Data‐driven iterative learning control using a uniform quantizer with an encoding–decoding mechanism
Bibliographic record
Abstract
Abstract This work explores the problem of uniform quantization of iterative learning control (ILC) for nonlinear nonaffine systems under a data‐driven design and analysis framework. First, to deal with the strong nonlinearity and nonaffine structure of the systems, an iterative linear data model (iLDM) utilizing more additional parameter information is developed consequently bypassing modeling process. The iLDM only serves for the controller design and analysis without any mechanistic interpretation. Then, an encoding–decoding mechanism (E‐DM) is employed to deal with the bounded tracking performance caused by the uniform quantizer. Using the iLDM, an E‐DM based quantized data‐driven ILC (E‐D QDDILC) method is developed with a quantized learning control law and a quantized parameter estimation law, both of which only utilize the quantized output estimations obtained from the E‐DM. The quantized parameter estimation law enhances the robustness of the proposed E‐D QDDILC as an adaptive mechanism to tune the learning gain in real‐time. A mathematical induction approach and the contraction mapping principle are introduced for the convergence analysis as the basic tools. When the scaling function is bounded, one shows the tracking error is bounded convergent. When the scaling function approaches zero iteratively, a zero convergence can be guaranteed in the iteration domain. The main results are verified through simulation examples.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".