Permanent magnetic brushless DC motor magnetism performance depends on different intelligent controller response
Bibliographic record
Abstract
This paper presents an intelligent controller to improve the performance of a permanent magnetic brushless DC motor (PM-BLDCM) in a ball-and-beam system (BBS). A hybrid interval type-2 fuzzy sliding controller (HFSC) was designed and compared with a conventional extended Karnik–Mendel (EKM)-type controller. The performance of the intelligent controller can affect the control phase margin condition, which regulates the DC motor rotational flux directly due to factors of voltage and current. Based on this result, the flux distribution of the magnetic material of the BLDCM was investigated through finite element analysis (FEA). For the torque response, which is critical to the performance of the BBS, the proposed intelligent controller exhibited faster response than that achieved by a conventional approach. The computation efficiency of the developed controller was significantly enhanced, and the computing time was reduced by more than 90%. Simulation results show that the torque required to achieve a prescribed control action was reduced by more than 10%. These results validate the performance of the proposed controller.
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 | 0.001 |
| 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".