Real-Time Dynamic Optimization of Controllable Linear Systems
Why this work is in the frame
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Bibliographic record
Abstract
A real-time optimization controller is developed to steer a linear time-varying control system to closed-loop trajectories that optimize a cost functional of interest. When advantage is taken of the differential flatness of the linear systems, a basis function approach is used to parameterize the open-loop trajectories of the system and to approximate the optimal solution of the finite time optimal control problem. An adaptive optimization method is used to formulate the real-time optimization scheme. The problem is posed as a real-time optimal trajectory generation problem in which the approximate optimal trajectories are computed using an extremum-seeking approach. The control algorithm provides tracking of the approximate optimal trajectories. Two optimal control problems are considered to demonstrate the application of the technique. It is shown that the technique can be successfully implemented in real time.
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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