Derives necessary and sufficient feasibility conditions for target density in leader-follower systems with follower interactions, plus a locally stabilizing feedback law with explicit basin of attraction.
Banach con- trol barrier functions for large-scale swarm control
4 Pith papers cite this work. Polarity classification is still indexing.
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years
2026 4verdicts
UNVERDICTED 4representative citing papers
The paper introduces distributional ISS via Wasserstein distance and proves stability for l-smooth lambda-convex Wasserstein gradient flows under bounded perturbations, plus error bounds for kernel and particle approximations.
A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.
citing papers explorer
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Leader-Follower Density Control of Multi-Agent Systems with Interacting Followers: Feasibility and Convergence Analysis
Derives necessary and sufficient feasibility conditions for target density in leader-follower systems with follower interactions, plus a locally stabilizing feedback law with explicit basin of attraction.
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Input-to-State Stability of Gradient Flows in Distributional Space
The paper introduces distributional ISS via Wasserstein distance and proves stability for l-smooth lambda-convex Wasserstein gradient flows under bounded perturbations, plus error bounds for kernel and particle approximations.
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Local Safety Filters for Networked Systems via Two-Time-Scale Design
A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
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A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
A distributed bilevel algorithm optimizes emergent macroscopic behavior in multi-agent systems by combining local exponential-family state estimation with hypergradient microscopic updates and proves convergence via timescale separation.