Optimizing energy conversion efficiency of nonlinear wave energy converters via robust Koopman economic model predictive control
Bibliographic record
Abstract
This paper proposes a robust Koopman economic model predictive control (Koopman-EMPC) method aimed at enhancing the energy conversion efficiency of nonlinear point-absorber wave energy converters (WECs). By applying Koopman operator theory and deep neural network techniques, a deep Koopman network (DKN) is constructed to achieve data-driven identification and modeling of nonlinear systems. An approximation of the infinite-dimensional Koopman linearization model is obtained within a finite-dimensional space, enabling the modeling and global linearization of the nonlinear WEC system. The EMPC method is used for process control of the WEC, while simultaneously optimizing wave energy extraction to maximize the system’s economic performance. Given the inevitable modeling errors in the Koopman model and the presence of external disturbances, a nonlinear offline feedback control strategy is introduced during the online solution of the EMPC problem to enhance system robustness. A rigorous analysis was conducted on the boundedness of the state estimation error between the Koopman model and the real system, as well as on the asymptotic stability and closed-loop robustness of the system under the proposed robust EMPC control algorithm. Multiple simulation results demonstrate the precision of the WEC model established using the proposed DKN, as well as the effectiveness of the proposed algorithm in enhancing the WEC’s energy conversion efficiency.
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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 | 0.001 | 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) | 0.001 | 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".