Development of a novel iterative learning control algorithm using empirical mode decomposition technique

Abstract

In this paper, a novel algorithm (ILC-EMD) which integrates iterative learning control (ILC) with empirical mode decomposition (EMD) is proposed to improve learning process. To explain the divergence behavior under the conventional ILC, the EMD is utilized to decompose the tracking error signal into 11 intrinsic mode model (IMFs). By observing the root means square (RMS) of the IMFs during iterations, the first IMF is determined to be the undesired signal which could not be reduced by learning process. By using ILC-EMD, it can filter out the undesired signal and prevent the amplification effect. Experimental results on tracking the butterfly NURBS curve validate the effectiveness of the ILC-EMD algorithm.