Machine learning assisted controller design for voltage regulation in a more electric aircraft power system
Bibliographic record
Abstract
Abstract Three-stage synchronous generators (TSSG) are used in a more electric aircraft (MEA) to power various parts of the aircraft, such as environmental, hydraulic, avionics, and mechanical systems. However, regulating the voltage output of TSSGs in the presence of speed and load variations presents a significant challenge due to the dynamic couplings inherent in the system. In this work, a machine learning-assisted controller (MLAC) is designed to regulate the output voltage of the TSSG system at variable speeds. Moreover, data-driven techniques are employed for the training, testing, and deployment of the proposed MLAC controller. Furthermore, variants of meta-heuristics algorithms are investigated to fine-tune the response of the proposed controller through the selection of optimal hidden and output layer weights. Additionally, the transparency of the proposed controller is addressed and the optimized weights are auto-tuned with the assistance of a fuzzy logic controller (FLC). The resultant intelligent controller is evaluated in MATLAB/Simulink environment on a nonlinear model of the three-stage generator. The effectiveness and validity of the proposed approach in controlling the output voltage of the TSSG system are confirmed through comprehensive results analysis.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".