Robust High Bandwidth Discrete-Time Predictive Current Control with Predictive Internal Model—A Unified Approach for Voltage-Source PWM Converters
Bibliographic record
Abstract
This paper presents a robust high bandwidth discrete-time predictive current control scheme for voltage-source pulsewidth-modulated (VS-PWM) converters. First, to achieve high bandwidth current control characteristics, a digital predictive current controller with delay compensation is adopted. The compensation method utilizes a current observer with an adaptive internal model for system uncertainties. The predictive nature of both the current observer and the internal model forces the delays elements to be equivalently placed outside the closed loop system. Second, to ensure perfect tracking of the output current in the presence of uncertainties and providing means for attenuating low- and high- frequency system disturbances, the frequency modes of the disturbances to be eliminated should be included in the stable closed loop system. Toward this, an adaptive internal model for the estimated uncertainty dynamics is proposed. To cope with the high bandwidth property of the lump of uncertainties in VS-PWM converter applications, the disturbance slowly varying assumption is relaxed in the proposed controller. The relaxation is achieved by adopting a curbing sliding-mode-based feedback gain vector within the internal model observation system. Comparative evaluation tests were carried out on a grid-connected VS-PWM converter and a direct drive permanent magnet synchronous motor (DD-PMSM) drive system to demonstrate the validity and effectiveness of the proposed control scheme at different operating conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".