Research on Phase Current Reconstruction and Fault-Tolerant Control for EMA Control Unit
Bibliographic record
Abstract
During the whole-life service of the aviation electro-mechanical actuator, the current sensor may be faulty due to the manufacturing demerits or exposure to harsh operating conditions. In order to improve the fault tolerance capability of the system, a new three-phase current reconstruction method for Electro-Mechanical Actuator (EMA) control unit is proposed in this paper. Firstly, the limitations posed by the installation position of the DC-link current sensor on the traditional phase current reconstruction method are presented. In real applications, considering the DC-link current sensor is normally placed before the DC-link capacitor due to the utilization of busbar, the relationship between EMA DC-link current and phase current is analyzed in detail. Later, based on the above relationship, the phase current of EMA can be successfully reconstructed. With the reconstructed phase current, a fault-tolerant control method for EMA is also presented. Simulation and experimental results verify the effectiveness of the proposed method and the feasibility of the fault-tolerant control method. Compared to the conventional method, the proposed current reconstruction method reduces the dependency on the accuracy and rate of the DC-link current measurement. Besides, the proposed method shows a low computational burden and has practical engineering value.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".