Integration of an Empirical Mode Decomposition Algorithm With Iterative Learning Control for High-Precision Machining

In this paper, a novel algorithm (ILC-EMD) that 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 functions (IMFs). By observing the root mean square and the correlation values of the IMFs during iterations, the first IMF is determined to be the undesired signal which could not be reduced by learning process. Furthermore, the command containing the first IMF could further excite the machine tool due to the resonance effects and cause the amplification of the error signal. The ILC-EMD can filter out the undesired signal and prevent the amplification effect. Experimental results on tracking the butterfly and dragon nonuniform rational B-spline curves validate the effectiveness of the ILC-EMD algorithm.