Robust MPC design for multi-model infinite-dimensional distributed parameter systems
Bibliographic record
Abstract
Infinite-dimensional systems are essential for describing complex phenomena that exhibit continuous spatial and temporal variations. This article introduces a robust model predictive control (RMPC) design to regulate constrained multi-model infinite-dimensional systems governed by a class of hyperbolic/parabolic partial differential equations (PDEs). Model uncertainty stems from system parameters that are imprecisely determined, but can be quantitatively characterized within a certain range. The RMPC algorithm is designed in a discrete-time infinite-dimensional setting, achieved through the structure-preserving Cayley–Tustin transformation without model reduction nor spatial approximation. Robustness of the controller is ensured via constraining the future cost for each model dynamics accounting for uncertainty description. Properties of the closed-loop system are discussed, including feasibility, convergence, and asymptotic stability. The proposed controller is implemented by considering three typical infinite-dimensional distributed parameter process models, with simulation demonstrating the effectiveness and enhanced performance of the RMPC over the nominal model predictive controller.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".