Brushless DC Motor Speed Control Based on Advanced Sliding Mode Control (SMC) Techniques
Bibliographic record
Abstract
Recently, the great advantages of the Brushless DC Motors (BLDCM) such as their simple design, high applied output force (torque), long term usage and speed stability encourage the designers to wide use these motors in various industries. Whilst, BLDC systems are characterized by their uncertainties and non-linearity. One of the famous control techniques in handling nonlinear and uncertain systems is the Sliding Mode Control (SMC). The main contribution in this paper is applying advanced SMC techniques such as adaptive SMC and fuzzy SMC approaches for effective speed regulation of BLDCM in the absence and presence of external load. The simulation performance of speed regulation for BLDCM using the designed approaches is compared with a classical Proportional-Integral-Derivative (PID) controller to validate the success of the proposed advanced SMC techniques in improving the system characteristics (settling time, steady state error, rise time and disturbance & noise rejection). Our simulations run under the umbrella of MATLAB 2017.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".