Computationally Efficient Adaptive Model Predictive Control for Constrained Linear Systems with Parametric Uncertainties
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Bibliographic record
Abstract
This paper investigates adaptive model predictive control (MPC) for constrained linear systems subject to multiplicative uncertainties. Different from robust MPC considering the worst-case disturbances, the proposed solution updates the unknown system model online based on input and state histories. We firstly propose a parameter estimator based on recursive least square technique, which guarantees the nonincreasing estimator error and a contractive sequence of uncertainty sets. Then a computationally tractable adaptive MPC method is developed to handle the multiplicative uncertainties directly by using the polytopic tube. Instead of designing the tube offline, we consider the homothetic tube in this work, where the tube parameters are the MPC optimization problem. This strategy allows that the tube can be optimized based on the updated system model to reduce the conservatism. We have proved that the proposed adaptive MPC method is recursively feasible and the closed-loop system is asymptotically stable. Finally, a numerical example is given to evaluate the proposed 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