Resilient Control of Dynamic Flow Networks Subject to Stochastic Cyber-Physical Disruptions

Modern network systems, such as transportation and communication systems, are prone to cyber-physical disruptions and thus suffer efficiency loss. This article studies network resiliency, in terms of throughput, and develops resilient control to improve throughput. We consider single-commodity networks that admit congestion propagation. We also apply a Markov process to model disruption switches. For throughput analysis, we first use insights into congestion spillback to propose novel Lyapunov functions and then exploit monotone network dynamics to reduce computational costs of verifying stability conditions. For control design, we show that 1) for a network with infinite link storage space, there exists an open-loop control that attains the min-expected-cut capacity; 2) for a network with observable disruptions that restrict maximum sending and/or receiving flows, there exists a mode-dependent control that attains the expected-min-cut capacity; 3) for general networks, there exists a closed-loop control with throughput guarantees. We also derive lower bounds of resiliency scores for a set of numerical examples and verify resiliency improvement with our method.