  Adaptive FRIT (A-FRIT) with exponential forgetting (EF) has been proposed for
time-varying systems to improve the data dependence of FRIT, which is a direct
data-driven tuning method. However, the EF-based method is not a reliable
controller because it can cause significant degradation of the control
performance and instability unless the persistent excitation (PE) condition is
satisfied. To solve this problem, we propose a new A-FRIT method based on
directional forgetting (DF) and exponential resetting that can forget old data
without instability regardless of the PE condition. To confirm the
effectiveness of the proposed method, we applied it to artificial muscle
control with strong asymmetric hysteresis characteristics and evaluated its
robust performance against load changes during the experiment. The experimental
results show that the proposed method based on DF achieves high control
performance and is robust against changes in the characteristics and/or target
trajectory. The proposed method is also practical because it does not require
system identification, model structure, or prior experimentation.
