Iteration Tuning of Disturbance Observer-Based Control System Satisfying Robustness Index for FOPTD Processes
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Bibliographic record
Abstract
A conventional disturbance observer (DOB)-based control system is a model-based control system. In this paper, a data-driven design method is proposed for the DOB-based control system using iteration feedback tuning (IFT) based on the first-order-plus-time-delay models. It is well known that the conventional data-based design methods cannot provide an explicit tradeoff between robustness and performance. Here, our goal is to develop a method to design the data-driven DOB-based control system satisfying a given robustness index. To this end, first, the tuning rules of the controller and the Q-filter in terms of the nominal process model are analytically determined for a given robustness index. Second, an optimization problem, solved by IFT algorithm, is established to find the optimal parameters of the nominal model. The merits of the proposed method are that: 1) the number of parameters needing to be tuned is reduced, since only the parameters of the nominal model are optimized and 2) the system satisfies the explicit robustness index if the parameters are optimal. Moreover, the selection of the robustness index, the output performance of the system, and the performance of the iteration algorithm are addressed. Two simulation examples and an experiment are presented to demonstrate the effectiveness and merits of the proposed method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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