Extended Kalman Filter Application in Permanent Magnet Synchronous Motor Sensorless Control
Bibliographic record
Abstract
This thesis presents the modeling, analysis, design and experimental validation of a robust sensorless control method for permanent magnet synchronous motor (PMSM) based on Extended Kalman Filter (EKF) to accurately estimate speed and rotor position. Currently, there is no robust position/speed sensorless control method available for the permanent magnet synchronous motor (PMSM) in the published literature. Traditionally, commercial off-the-shelf simulation models for PMSM do not incorporate initial rotor position and simplified steady-state based modeling of the associated power electronic circuitry and controls are used. These limitations have prevented the development and application of a robust real-time sensorless control method with good dynamic performance over the full speed range for the PMSM. The main focus of this thesis is to overcome these limitations. In particular, a detailed real-time PMSM model in MATLAB/Simulink simulation environment is developed which is used to validate the EKF sensorless control method by varying the initial position of the rotor. The proposed position/speed sensorless control based on EKF method along with all the power electronic circuitry are modeled in this simulation environment. This user-friendly simulation and rapid-prototyping platform is then effectively used to predict, analyse, fine-tune and validate proper operation of the proposed EKF sensorless control method for all operating conditions. In particular, different control strategies are reviewed and the performance of the proposed EKF sensorless control method is critically assessed and validated for different types of dynamic and static torque loads. The robustness of the proposed EKF sensorless method is demonstrated by validating proper operation of the closed-loop motor control system for different rotor initial positions and insensitivity of the EKF speed/position estimation method to the PMSM parameter variations. Proper operation of the proposed EKF based sensorless control method for a high speed permanent magnet synchronous machine is verified experimentally in the lab at Honeywell.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".