High-precision speed control of induction motors using a multi-pulse voltage source converter and advanced observer-based strategies
Bibliographic record
Abstract
This paper presents a speed motor control using a precise Electric Motor Drive System (EMDS) utilizing an 84-pulse Voltage Source Converter (VSC). The development of the VSC signal requires two critical parameters: V module , representing the combined amplitude of the 3-phase signals, and a variable frequency adjusted via a Phase-Locked Loop (PLL) within each sample cycle. To estimate the non-measurable variables ( λ α r , λ β r and T L ), sliding mode, asymptotic, and Luenberger observers are employed and compared among themselves. The control algorithm is based on sliding mode with an equivalent control strategy, ensuring robust performance under various operating conditions. This control algorithm transforms the plant to be controlled to the non-linear block control form by using error-tracking dynamics. This is to obtain the sliding manifold and to apply the equivalent control strategy. The effectiveness of the proposed system is validated through a set of simulations conducted in Matlab/Simulink, demonstrating its capability to achieve high precision in motor drive applications. • Precise Speed Control Using 84-Pulse VSC. • Advanced Observer Techniques for Non-Measurable Variables. • Robust Control Algorithm with Sliding Modes.
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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".