Quantum space vector pulse width modulation for speed control of permanent magnet synchronous machines
Bibliographic record
Abstract
The need for effective control strategies in electrical motor drives has resulted in significant advances in inverter modulation approaches, particularly for permanent magnet synchronous machines (PMSMs). This study proposes a quantum-based strategy to improve energy efficiency, control precision, and system stability by developing quantum space vector pulse width modulation (QSVPWM) as an alternative technique to classical SVPWM for PMSM control. The proposed QSVPWM employs a quantum comparator implemented via a quantum subtractor for real numbers ranging from −100 % to +100 %. Trigonometric properties and the tensor product are combined to create a quantum sign function. The QSVPWM controller also incorporates quantum versions of classical logical gates such as and OR. The effectiveness of QSVPWM was assessed using MATLAB Simulink simulations, and its performance was compared with that of SVPWM under the same conditions. QSVPWM outperforms SVPWM in terms of control precision, oscillation reduction, and energy efficiency, reducing the root mean square speed error by 0.47 %, the d -axis current by 0.11 %, and the q-axis current by 0.59 %. Furthermore, a total harmonic distortion study revealed that QSVPWM reduces higher-order harmonics, thereby improving power quality and lowering energy losses. These enhancements help smooth control dynamics, minimize mechanical stress on components, and improve energy efficiency. In summary, QSVPWM outperforms traditional SVPWM, particularly for applications requiring precise control and greater energy savings in motor control systems.
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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 | 0.000 |
| 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) | 0.002 | 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".