Abstract
Abstract Data-driven model reference control allows for the design of a controller from input and output data when a parametric model of the system is not available. In this work we propose to use advanced nonparametric frequency response function estimation methods to aid in the model reference control task. This allows for a convenient way to extend model reference control to continuous-time systems. Moreover, we also outline a procedure that implements frequency weighing to achieve the Cramer-Rao lower bound in the case that the ideal controller is realizable and in the case that the input is also perturbed by noise. The proposed methods are used to design an analog controller for a continuous-time system.
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