Robust Adaptive Model Predictive Control of Nonlinear Systems
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
In this chapter we have demonstrated the methodology for adaptive MPC, in which the adverse effects of parameter identiï¬cation error are explicitly minimized using a robust MPC approach. As a result, it is possible to address both state and input constraints within the adaptive framework. Another key advantage of this approach is that the effects of future parameter estimation can be incorporated into the optimization problem, raising the potential to signiï¬cantly reduce the conservativeness of the solutions, especially with respect to design of the terminal penalty. While the results presented here are conceptual, in that they are generally intractable to compute due to the underlying min-max feedback-MPC framework, this chapter provides insight into the maximum performance that could be attained by incorporating adaptation into a robust-MPC framework.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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