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Data-driven controls using Gaussian process regression have recently gainedmuch attention. In such approaches, system identification by Gaussian processregression is mostly followed by model-based controller designs. However, theoutcomes of Gaussian process regression are often too complicated to applyconventional control designs, which makes the numerical design such as modelpredictive control employed in many cases. To overcome the restriction, ouridea is to perform Gaussian process regression to the inverse of the plant withthe same input/output data for the conventional regression. With the inverse,one can design a model reference controller without resorting to numericalcontrol methods. This paper considers single-input single-output (SISO)discrete-time nonlinear systems of minimum phase with relative degree one. Itis highlighted that the model reference Gaussian process regression controlleris designed directly from pre-collected input/output data without systemidentification.

Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller