A comparison between fuzzy, fractional-, and integer-order controllers for small satellites attitude control
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Bibliographic record
Abstract
The design of an optimal and robust controller is critical for any application subjected to time delays between the start of an event at one point in a system and its resulting action at another point. This paper discusses and compares three different controllers, namely fuzzy, fractional-order and integer-order proportional integral derivative (FPID, IPID) controllers for the purpose of small satellites attitude control. This paper introduces the background knowledge related to the three different controllers. The second one evaluates their performance based on their ability to provide an optimal attitude control scheme for small satellites. The controllers are evaluated in the time-domain based on different performance measures (e.g., overshoot, transient response, and steady-state error). Simulation results illustrate the superiority of FPID over IPID and fuzzy controllers for this specific application and reinforces the power of fractional calculus in dealing with nonlinear systems.
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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) | 0.001 | 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