A DATA-DRIVEN APPROACH TO CASCADED INTERNAL CONTROLLERS: SIMULTANEOUS ATTAINMENT OF CONTROLLERS AND MODELS

Abstract

This paper proposes a data-driven parameter tuning of the internal model controllers (IMC) in cascade architecture with minimum phase processes. In order to perform the parameter tuning of the IMC, we utilize the fictitious reference iterative tuning (FRIT), which enables us to obtain the desired parameter of the controllers with only one-shot experiment data. The algorithm does not require mathematical process models but only a single set data collected from the closed loop system. Moreover, the proposed approach enables us to obtain both the optimal parameters of two controllers for the desired tracking property and mathematical models of the controlled process simultaneously. To show the validity of the proposal, we give illustrative examples.