Dynamic Neural Units for Adaptive Magnetic Attitude Control of a Satellite
Why this work is in the frame
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Bibliographic record
Abstract
Various controllers are available for the attitude control of magnetically actuated satellite including feedforward neural network. However, dynamic neural network has not been implemented for attitude control of satellite. Dynamic neural network based on dynamic neural units has the capability to handle any type of nonlinearity besides it can adapt itself in real time. The problem of attitude control for an earth pointing satellite using magnetic actuators and the adaptive neural controller, based on dynamic neural units through inverse modeling, has been addressed in this paper. Besides, weights normalization of dynamic neural units has been suggested to ensure their convergence for proper learning. Being adaptive, the proposed neural controller not only takes care of any unknown disturbance torque but also can adapt itself following the large parameter changes in the plant, and therefore, is robust to any unplanned change in the parameters of the plant such as moment of inertia. It has been shown that stabilization accuracy of the plant is better under neural controller as compared to the PD controller.
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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 | 0.000 | 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) | 0.000 | 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