Design, Modeling, and Model-Free Control of Permanent Magnet-Assisted Synchronous Reluctance Motor for e-Vehicle Applications
Bibliographic record
Abstract
This paper describes the model-free control approaches for permanent magnet-assisted (PMa) synchronous reluctance motors (SynRMs) drive. The important improvement of the proposed control technique is the ability to determine the behavior of the state-variable system during both fixed-point and transient operations. The mathematical models of PMa-SynRM were firstly written in a straightforward linear model form to show the known and unknown parts. Before, the proposed controller, named here the intelligent proportional-integral (iPI), was applied as a control law to fix some unavoidable modeling errors and uncertainties of the motor. Lastly, a dSPACE control platform was used to realize the proposed control algorithm. A prototype 1-kW test bench based on a PMa-SynRM machine was designed and realized in the laboratory to test the studied control approach. The simulation using MATLAB/Simulink and experimental results revealed that the proposed control achieved excellent results under transient operating conditions for the motor drive’s cascaded control compared to traditional PI and model-based controls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".