抄録
In this paper, we propose a data-driven method for simultaneously updating a process model and controller for disturbance attenuation. Since the proposed method allows to both identify a process model and design the corresponding minimum variance controller, the identified model can be used for assessment or analysis of the control system before actual implemntation. In order to achieve system identification and minimum variance (MV) control, we introduce a data-driven criterion that consists of the input and output signals of the system which are driven by white noise. We also show that the parameters which theoretically optimize the poposed criterion are equivalent to the true process parameters and the MV controller parameters. The validity of the proposed method is illustrated in a numerical example.
