A data-driven approach to cascaded internal controllers: Simultaneous attainment of controllers and models

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.
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A data-driven approach to cascaded internal controllers: Simultaneous attainment of controllers and models