Suboptimal digital LQ output feedback control design <i>via</i> LMI relaxations
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Bibliographic record
Abstract
Abstract This paper deals with suboptimal linear quadratic (LQ) output feedback control of linear discrete systems. It is shown that degree of freedoms by instrumental variables employed in this paper lead to much flexibility in obtaining a suboptimal LQ controller. An improved convex optimization method involving linear matrix inequalities (LMIs) is suggested to solve the matrix inequalities characterizing a solution of the suboptimal LQ problem. Of the major interest of this paper is an extension to a class of nonconvex LQ problems of large size arising in decentralized feedback, simultaneous control, periodic feedback control, etc. Illustrative examples demonstrate the validity of the proposed convex approximate approach to optimal LQ output feedback control. Also, it is shown that suboptimal LQ solutions obtained by the proposed method can be used as an initial feasible point of existing iterative LMI algorithms to improve the feasibility of the iterative methods. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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