Robust Suboptimal Output Synchronization of Nonlinear Heterogeneous Agents
Bibliographic record
Abstract
In this paper, the problem of output synchronization of a set of nonlinear heterogeneous systems is investigated. The steering scenario is such that, subject to bounded uncertainties, the output of each of the agents will track the output of a generated reference command asymptotically. The structure of the controller is designed in such a way to tackle the problem in two steps. In the first step, local exosystems associated to each of the agents are designed. Consensus achievement among these exosystems is guaranteed provided that the communication graph is connected. In the second step, based on a combination of a state-dependent Riccati equation (SDRE) and integral sliding mode control (ISMC), distributed regulators are designed to track the generated reference command. The proposed controller scheme is proven to be suboptimal in the sense of asymptotically minimizing a quadratic cost functional while maintaining robustness against perturbations. Simulation results are presented to illustrate the efficacy of the proposed method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".