Observer compensation‐based model predictive fault‐tolerant control for industrial processes: A high‐order fully actuated system‐method
Bibliographic record
Abstract
Abstract This paper proposes a fault‐tolerant predictive tracking control strategy for the industrial process based on a high‐order fully actuated (HOFA) system method. First, a novel system representation method is employed to model the industrial process as a HOFA system. Subsequently, a fault‐compensated HOFA predictive fault‐tolerant control scheme is introduced, which includes two components: HOFA feedback stabilization and HOFA model predictive tracking control. Within this framework, an incremental predictive model is developed to replace the reduced‐order prediction model by employing a Diophantine equation. The cost function, which incorporates tracking performance, is subsequently minimized using multi‐step output prediction. Additionally, sufficient conditions for the stability and tracking performance of the closed‐loop HOFA system are derived. The advantage of this approach lies in its ability to reduce system dimensionality while effectively eliminating the impact of faults on system stability through the introduction of an observer‐based compensation concept. This ensures stable operation under fault conditions, or even operation unaffected by faults. Finally, the effectiveness and reliability of the proposed method are validated through a case study involving a nonlinear reactor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".