Data-Driven Plant–Model Mismatch Detection for Closed-Loop LPV System Based on Instrumental Variable Using Sum-of-Norms Regularization
Bibliographic record
Abstract
Models are the key to model-based control strategies. However, due to the nonlinear and time-varying nature of industrial processes, plant–model mismatches are inevitable. Therefore, it is highly desirable to detect mismatches and update the model in a closed-loop system to avoid re-identifying the entire model. In this study, a mismatch detection method based on the instrumental variable that uses sum-of-norms regularization and is robust to noise modeling errors is proposed for linear parameter variation-input–output (IO) models. The introduction of the instrumental variable addresses the challenge that regression variables are corrupted with colored noise. The proposed method integrates the detection and quantification of mismatches into a framework in which significant jumps in parameters can be detected by segmenting the signal using sum-of-norms regularization and the search space of quantification is reduced. Moreover, the method is noninvasive and applicable to closed-loop systems under colored noise disturbances. Finally, the feasibility and robustness of the proposed method under closed-loop conditions are analyzed from a stochastic perspective and demonstrated with representative simulation examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 |
| 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".