Simultaneous update of model and controller using fictitious reference iterative tuning for disturbance attenuation based on variance evaluation

This paper presents a data-driven method for simultaneously updating a process model and a controller for disturbance attenuation. Since the proposed method both designs a minimum variance controller and identifies the process model, the control performance can be assessed and the control system analyzed before actual implementation. For the purpose of simultaneous updating, we introduce a new variance criterion derived from a set of input and output data from the closed-loop system driven by colored noise signals. The paper also shows that the optimal parameters that theoretically optimize the proposed variance criterion are equivalent to the true process parameters and minimum variance control parameters. The validity of the proposed method is illustrated in a numerical example.