Data-Driven Finite-Iteration Learning 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
This article develops a novel data-driven finite-iteration learning control (DDFILC) for the nonlinear repetitive systems that are stable for the finite operation length. Both the error range and the finite-iteration number can be designated beforehand by considering the efficiency and economy of the industrial processes. As a result, not only can the proposed DDFILC guarantee the desired product quality but also can reduce the operation cost. First, a linear data model (LDM) is constructed to reformulate the system dynamics that satisfies the Lipschitz continuity condition. Then, an iterative updating law of the DDFILC is developed for estimating the unknown parameter of the LDM. The proportional-differential type learning law used in the DDFILC has two iteration-time-varying learning gains, both of which are updated according to the linear matrix inequality conditions. Not only the finite-iteration convergence but also the iteratively asymptotic convergence can be shown mathematically by using the two-dimensional (2-D) system theory. The proposed DDFILC approach does not require an exact model and is robust to uncertainties. The simulation study verifies the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 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