Stabilization of a class of remote control systems and its robust stability analysis
Bibliographic record
Abstract
In this paper, the stabilization of one class of remote control systems with time delays is first analyzed and then designed by using LMI techniques. Its robust design with respect to system parametric uncertainties, and its robust analysis with respect to nonlinear additive uncertainties and time delay uncertainties are discussed. The system under investigation is a cascade system with two subsystems, controlled by a remote controller with static gains. The motivation of this work is to explore the problem of distributed networked control systems; here we start the discussion of a simple cascade system. Static controller designs based on delay-dependent stability conditions are presented, which is proven to be less conservative than the conventional one. This design is then extended to the case when parametric uncertainties exist. Furthermore, sufficient stability conditions are derived for the system with norm-bounded nonlinear additive uncertainties and time delay variations. Finally, simulation examples are presented to show the effectiveness of the proposed method and to demonstrate the stability condition test for uncertain systems.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".