A DSP-Based Implementation of a Nonlinear Model Reference Adaptive Control for a 1.5 kW Three-Phase Three-Level Boost-Type Vienna Rectifier
Bibliographic record
Abstract
In this paper, the design and implementation of a model reference adaptive control (MRAC) applied to a three-phase three-level boost-type Vienna rectifier are presented. The proposed adaptive controllers are designed for inner loops, targeting to balance partial output DC bus voltages, while maintaining unity power factor and minimum AC line currents harmonics. The dqo nonlinear multiple-input multiple-output (MIMO) state space model of the rectifier is first over- parameterized. Then, the controllers are designed based on adequate input-to-output linearization and Lyapunov-based parameters adaptation scheme, with a view to track the reference model and compensate the system parametric variations. The outer voltage loop is set consequently, assuming fast inner loops dynamics. The proposed control law is designed in Simulink/Matlab and executed in real- time on a 1.5 kW laboratory prototype using the DS1104 controller board of dSPACE. The experimental results are given under various operating conditions, including steady state operation at different power levels, unbalanced DC load steps, temporary phase loss and plusmn30% AC supply voltage dip/swell. The proposed control law ensures low output voltage ripple, minimum AC line-current THD, small overshoots and fast settling times, face to a wide clan of disturbances.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".