Fictitious reference iterative tuning of internal model controllers for a class of nonlinear systems

This paper presents a direct data-driven design or tuning of the internal model control architecture for a class of non-linear systems to achieve the desired output. We assume that the structure of a nonlinear system addressed here is known and the parameters are unknown. In addition, a nonlinear system addressed here is assumed to be with the property that the input time series and the output time series has one to one relation. For this type of nonlinear system, the internal model controller that is represented by the parameters of a model is introduced. Then, fictitious reference iterative tuning, which is one of the data-driven controller tuning methods based on the direct use of one-shot experimental data, is extended for tuning the parameterized internal model controllers. It is also shown that the cost function to be minimized in fictitious reference iterative tuning is related to both of the achievement of the desired tracking and the attainment of a model. That is, the proposed method in this paper enables us to simultaneously obtain a model and a controller by applying only one-shot experimental data to the parameterized internal model controller. A numerical example is also illustrated to show the validity of the result.