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Abstract
Today's increasing availability of data is having a remarkable impact on control design. However, for data-driven control approaches to become widespread in practical applications, it is necessary to devise strategies that can effectively handle the presence of noise in the data used to design the controller. In this work, we analyse the existing approaches to deal with noisy measurements in data-driven predictive control (DDPC) and we highlight the advantages and downsides of each technique from a practitioner's perspective. Our qualitative conclusions are supported by the results obtained from two benchmark examples.
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