Data-driven linearly parametrized controller design based on minimum variance evaluation

Abstract

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. Most of data-driven controller design methods address model matching problem for improving tracking properties, whereas our previous works proposed data-driven minimum variance controller design that achieves simultaneous update of both a controller and a process model. However, they used the same controller structure as the controller of the minimum variance control, which requires correct order informations on the controlled process model. In contrast, the proposed method uses linearly parametrized controllers, and obtains sub-optimal control parameters based on variance evaluation. The paper shows data-driven cost function and its validity. Comparing with the original cost criterion that represents minimum variance evaluation subject to linearly parametrized controller, the validity is firstly shown by the analytical expansion on the assumption that disturbance model is known. Then, the paper extends the analysis to the unknown disturbance model case by simultaneously estimating disturbance model. Finally, the paper shows the effectiveness of the proposed method through a numerical example.