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This paper presents a robust data-driven controller design based on the noisyinput-output data without assumptions on the statistical properties of thenoises. We start with the direct data-representation of system models that takeelements from behavioral system theory, followed by analyses of the upper boundof the "modeling" error with the data representation with presence of noises.Some pre-conditioning methods are put into the context based on how the derivedbound is structured. We lastly leverage the upper bound to develop robustcontrollers that ride through the data noises.

Robust Data-Driven Control for Systems with Noisy Data