Constrained Data-Driven Model-Free ILC-based Reference Input Tuning Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a data-driven Iterative Reference Input Tuning (IRIT) algorithm that solves a reference trajectory tracking problem viewed as an optimization problem subjected to control signal saturation constraints and to control signal rate constraints. The IRIT algorithm incorporates an experiment-based stochastic search algorithm formulated in an Iterative Learning Control (ILC) framework in order to combine the advantages of model-free data-driven control and of ILC. The reference input vector's dimensionality is reduced by a linear parameterization. Two neural networks (NNs) trained in an ILC framework are employed to ensure a small number of experiments in the gradient estimation. The IRIT algorithm is validated by two case studies concerning the position control of a nonlinear aerodynamic system. The results prove that the IRIT algorithm offers the significant control system performance improvement by few iterations and experiments conducted on the real-world process. The paper successfully merges the use of ILC in both model-free reference input tuning and NN training.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it