An integrated fault diagnosis and safe‐parking framework for fault‐tolerant control of nonlinear systems
Bibliographic record
Abstract
SUMMARY In this work, we consider the problem of designing an integrated fault diagnosis and fault‐handling framework to deal with actuator faults in nonlinear systems. A model‐based fault diagnosis design is first proposed, which can not only identify the failed actuator but also estimate the fault magnitude. The fault information is obtained by estimating the outputs of the actuators and comparing them with the corresponding prescribed control inputs. This methodology is developed under state feedback control and generalized to deal with state estimation errors. Then the safe‐parking framework developed previously (to handle the case where the failed actuator reverts to a known fixed value) for fault‐tolerant control is extended to handle the case where an actuator seizes at an arbitrary value. The estimate of the failed actuator position provided by the fault diagnosis design is used to choose a safe‐park point, at which the system operates temporarily during fault repair, from those generated offline for a series of design values of the failed actuator position. The discrepancy between the actual value of the failed actuator position and the corresponding design value is handled through the robustness of the control design. The efficacy of the integrated fault diagnosis and safe‐parking framework is demonstrated through a chemical reactor example. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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".