FRIT of Internal Model Controllers for Poorly Damped Linear Time Invariant Systems: Kautz Expansion Approach

Abstract

This paper addresses the tuning of data-driven controllers for poorly damped linear time-invariant systems in the internal model control (IMC) architecture. In this study, fictitious reference iterative tuning (FRIT), which is one of the controller parameter tuning methods with the data obtained from a one-shot experiment, is used for tuning the controller. The Kautz expansion method in which the coefficients are tunable parameters is introduced to approximate the dynamics of linear time-invariant systems, which have poor damping characteristics. Such an approximated model with tunable parameters is implemented in the IMC architecture. A model and a controller can be realized simultaneously with a one-shot experiment by tuning the IMC with the parameterized Kautz expansion model and by using FRIT. The validity of the proposed method is examined with a numerical example.