Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
Dissipativity-based data-driven decentralized control of interconnected systems,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Neural networks learn dissipativity matrices from data to create a model-free controller that improves transient stability in all-VSG power systems.
citing papers explorer
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Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Develops a dissipativity and contraction theory framework for convergence analysis of distributed optimization algorithms, producing LMI conditions for arbitrary network structures.
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Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control
Neural networks learn dissipativity matrices from data to create a model-free controller that improves transient stability in all-VSG power systems.