A Dual-Loop Robust Control Scheme With Performance Separation: Theory and Experimental Validation
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Bibliographic record
Abstract
A dual-loop robust control scheme and its property of performance separation are presented in this article. The dual-loop control scheme consists of two degrees of freedom for nominal and robust performances, with the nominal controller being any stabilizing controller in the observer-based state-feedback form and robust controller being a standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> controller. When there is model error and/or disturbance, the robust controller is activated to compensate the nominal controller; otherwise, the dual-loop control returns to a single-loop nominal controller. We also show that the nominal and robust performances of the dual-loop control are independent of one another. As a result, the nominal and robust controllers can be designed separately offline, and then, online coordinated in the dual-loop control. Furthermore, the state-space realization and controller implementation are also provided. Finally, a two-wheeled robot with varying slip effect is considered as an illustrative example. Both simulation and experimental results show that the dual-loop control outperforms the classical robust control methods.
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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 | 0.001 |
| 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