H∞ Performance of Mechanical and Power Networks
Bibliographic record
Abstract
This paper investigates the robustness of two well-known applications of second-order consensus dynamics, namely mechanical and power networks. For uniform subsystem parameters, we derive expressions for the H∞ norms of mechanical and power networks, from external disturbances to body displacements and to generator phase angles, respectively. The closed-form expressions are in terms of the physical parameters (damping coefficients and inertias) of the dynamics and in terms of the spectrum of the grounded Laplacian matrix associated with the network. We then analyze the dependence of the H∞ norm of each network on both the network structure and the physical parameters. For a fixed network topology, we find that each system norm can be minimized by choosing the damping coefficient within a specified range. Theoretical contributions are verified via two illustrative examples for mechanical and power networks, in which we show that the network structure, number of the reference nodes and their location in the network can have considerable effects on the system H∞ norm.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".