Optimum Design of Fractional Order PIᵅ Speed Controller for Predictive Direct Torque Control of a Sensorless Five-Phase Permanent Magnet Synchronous Machine (PMSM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In both direct torque control (DTC) and predictive direct torque control (PDTC) strategies, just single voltage vector is applied. The question arose, is this applied vector the optimumin terms of minimizing torque and stator flux ripples? In DTC, it may not be the optimum one. However, in case of PDTC, there is a possibility to evaluate the performance of different voltage vectors, where a cost function is proposed to determine the appropriate voltage vector that brings the lowest torque and stator flux ripple within one cycle. On the other hand, PI controller provides a good performance but if the parameters change, the system may lose its performance. With the aim of enhancing the robustness of the PDTC scheme, a fractional order PI controller is proposed that can be considered as a generalization of the classical PI controller, and to set its parameters, a Grey Wolf Optimization algorithm is employed. Furthermore, omitting the sensor increases reliability and decrease the size and cost of the drive system. For these reasons, an extended Kalman filter observer is adopted, where the rotor speed and rotor position as well as the load torque are estimated. In this work, a fractional order PI controller tuned by GWO for PDTC of a five-phase permanent magnet synchronous machine (PMSM) based on EKF observer is presented. Analysis of simulation results exhibit clearly the efficiency and robustness of the suggested control compared to conventional DTC based on classical PI controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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