学習の最適収束を考慮した適応型繰り返し学習制御

概要

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Authors proposed the machine control technique for manipulating mans condition by the iterative learning control (ILC) based on the biomedical signal. However, there was a possibility that ILC becomes unstable when the system with low repeatability like man was included in controlled object. Then, “Adaptive type ILC (ATILC)” that adjusted the learning gain according to the characteristic change of controlled object at each trial was newly proposed in this paper. The ATILC adjusts learning gain by using the model parameter after controlled object is modeled by first-order delay based on the time series of I/O in one trial of controlled object. Even if controlled system is accompanied by the property change, the output trajectory of controlled object follows to objective one by ATILC. The effectiveness of the proposed method was confirmed by simulation work by using the approximation model of biological signal.