Hybrid iterative learning fault‐tolerant guaranteed cost control design for multi‐phase batch processes
Bibliographic record
Abstract
Abstract A robust design of a hybrid iterative learning fault‐tolerant guaranteed cost control scheme is proposed for a class of multi‐phase batch processes under faults and disturbances. Firstly, based on an equivalent two‐dimensional Fornasini‐Marchesini (2D‐FM) switched system with actuator faults varying within an allowable range, a 2D robustly hybrid controller that includes a robust hybrid extended feedback control to ensure performance over time and a hybrid iterative learning control to improve the tracking performance from cycle to cycle is formulated to guarantee the closed‐loop convergence and the H∞ performance level with a cost function bearing the upper bounds for all admissible uncertainty and actuator failures. Secondly, 2D system theory and the average dwell time strategy are adopted to derive conditions for guaranteeing exponential stability of the corresponding system in terms of linear matrix inequalities (LMIs), where the suboptimal hybrid guaranteed cost controller, which minimizes the quadratic performance index and rejects external disturbances, is designed using a convex optimization under LMI constraints. Finally, the proposed method is further verified by simulation on an injection molding process in comparison with traditional methods.
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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".