Research on a Control Strategy for Dual Active Bridge Converters Combining Super-Twisting Sliding Mode and Model Predictive Control
Bibliographic record
Abstract
Against the backdrop of the "dual-carbon" strategy and the rapid development of renewable energy, Dual Active Bridge (DAB) converters have attracted wide application in energy storage systems and electric vehicles due to their high-efficiency bidirectional power transfer and galvanic isolation. Traditional PI control shows limited dynamic performance under complex operating conditions. Model Predictive Control (MPC), while forward-looking, depends heavily on model accuracy and is prone to steady-state errors under light load or parameter variations. This paper proposes a hybrid control strategy that integrates Super-Twisting Sliding Mode Control (ST-SMC) with MPC. The output of ST-SMC is employed as an error compensation term for MPC, combining predictive optimization with strong robustness. First, a mathematical model of the DAB converter is established and the control law is derived. Next, system stability is verified using the Lyapunov method. Finally, MATLAB/Simulink simulations compare the performance of traditional PI control, standalone MPC, and the proposed hybrid strategy. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of dynamic response and disturbance rejection, confirming its effectiveness and practical value in renewable energy power systems.
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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 | 0.001 |
| Open science | 0.001 | 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".