Prescribed-Time Semi-Global Control for a Class of Nonlinear Uncertain Systems by Linear Time-Varying Feedback
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Bibliographic record
Abstract
The prescribed-time semi-global control for a class of time-varying uncertain systems under a nonlinear growth condition is achieved via linear time-varying feedback. The involved nonlinear uncertainties are categorized as unmatched uncertainties (depending on states and time) and matched uncertainties (depending on time only). Both state feedback and observer-based output feedback are constructed relying on the properties of parametric Lyapunov equations and the time-varying gains acquired by solving scalar differential equations. The proposed output feedback approach features a separation principle, that is, the construction of prescribed-time observer and prescribed-time state feedback is conducted separately. The proposed control scheme is validated by simulations carried out on a standard mechatronics system with complicated loads.
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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) | 0.001 | 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