Bibliographic record
Abstract
This paper presents an introductory overview on the development of fault-tolerant con- trol systems. For this reason, the paper is written in a tutorial fashion to summarize some of the important results in this subject area deliberately without going into details in any of them. How- ever, key references are provided from which interested readers can obtain more detailed information on a particular subject. It is necessary to mention that, throughout this paper, no efforts were made to provide an exhaustive coverage on the subject matter. In fact, it is far from it. The paper merely represents the view and experience of its author. It can very well be that some important issues or topics were left out unintentionally. If that is the case, the author sincerely apologizes in advance. After a brief account of fault-tolerant control systems, particularly on the original motivations, and the concept of redundancies, the paper reviews the development of fault-tolerant control systems with highlights to several important issues from a historical perspective. The general approaches to fault-tolerant control has been divided into passive, active, and hybrid approaches. The analysis techniques for active fault-tolerant control systems are also discussed. Practical applications of fault- tolerant control are highlighted from a practical and industrial perspective. Finally, some critical issues in this area are discussed as open problems for future research/development in this emerging field.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".