Double-Layered Iterative Learning Control for Nonlinear Systems
Bibliographic record
Abstract
This work aims at improving the control performance of the iterative learning control through set-point learning along iteration direction. A double-layered learning control mechanism is designed for both the control input and the set-point, respectively. The learning control of the input is regarded as a local controller in the inner layer, and the learning control of the set-point is designed as an auxiliary controller in the outer layer whose design is a main challenge since no any priori knowledge is available to describe the relationship between the set-point and the control performance. To solve this issue, an ideal nonlinear nonaffine set-point learning optimization (SPLO) algorithm is designed by taking the set-point and the tracking error as the arguments. Then, an iterative dynamic linearization (iDL) is introduced to formulate the ideal SPLO algorithm as a linear parametric one whose unknown parameter is estimated by designing a parameter updating algorithm. Further, since a strongly nonlinear and nonaffine system is considered without any model information available, the iDL is also used to derive its equivalent linear data model which is then updated by the input and output data to make the linear parametric SPLO realizable. Finally, a double-layered iterative learning control (DLILC) is proposed under the data-driven framework for tracking an iteration-varying trajectory. Convergence analysis and extensive simulations are included to demonstrate the effectiveness of the presented DLILC.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".