Real-Time Implementation of Input-State Linearization and Model Predictive Control for Robust Voltage Regulation of a DC-DC Boost Converter
Bibliographic record
Abstract
The use of DC-DC step-up converters has significantly increased due to their implementation as power interfaces in microgrids (MGs), smart grids (SGs) and electrical vehicles. Step-up converters adapt the source voltage or current to the load specifications through an appropriate control algorithm, which is linear in most cases. However, linear algorithms mostly guarantee the system's stability and desired performances only around a relatively small neighborhood of the equilibrium point. Model predictive controllers (MPCs) have been proposed to improve the performance of the converter and broaden its operating region. However, MPCs have mostly been based on an approximated linear model of the converter, which contributes to a relatively narrow operating region. This work proposes an MPC algorithm based on an exactly linearized converter model. The converter model is linearized according to an exact input-state linearization control (ILC). To the best of our knowledge, this is the first work to present a real-time implementation of the ILC in the context of nonlinear DC-DC boost converter control. The objective of exact linearization is to continue using the same reduced-complexity linear MPC while extending the operation area of the system compared to classic linear control. Simulations and experimental results show that the static and dynamic performances of the proposed control are significantly better than those of the standard linear control.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".