Evaluation of a new algorithm for model predictive control based on non-feasible search directions using premature termination
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Bibliographic record
Abstract
Model predictive control (MPC) is an attractive control methodology because it can deal with constraints directly. Unfortunately however, it is often not possible to implement the strategy, because large MPC horizon times can cause requirements of excessive computational time in solving the quadratic programming (QP) optimization that is necessary at each sampling interval. This motivates the study of developing more effective algorithms for solving QP problems, and of using approximate solutions to the QP problem associated with MPC. In this paper the premature termination of algorithms which solve the QP subproblem are evaluated experimentally using three different algorithms: a proposed new active set method, a conventional existing active set method and a primal-dual interior point method. Results from three representative linearized industrial control system examples are reported on in this paper. Favorable results are found using the proposed new active set method.
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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 | 0.000 |
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