Fast model predictive control based on sensitivity analysis strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study proposes a new method based on sensitivity analysis to solve a series of sequential parametric linear programmings (LPs) such as that those arise in model predictive control (MPC). The main idea is to find a relationship between each of the two successive parametric LPs by using sensitivity analysis strategy. Tolerance analysis‐based MPC (TA‐ MPC) and sensitivity analysis‐based MPC (SA‐ MPC) are introduced for reducing computational complexity and runtime. TA‐ MPC takes operations per step time, where N and n are the prediction horizon and the number of states, respectively. This approach is very faster than generic optimisation methods but it can be applied only for initial conditions that are near to steady‐state values. SA‐ MPC has not any limitation in usage and it reduces the runtime significantly compared with common solvers. Finally, numerical results indicate the potential of the proposed algorithms.
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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