Fuzzy Gain Scheduling of Subspace Predictive Controller
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Bibliographic record
Abstract
We present a Fuzzy Gain Scheduling (FGS) method to update Subspace Predictive Controller (SPC) gains in the presence of constraints. The method is denoted by FGS-SPC. Unlike existing approaches, FGS-SPC does not to adapt the system model by updating the subspace predictor matrices, instead it re-tunes existing control parameters solely based on future tracking error, its derivatives and derivatives of past control signal. The SPC gains are updated by applying fuzzy logic rules. The advantage of the approach is in not requiring any persistent excitations for updating the system model. Consequently, FGS-SPC has a faster convergence capability and better time efficiency compared to the conventional SPC approaches. Simulation results illustrate the efficiency of proposed method in presence of noisy data.
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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