Adaptive Model Predictive Control for A Class of Constrained Linear Systems with Parametric Uncertainties
Bibliographic record
Abstract
This paper investigates adaptive model predictive control (MPC) for a class of constrained linear systems with unknown model parameters. This is also posed as the dual control problem consisting of system identification and regulation. We first propose an online strategy for simultaneous unknown parameter identification and uncertainty set estimation based on the recursive least square technique. The designed strategy provides a contractive sequence of uncertain parameter sets, and the convergence of parameter estimates is achieved under certain conditions. Second, by integrating tube MPC with proposed estimation routine, the developed adaptive MPC provides a less conservative solution to handle multiplicative uncertainties. This is made possible by constructing the polytopic tube based on the consistently updated nominal system and uncertain parameter set. In addition, the proposed method is extended with reduced computational complexity by sacrificing some degrees of optimality. We theoretically show that both designed adaptive MPC algorithms are recursively feasible, and the perturbed closed-loop system is asymptotically stable under standard assumptions. Finally, numerical simulations and comparison are given to illustrate 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 | 0.001 |
| 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".