Implementation of Deep Reinforcement Learning for Model-free Switching And Control of a 23-level Single DC Source Hybrid Packed U-Cell (HPUC)
Bibliographic record
Abstract
This paper proposes a novel Deep Reinforcement Learning (DRL) method for controlling a 23-level Single DC Source Hybrid Packed U-Cell (HPUC) converter. The HPUC topology generates a high number of voltage levels with minimal components but presents control challenges due to its numerous switching states and dynamic charging behavior. Unlike conventional control methods, which require accurate models and are sensitive to noise and parameter mismatches, DRL offers a model-free and resilient approach to the non-linear control of such complex systems. A Deep Q-Network (DQN) agent which is inherently model-free and suited for high-dimensional state spaces and discrete action spaces, is employed to address these issues. To validate the proposed method, simulations were conducted in the MATLAB/Simulink environment. The obtained results demonstrated the satisfactory performance of the proposed DRL method, achieving a Total Harmonic Distortion (THD) of 2.71% in the output current under steady-state, maintaining stable capacitor voltage balancing, and exhibiting rapid dynamic response (e.g., settling within approximately 40 ms for current step changes). Furthermore, its resilience was highlighted by its ability to maintain control despite a 25dB SNR noise condition and up to 15% variations in capacitor values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".