Continuous-Control-Set Model Predictive Control-Based Energy Routing With Over/Under Voltage Protection
Bibliographic record
Abstract
Multi-active-bridge (MAB) converters are highly promising as energy routers in renewable energy applications. Their multiple-input multiple-output (MIMO) control challenges can be effectively addressed by a model predictive control (MPC) approach, which minimizes the interference between different ports and simplifies the controller design. However, during the optimization process, the input and output constraints are usually ignored to simplify the implementation, which may degrade the control performance and lead to large voltage overshoot/undershoot when the constraints are violated. Meanwhile, solving the constrained MPC problem within a short sampling time poses a huge computational challenge. To address these issues, in this article a continuous-control-set model predictive control (CCS-MPC) scheme is developed for the MAB converters, which incorporates input and output constraints to achieve over/under voltage protection. A MPC problem with multiple constraints is formulated and converted into a quadratic programming (QP) problem. Its real-time implementation on a low-cost microcontroller unit (MCU) is further discussed including QP solver deployment, real-time certification, and delay compensation. Finally, the developed CCS-MPC scheme is experimentally verified by a four-port 500 W energy router prototype and well demonstrates the over/under voltage protection capability.
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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) | 0.001 | 0.000 |
| 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) | 0.002 | 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".