Robust control of discrete minimum and non‐minimum phase systems via data‐driven virtual reference feedback tuning and IMC
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Bibliographic record
Abstract
Abstract In this paper, we develop a novel robust control approach for discrete minimum and non‐minimum phase systems via a combined data‐driven virtual reference feedback tuning () and internal model control (IMC) scheme. The first step in the conventional method controller design is the selection of the closed‐loop reference model (), and selection is still an open problem. The integration of the scheme and the VRFT method provides the advantage of flexibility in controller design due to the incorporation of the filter. As a result, the proposed design method begins with the selection of and filter. Unlike the standard method, the proposed combined and design approach has the unique feature of taking into account a robustness property of dynamics, namely, maximum sensitivity () as the design specification for the and IMC filter selection. Moreover, the proposed approach includes a robustness specification that resolves the trade‐off between performance and robustness in real‐time controller design. Furthermore, the robustness guarantee with plant uncertainties and controller fragility is elucidated. The proposed approach is validated using numerical simulations and experimental validation through the temperature control process. Compared to conventional controllers, experimental and simulation results show that the proposed controllers have less tracking error, minimize control effort, and improve robustness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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