Synthesis of stochastic fault tolerant control systems with random FDI delay
Why this work is in the frame
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Bibliographic record
Abstract
In this work, the synthesis of fault tolerant control (FTC) for stochastic stability and H ∞ performance is studied. Occurrence of faults in the system is governed by a Markov Chain, so the open-loop system is modelled as a linear system with Markovian jumping parameters. The fault detection and isolation (FDI) decision is modelled as another random process that will indicate the fault mode after an exponentially distributed random delay. This stochastic formulation of FTC concerns the random nature of faults and the effect of random fault detection delay on the overall system, and can be regarded as an extension to the traditional reconfigurable control problem. In this paper, output feedback controllers are designed using an iterative LMI algorithm for mean exponential stability (MES) and the H ∞ performance. Model uncertainties and external disturbance are also considered in the robust design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it