MTPA- and FW-Based Robust Nonlinear Speed Control of IPMSM Drive Using Lyapunov Stability Criterion
Bibliographic record
Abstract
This paper presents a robust nonlinear control technique for a wide-speed-range operation of interior permanent magnet synchronous motor (IPMSM) based on the use of maximum torque per ampere (MTPA) and flux-weakening (FW) controls. The global asymptotic stability of the drive is demonstrated by Lyapunov stability criterion in conjuncture with Barbalat's lemma. For the proposed nonlinear controller, the MTPA and FW schemes are used to control the d-axis stator current below and above the rated speed, respectively. Control laws are developed based on an adaptive backstepping technique to ensure robust system stability. The system nonlinearities are also accommodated through the online estimation of critical parameters. The control and adaptive backstepping laws have been successfully implemented in a MATLAB/Simulink simulation environment. Simulation results indicate excellent speed response and parametric variation insensitivity. The complete drive system is implemented using the peripheral component interconnect (PCI)-based DS1104 digital signal processor (DSP) board for a 3.7-kW laboratory IPMSM. Both experimental and simulation results have demonstrated excellent drive performance, with an extended-speed-range operation and rejection of load disturbance.
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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) | 0.001 | 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) | 0.001 | 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".