FPGA sensorless PMSM drive with adaptive fading extended Kalman filtering
Bibliographic record
Abstract
This paper addresses the design and implementation of an Adaptive Fading Extended Kaiman Filter (AF-EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the implemented AF-EKF and their estimates are then used in vector control of the PMSM. In conventional Kaiman filtering, abrupt state changes may not be tracked adequately since sudden variations may seriously affect the auto-correlation Gaussian property of white noise in the filter residuals. For this, the AF-EKF has been developed to recover the estimation results in events of frequent and sharp state jumps. The AF-EKF is, therefore, a promising estimator for PMSM drives that are subject to frequently-varying loads speed commands. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high-speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows effectiveness of the proposed chip-based AF-EKF PMSM speed estimation.
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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 | 0.001 | 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".