Resource Efficient Modelling of IPMSM Drive Systems Based on LUT Flux-Current Mapping
Bibliographic record
Abstract
Interior permanent magnet synchronous machines (IPMSMs) are widely used in industrial automation, electric vehicles, and precision motion control due to their high efficiency, compact design, and excellent torque characteristics. Accurate simulation of IPMSM drives, especially under nonlinear conditions, is critical for effective control design. Field-oriented control (FOC) requires precise flux-current mapping, typically handled using lookup tables (LUTs), which offer accuracy but demand significant memory. This paper examines the accuracy of LUT-based IPMSM models with varying sizes of flux-current maps (LUT10-LUT80) used for electromagnetic transient (EMT) simulations in MATLAB/Simulink. Both motoring and generating modes are evaluated in terms of numerical accuracy, memory usage, and computational time. The results show that while highresolution LUT maps improve dynamic accuracy, moderateresolution tables achieve comparable performance with lower resource consumption. These findings provide practical guidelines for selecting the LUT resolution to achieve high fidelity and resource efficiency in modelling IPMSM drive systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".