H ∞ performance analysis and distributed controller synthesis for interconnected linear systems from noisy input-state data

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design. In this paper, we extend a recent data-based approach for guaranteed performance analysis to distributed analysis of interconnected linear systems. We present a new set of sufficient LMI conditions based on noisy input-state data that guarantees ${\mathcal{H}_\infty }$ performance and has a structure that is applicable to distributed controller synthesis from data. Sufficient LMI conditions based on noisy data are provided for the existence of a dynamic distributed controller that achieves ${\mathcal{H}_\infty }$ performance. The presented approach enables scalable analysis and control of large-scale interconnected systems from noisy input-state data.