Iterative Learning Control with application to hydraulic actuators
Bibliographic record
Abstract
The concept of Iterative Leaming Control (ILC) was originated from the efforts to control systems that are required to perform repetitive tasks in the industrial field.The basic idea of ILC is that the information obtained from a previous trial is used to improve the control signal for the next trial until the desired performance level is reached.The iterative learning control has been developed and applied to many different fields, especially the robotic field.However, its application toward the hydraulic systems is rather sparse and is limited to a few articles.This thesis investigates the robust ILC of an -electrohydraulic positioning system with a faulty actuator piston seal.The goal is to develop an ILC scheme that is tolerant to a faulty condition such as intemal leakage.Toward this goal, three different aspects of iterative learning control are presented and compared, including the basic ILC, the ILC with proportional error feedback, and the ILC with current cycle feedback.The results prove that all ILC algorithms are tolerant to the intemal leakage given same initial conditions at each trial.It is also shown that both the ILC w'ith proportional enor feedback and the ILC with current cycle feedback are tolerant to the intemal leakage without the need of resetting the initial conditions.This study provided a groundwork for using an ILC-base, fault tolerant, control scheme for hydraulic actuators.Many operations that are repetitive in nature, such as injection molding or metal forming, will benefit from this approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".