Non‐fragile state‐feedback control of uncertain piecewise‐affine slab systems with input constraints: a convex optimisation approach
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Bibliographic record
Abstract
The main contribution of this work is to propose a design technique for state‐feedback control of continuous‐time piecewise‐affine (PWA) systems that is robust not only to the system uncertainties, but also to variations of the controller gains. More specifically, this study presents sufficient conditions to synthesise a robust non‐fragile PWA controller that exponentially stabilises the closed‐loop equilibrium point. Furthermore, these conditions are cast as an optimisation problem subject to a set of linear matrix inequalities (LMIs), which can then be solved efficiently. In addition, a set of LMI conditions are derived to ensure that the control input will always meet a pre‐assigned upper bound. Simulation results demonstrate the effectiveness of the proposed approach.
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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 | 0.000 |
| 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