Time-Domain Model Matching Under General Norms via Sparse Matrix Methods
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Bibliographic record
Abstract
This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.
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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) | 0.000 | 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 | 0.001 |
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