Relaxed LMI conditions for control of nonlinear Takagi-Sugeno models
Bibliographic record
Abstract
Linear Matrix Inequalities (LMI) optimization problems became the tool of choice for fuzzy control in the 1990's. Many nonlinear systems can be modelled as fuzzy systems, so fuzzy control may be considered as a nonlinear control technique. Useful results have been obtained using LMIs, however some sources of conservativeness remain when compared to other nonlinear approaches. This thesis deals with such issues of conservativeness and discusses some ideas on overcoming them. This document is the result of a lot of hard work. There are lots of people I would like to thank for a huge variety of reasons. First of all, I would like to thank my Supervisor, Antonio Sala. I could not have imagined having a better advisor for my PhD. His knowledge, commonsense and perspective have helped make my research both prolific and interesting. Thank-you to Jose Lus Navarro and Pedro Albertos for supporting me at the beginning of my journey in research. I would also like to
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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) | 0.001 | 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 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".