A fuzzy optimal controller for the mechatronic system with non-smooth non-linearities
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Bibliographic record
Abstract
In this paper, an optimal controller design for a system with time varying and non-smooth non-linearities described by Takagi-Sugeno fuzzy model is presented. The controller design is posed in an optimisation problem subject to linear matrix inequalities (LMIs) constraints. Multiple positive definite matrices, as a part of the optimisation results, are combined to build a single positive definite matrix that guarantees the stability of the closed-loop system. State feedback controllers are constructed with the optimisation results obtained for each local linear system in the respective fuzzy rule. The controller of the whole system is presented as a fuzzy combination of these local feedback controllers. The controller is then applied to a harmonic drive system which is subject to the non-linear and temperature varying friction. The performance of the controller is verified through the simulation results of tracking reference signals in a temperature varying environment.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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