Causality-Informed Data-Driven Predictive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control (DDPC) is subject to bias-variance tradeoff and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control (SPC), we gain insight into the reason being the lack of causality as a main cause for their high variance of implicit prediction. In this brief, we derive a new causality-informed formulation of DDPC and its regularized form that balances between control cost minimization and implicit identification of a causal multistep predictor. Since the proposed causality-informed formulations only call for block-triangularization of a submatrix in the generic noncausal DDPC based on LQ factorization, our causality-informed formulation of DDPC enjoys computational efficiency. Its efficacy is investigated through numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it