Data-driven Minimum Variance Regulatory Control for Linear Parametrized Controllers

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The paper proposes a linearly parametrized data-driven controller design method based on minimum variance evaluation. The approach updates control parameters that improves disturbance attenuation properties from regulatory control input and output measurements. The proposed method uses linearly parametrized controllers, and obtains sub-optimal control parameters based on variance evaluation. The paper considers a new data-driven cost criterion on the assumption that disturbance model is unknown. Then, the analysis using the approach of “Domain of Attraction (DOA)” is provided to confirm that minimization of the proposed cost criterion reduces the value of the original cost criterion. Finally, the paper shows the effectiveness of the proposed method through a numerical example.