Research on PMSM control without speed sensorless applied to industrial electric drive system based on ADSMC method
Bibliographic record
Abstract
The paper research, calculates, and designs an industrial electric drive system control such as: computer numerical control (CNC) machining machines, milling machines, and grinding machines, with sensorless permanent magnet synchronous motors (PMSM) based on measuring current components, axial position and applied voltage to obtain information about rotation angle and speed for PMSM based on adaptive sliding mode control (ADSMC) method. Here an optimal sliding surface will be designed to demonstrate faster convergence than conventional sliding mode control. Then, an adaptive law is researched and developed to make the control parameters, especially the switching gain, updated quickly online. Therefore, the motor noise can be effectively reduced and the system can be better eliminated from noise, Chattering, and nonlinear noise. Finally, a reference model was created, the exponential decay curve was applied to track the angular position error. The ADSMC system with model reference proposed by the authors in the paper has combined the advantages of sliding mode control method and adaptive control method according to the sample model. The simulation results show that the performance is achieved faster and the control process is more accurate, the error of speed and angular position (less than 0.01%) compared to other control methods.
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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 | 0.001 |
| 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) | 0.001 | 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".