REDUCED-ORDER MODELS FOR FEEDBACK STABILIZATION OF LINEAR SYSTEMS WITH A SINGULAR PERTURBATION MODEL
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Bibliographic record
Abstract
The problem of output feedback stabilization of linear systems based on a reduced-order model is addressed in this paper. New reduced-order models are proposed for the output feedback design of linear systems with a singular perturbation model. An output feedback controller with a zero steady-state gain matrix is proposed for stabilizing this kind of system. It is shown that with the proposed controller the reduced-order model based feedback design can guarantee the actual closed-loop stability for the sufficiently small perturbation parameter. This approach can overcome the difficulties in the existing design method using the so-called zeroth-order approximation model, whose validity is highly dependent on the value of the perturbation parameter.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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