Real-time DSP-based computation-efficient speed-sensorless drive of induction motors
Bibliographic record
Abstract
Induction motors are the workhorse of industries, continuously used in existing and new applications with improved performance utilizing modern power electronics and digital controls. This paper proposes a new computationally efficient real-time control for induction motor drives using state-of-the-art digital signal processing (DSP) technology, but without using a speed sensor. This control is termed the advanced speed-sensorless induction-motor drive (ASID). Its advantages are particularly evident in hostile application environments where maintenance of speed sensors requires costly downtime or installation of sensors is physically difficult or expensive for retrofitting existing electromechanical systems. This paper details the ASID control algorithms, formulations, and implementations utilizing high-speed DSP technology. The features of the ASID are demonstrated and compared with the manufacturer recommended speed-sensorless drive controls. Key comparisons provided in the paper include efficiency of computations, easy of real-time implementations, simplicity of control algorithms, accuracy of speed estimations, convergence and stability of feedback controls, comprehension of control methodology, etc.
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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 | 0.001 | 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".