The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks
成果类型：
Article
署名作者：
Nilsson, Gustav; Coogan, Samuel
署名单位：
Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
刊物名称：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN：
0018-9286
DOI：
10.1109/TAC.2023.3277924
发表日期：
2024
页码：
1179-1185
关键词：
Stability analysis
Vehicle dynamics
transportation
Routing
Lyapunov methods
Queueing analysis
trajectory
Dynamical flow networks
input-to-state stability (ISS)
queuing networks
transportation networks
摘要：
Dynamical flow networks serve as macroscopic models for, e.g., transportation networks, queuing networks, and distribution networks. While the flow dynamics in such networks follow the conservation of mass on the links, the outflow from each link is often nonlinear due to, e.g., flow capacity constraints and simultaneous service rate constraints. Such nonlinear constraints imply a limit on the magnitude of exogenous inflow that is able to be accommodated by the network before it becomes overloaded and its state trajectory diverges. This article shows how the strong integral input-to-state stability (Strong iISS) property allows for quantifying the effects of the exogenous inflow on the flow dynamics. The Strong iISS property enables a unified stability analysis of classes of dynamical flow networks that were only partly analyzable before, such as networks with cycles, multicommodity flow networks, and networks with nonmonotone flow dynamics. We present sufficient conditions on the maximum magnitude of exogenous inflow to guarantee input-to-state stability for a dynamical flow network, and we also present cases when this sufficient condition is necessary. The conditions are exemplified on a few existing dynamical flow network models, specifically, fluid queuing models with time-varying exogenous inflows and multicommodity flow models.