Integral Reinforcement Learning Control for a Class of Unknown Nonlinear Systems with an Application to a Microgrid System
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Bibliographic record
Abstract
In this paper, a brand-new technique based on integral reinforcement learning (IRL) combined with the event-triggered control (ETC) for multiplayer non-zero-sum (NZS) game is proposed, taking into account nonlinear systems with uncertain system drift dynamics. System drift dynamics are no longer necessary for controller design with the IRL method. Furthermore, this method is implemented online, in contrast to other iterative calculating techniques. In this instance, the NZS game problems can be resolved by combining the IRL algorithm and the event-triggered control architecture. It offers a new triggering condition and lessens the computational and communication overhead of the entire control process. The system’s stability is ensured at the same time. An example is then given to show how well our method works.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it