FPGA sensorless PMSM drive with adaptive fading extended Kalman filtering
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it