Data-Driven Control Parameters Tuning of Internal Model Controllers for a Class of Nonlinear Systems in the Presence of Deterministic Disturbances

摘要

In this paper, we propose a data-driven control parameters tuning of internal model controllers for a class of nonlinear systems in the presence of deterministic disturbances. The fictitious iterative feedback tuning (FRIT) for nonlinear internal model control (IMC) structure is focused on the assumption that the system has a one-to-one correspondence between the input time series and the output time series. The present work extends the FRIT for nonlinear systems to the case where a deterministic disturbance is fed to the process output, and shows that the approach simultaneously obtains both plant and controller parameters. In addition, the present work shows that the approach can treat disturbances with unknown parameters by estimating unknown parameters. Finally, the efficiency of the proposed method is shown through a numerical example.