A Neural-Network-Based Electric Machine Emulator Using Neuro-Fuzzy Controller for Power-Hardware-in-the-Loop Testing
Bibliographic record
Abstract
The emulation of permanent magnet synchronous machines (PMSMs) is critical for the advancement of power electronics and drive converter testing, particularly within power-hardware-in-the-loop (PHIL) platform. Despite significant progress, and developing accurate machine models, the amount of resources and memory used by these accurate models are not ideal for real-time applications due to added latency. Hence, a research gap exists in developing models that while accurately and efficiently replicate the dynamic behaviors of the machine model under various operating conditions, are light in resource usage. This paper addresses this gap by introducing an artificial neural network (ANN)-based machine modeling approach and combines it with a neuro-fuzzy-based control strategy to ensure robust and precise performance of the system, that is to minimize the error between the electric machine emulators (EME) and physical PMSM test results. The ANN model requires only 0.68 KB of memory compared to the 4 MB needed for traditional 1,000 × 1,000 LUT-based models, which incur greater latency due to cache limitations and interpolation demands despite lower floating-point operation (FLOP) requirements. By using this optimized ANN model with an adaptive ANFIS controller, the proposed system So, the main objective is to enhance the performance and accuracy of EMEs in PHIL testing environments. The ANN model provides a resource-efficient yet precise representation of the PMSM, while the adaptive neuro-fuzzy inference system (ANFIS)-based controller dynamically adjusts its membership functions to adapt to changing system dynamics and loading conditions and provide proper control command.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".