Computationally Efficient Adaptive Model Predictive Control for Constrained Linear Systems with Parametric Uncertainties
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".