Constrained Data-Driven Model-Free ILC-based Reference Input Tuning Algorithm
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".