Dynamic mode decomposition (DMD) is a data-driven technique used for capturing the dynamics of complex systems. DMD has been connected to spectral analysis of the Koopman operator, and essentially extracts spatial-temporal modes of the dynamics from an estimate of the Koopman operator obtained from data. Recent work of Proctor, Brunton, and Kutz has extended DMD and Koopman theory to accommodate systems with control inputs: dynamic mode decomposition with control (DMDc) and Koopman with inputs and control (KIC). In this paper, we introduce a technique, called Network dynamic mode decomposition with control, or Network DMDc, which extends the DMDc to interconnected, or networked, control systems. Additionally, we provide an adaptation of Koopman theory for networks as a context in which to perform this algorithm. The Network DMDc method carefully analyzes the dynamical relationships only between components in systems which are connected in the network structure. By focusing on these direct dynamical connections and cutting out computation for relationships between unconnected components, this process allows for improvements in computational intensity and accuracy.
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