Control Allocation-based Fault-tolerant Control for Over-actuated Systems with Saturation and Imperfect Fault Information
Bibliographic record
Abstract
To guarantee the stability of the over-actuated systems in the presence of actuator faults, saturation and imperfect fault information, two different design methods based on control allocation are provided in this paper, which can estimate optimally the attraction domain of the closed-loop system. By exploiting virtual control concept and adopting the pseudo inverse method for control allocation, the bound of difference between virtual control and desired virtual control is estimated, given a bounded fault diagnosis error. Then, a saturating linear system with bounded disturbance is constructed for the design of the baseline controller, and the attraction domain of the overall system is approached with the ellipsoidal set by state feedback. Two different forms of state feedback are discussed in details for the design of the baseline controller. Finally, the ADMIRE aircraft model is used to test the design techniques. The simulation results demonstrate that the two design methods can cope with the given actuator faults effectively and the stability of the closedloop system can be guaranteed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".