2014 | OriginalPaper | Chapter
Iterative Learning Control Design with High-Order Internal Model for Permanent Magnet Linear Motor
Authors : Wei Zhou, Miao Yu, Donglian Qi
Published in: Intelligent Computing in Smart Grid and Electrical Vehicles
Publisher: Springer Berlin Heidelberg
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In this paper, an iterative learning control algorithm was proposed for improving the permanent magnet linear motor (PMLM) velocity tracking performance under iteration-varying desired trajectories. A high-order internal model (HOIM) was utilized to describe the variation of desired trajectories in the iteration domain. By incorporating the HOIM into P-type ILC, the convergence of tracking error can be guaranteed. The rigorous proof was presented to show that the system error converge well. The simulation results indicate that the proposed high-order internal models based approach yields a good performance and achieves perfect tracking.