Abstract
A McKibben-type artificial muscle exhibits strong asymmetric hysteresis characteristics. In a previous study, fictitious reference iterative tuning (FRIT) was applied to a 1-DOF rotational actuator applying these muscles because it was able to determine the controller parameters using only a pair of single closed-loop data. However, it is generally difficult to design a reference model properly without information on the plant, which is strongly related to the control performance, and sometimes this leads to performance degradation. To solve this problem, in this study, we propose a method combining FRIT and model predictive control (MPC). First, we define the pseudo linearization (PL) model, which is adopted as a predictor in MPC, and is used to optimization between the model and design a control system by matching the closed-loop system to the PL model using FRIT. Additionally, it can consider input constraints by combined MPC.
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