Data-driven minimum variance control using regulatory closed-loop data based on the FRIT method
The paper proposes data-driven minimum variance regulatory control using a Fictitious Reference Iterative Tuning (FRIT) method. The present work uses the inverse of a linearly parametrized controller. Thus, a general parametrization is realized, and the integrator is straightforwardly introduced. A numerical example shows an effectiveness of the proposed method.