Direct learning of control efforts for trajectories with different magnitude scales

Abstract

In this paper, we propose a new concept - direct learning which is denned as the generation of the desired control input profile directly from existing control input profiles without any repeated learning. The motivation of developing direct learning control schemes is to overcome the limitation of conventional learning control methods which require that the desired tracking patterns (trajectories) be strictly repeatable throughout the learning process. The main advantages of the direct learning are (1) the capability of fully utilizing the preobtained control input signals which may correspond to tracking patterns with different magnitude scales and be achieved through various control approaches; (2) direct generation of the desired control input profile without repeating the operation cycles. The focus of this paper is on direct learning for a class of trajectories which have identical operation periods but are different in magnitude scales.