Neural network-based LED lighting control with modeling uncertainty and daylight disturbance
Bibliographic record
Abstract
This paper presents a neural network-based control method for achieving desired lighting levels in an LED-based lighting system with unknown or uncertain system model parameters in the presence of daylight disturbances. Assuming an unknown system model matrix, the control strategy utilizes an online neural network method to synthesize a learning controller. The control commands are dimming levels, which represent the percentage of LED's full power level and the outputs are illuminance levels at target points. The neural controller is designed using a Lyapunov-based analysis to achieve boundedness of the output error to an arbitrarily small ultimate bound. By considering the daylight as a disturbance, it serves as a bias for the desired control system set-point resulting in lower dimming command inputs and energy savings. The controller design only requires the tracking of error signal as input which eliminates the need for any prior knowledge of the daylight disturbance and room model.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".