Optimized design of a pseudo-linearization-based model predictive controller: Direct data-driven approach

To reduce the typical time-consuming routines of plant modelling for model-based controller designs in Single-Input Single-Output (SISO) systems, the fictitious reference iterative tuning (FRIT) method has been proposed and proven to be effective in many applications. However, it is generally difficult to properly select a reference model without a prior information on the plant. This significantly affects the control performance and might considerably degrade the system performance. To address this problem, a pseudo-linearization (PL) method using FRIT is proposed, and a new controller for SISO non-linear systems by combining data-driven and model-based control methods is designed. The proposed design considers input constraints using model predictive control. The effectiveness of the proposed method was evaluated based on several practical references using numerical simulations for hysteresis and dead zone classes and experiments involving artificial muscles with hysteresis characteristics.