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Performance limitations of large-scale networks with distributed dynamic feedback
Networked control systems arise in a wide range of applications. These systems typically have a global control objective, while the control is distributed and relies only on local feedback from a neighborhood around each site. In this talk, I will address the question of what this implies in terms of limitations to the overall performance of such systems, in particular as the networks grow large.
We consider networked dynamical systems with double integrator dynamics, controlled with linear consensus-like algorithms. Such systems can be used to model, for example, vehicular formation dynamics and synchronization in electric power networks. We assume that the systems are subject to distributed disturbances and study performance in terms of H2 norm metrics that capture the notion of network coherence. In the context of power networks, we also show how such metrics can be used to quantify losses due to non-equilibrium power flows. With local, static feedback control, there are known performance limitations that cause these metrics to scale unfavorably with the network size. We discuss the underlying reasons for these unfavorable scalings and propose distributed dynamic feedback controllers, which, under certain conditions, alleviate the limitations of static feedback.