Noise-Free Sensorless Control of Robotic PMSMs Based on Variable Structure Speed Observer with Embedded Single-Waveform Injection Over Full-Speed Range
Bibliographic record
Abstract
For the full-speed sensorless control of permanent magnet synchronous motors (PMSM) used in robotic joints, conventional methods adopt a combined strategy of zero-low-speed and medium-high-speed control methods. This requires switching between methods during motor operation, which reduces the reliability and stability, making it unable to be used in robotic joint motors. In this paper, a novel full-speed range sensorless control method is proposed. The method consists of two ranges: the zero-speed and the operation range. In the zero-speed range, a high-frequency square wave injection (HFSI) method with only 20 square-wave pulses (SWPs) is employed, resulting in a short injection duration, which ensures low noise. Leveraging the bidirectional convergence property of the linear time-invariant enhanced phase-locked loop (LTI-EPLL), the initial position and NS polarity can be determined without additional signal injection, ensuring smooth startup and rotor standstill. In the operation range, based on the eletrical properties of the PMSM, a sliding-mode speed observer (SMSO) is constructed to directly estimate the speed, and the position is obtained via integration. Thus, full-speed range operation is achieved without the need to switch between methods during operation range. Finally, the feasibility of this method is validated in MATLAB/Simulink.
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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) | 0.000 | 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".