  To reduce the typical time-consuming routines of plant modeling for
model-based controller designs, the fictitious reference iterative tuning
(FRIT) has been proposed and has proven to be effective in many applications.
However, it is generally difficult to select a reference model properly without
information on the plant, which significantly affects the control performance
and sometimes leads to considerable performance degradation. To address this
problem, we propose a pseudo-linearization (PL) method using FRIT and design a
new controller for nonlinear systems that combines data-driven and model-based
control. This design considers the input constraints using model predictive
control. The effectiveness of the proposed method was evaluated according to
several practical references using numerical simulations for nonlinear classes
and experiments involving artificial muscles with hysteresis characteristics.
