A Two-step LMI Scheme for H<sub>2</sub> − H<sub>∞</sub> Control Design
Bibliographic record
Abstract
In this paper, a two-step H2- H∞control design scheme with guaranteed mixed H2and H∞performance is proposed. Different from the traditional H2/H∞control, the proposed method designs an H2controller for a nominal plant and then designs an extra Q operator to recover robustness in H∞sense for the closed-loop system. When the system uncertainty occurs, operator Q is triggered by a residual signal due to the error between the nominal model and the actual plants, and an extra control signal is generated by operator Q to compensate the nominal H2controller. It is noted that the proposed H2- H∞design scheme provides additional design freedom to reduce conservativeness, comparing with the traditional mixed H2/H∞control. The control design in the Linear Matrix Inequality (LMI) approach is applied to synthesize the H2- H∞controller. Simulation results of a numerical example are given to demonstrate that H2- H∞control design is able to compensate the nominal H2control and significantly improve system performance in the presence of system uncertainty. Moreover, two-step H2-H∞control renders better state responses than the traditional mixed H2/H∞control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".