Bibliographic record
Abstract
This paper presents a fuzzy-adaptive control method that results in the same gain and phase margins as a known linear control. The method uses fuzzy switching to transition between a traditional fuzzy-adaptive control and the linear control. Our novel switching algorithm changes the structure of the fuzzy approximator itself, so that the fuzzy approximator completely transforms into a pure integrator e.g. becoming the integral term in a PID control. The fuzzy inference takes both state error and adaptive-parameter drift as the inputs, so as to detect possible instabilities resulting from unmodeled dynamics, disturbances, and/or adaptive-parameter drift. The fuzzy inference determines how close the error and adaptive-parameter drift are to imposed limiting parameters. In our simulation we find that when the limiting parameters are tuned for peak performance, the proposed method doubles the performance compared to the traditional robust adaptive weight update methods of deadzone, leakage, and e-modification. Moreover, the limiting parameters can be tuned to find this peak performance by trial-and-error without fear of instability, unlike with traditional robust update methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".