Adaptive Neural Current Controllers for Decentralized and Non-Sinusoidal Multiphase Drives in Healthy and Faulty Modes
Bibliographic record
Abstract
This paper proposes a neural current controller for decentralized non-sinusoidal permanent magnet multiphase machines. This architecture has many advantages, such as increased reliability and simplified motor design, but requires new algorithms for decentralized control. The neural network current controller features a so-called hybrid operation, with one part trained offline and a second part working online. Using the machine’s model, the network is first trained offline based on multi-agent reinforcement learning (RL) and is then implemented in the real machine. In a second step, an online calibration using Least Mean Square (LMS) to take account of the uncertainties of the model is applied only to the output layer to reduce computational burden. This solution combines the speed of offline (forward) and the adaptability of online (feedback) training. This proposal has been validated in simulation and tested on a highly non-sinusoidal 7-phase test bench to demonstrate the feasibility of the proposed approach.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".