DIRECT LEARNING OF CONTROL EFFORTS FOR TRAJECTORIES WITH DIFFERENT MAGNITUDE SCALES

In this paper, we propose a new concept-direct learning which is defin ed as the generation of the desired control input profile directly fro m existing control input profiles without any repeated learning. The m otivation 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 repeata ble throughout the learning process. The main advantages of the direct learning are (1) the capability of fully utilizing the pre-obtained c ontrol input signals which may correspond to tracking patterns with di fferent magnitude scales and be achieved through various control appro aches; (2) direct generation of the desired control input profile with out repeating the operation cycles. The focus of this paper is on dire ct learning for a class of trajectories which have identical operation periods but are different in magnitude scales.