Iterative Learning Control of Dual-Axis Linear Motor Platform Based on Modified Empirical Mode Decomposition
In response to the issue of contour error accumulation in the iterative learning control system for a dual-axis linear motor platform with cross-coupling,a control strategy of modified empirical mode decom-position algorithm is proposed in this paper.Firstly,a single-axis PDFF position controller is designed to meet the high precision positioning requirements of PMLSM.Secondly,a self-adaptive PD-type cross-cou-pled iterative learning controller is designed,which possesses the characteristic of adaptive adjustment.Be-sides,in response to solve the endpoint effect and mode mixing problem for EMD algorithm,a novel im-proved algorithm based on interactive linear continuation and complementary ensemble empirical mode de-composition is proposed.This algorithm can decompose the contour errors of each iteration and eliminate the components that affect error convergence.Through simulation analysis and a comparison with the tradi-tional iterative learning control,the paper demonstrates that the proposed method exhibits faster convergence speed and can achieve high-precision contour tracking control of linear motors with fewer iterations.
dual-axis linear motor platformcontour erroriterative learninginteractive linear extrapola-tioncomplementary ensemble
万方数据
维普
