Fault Tolerant Control System against actuator failures based on re-configuring reference input
Bibliographic record
Abstract
This paper deals with Active Fault Tolerant Control System when performance degradation occurs on system due to actuator faults. Fault Tolerant Control methods are generally focused their attention to design a reconfigurable controller in order to reach the nominal closed-loop performances as close as possible. In the presence of such faults, the steady-state performance can be also degraded due to the physical actuator limits. However, only few contributions concern the reference (also called command) input adjustments to prevent the actuators from saturation. Inspired by, the main contribution is to consider the reconfigured system as an open loop controlled by a classical Model Predictive Control strategy in order to design ldquoone-linerdquo new reference input trajectories. The added value of this method is to reduce the energy spent to achieve desired closed-loop performance and consequently to maintain a reliable system in dynamical way. The effectiveness of the proposed approach is illustrated using a classical benchmark corrupted by abrupt actuator faults: the three-tank system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".