A Fast-Digital Current Regulator Based on Matched Pole–Zero Discretization for PMSM
Bibliographic record
Abstract
Continuous-time methods design the synchronous-frame current regulator in the S-domain and then discretize it into the Z-domain using the Tustin or Euler method for digital implementation. This process causes discretization errors that significantly impair the performance of permanent magnet synchronous motor (PMSM) drives, especially at a low sampling-to-fundamental frequency ratio (SFR). A direct discrete-time method, design the current regulator directly in the Z-domain with improved dynamic performances. However, it results in online calculations of exponential terms that increase the execution time of the control loop, particularly for Interior PMSM (IPMSM). Therefore, this paper proposes a novel design approach of a digital current regulator with a high discretization accuracy and fast execution time. The proposed method is a combination of the multiple-input-multiple-output (MIMO) decoupling and matching pole-zero (MPZ) discretization concepts. MIMO decoupling is used to cancel accurately the poles of the plant that migrate with the motor’s speed/frequency change in the S-domain. The MPZ method is used to discretize accurately the current regulator from S-domain to Z-domain. A new approximation concept for the calculation of the exponential matrix is adopted to reduce the complexity of the proposed approach. Thus, with the synthesized current regulator, the execution time is reduced significantly compared to the direct discrete-time design methods while keeping the same decoupling and dynamic performances even under low SFRs. Simulations and experiments performed on IPMSM drives have successfully verified the proposed design method, and results show improved decoupling performance and robustness against the variation of parameters with better current harmonic distortion.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".