics equations for a class of spacecraft/satellite are presented. Since the flexible spacecraft is under-actuated, the input-output linearization technique was specifically used to break up the system into two distinct parts, namely (1) an external linearizable system for which a linear controller can be easily implemented and (2) an internal nonlinear unobservable system for which the associated zero dynamics is shown to be asymptotically stable for a representative case. The overall closed-loop stability of the flexible satellite is analyzed rigorously and shown to be asymptotically stable using Lyapunov's method. In order to design and analyze attitude control system for satellites, it is important to be able to simulate the dynamics of the spacecraft. Hardware-in-the-loop simulations of a spacecraft, such as air-bearing spacecraft simulators, are not only expensive to build, but they cannot provide the full experience of micro-gravity. An alternative is to have a high fidelity software simulator. Consequently, the resulting nonlinear and coupled equations of the satellite are implemented into a high-fidelity, user-friendly simulation environment, named the Flexible Spacecraft Simulator (FS2). The development and utilization of the FS2 for our research will also be presented.
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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 | 0.000 |
| 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".