基于经验模态分解算法的永磁直线同步电机迭代学习控制

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Abstract In the process of iterative learning control (ILC) of the servo system driven by permanent magnet linear synchronous motor (PMLSM), the accumulation of tracking error would lower convergence rate or even system divergence. In this paper, a closed-loop ILC controller was designed, and then a method that combined empirical mode decomposition (EMD) algorithm with ILC was adopted to decompose the tracking error in ILC. Then the divergent components were screened and eliminated. Accordingly, the convergence of ILC was guaranteed, and the convergence rate was improved. The simulation and experimental results show that, compared with the traditional ILC, the presented method can produce better tracking results, and guarantee that the output trajectory track the anticipant trajectory quickly and precisely with fewer iterations.

Key words： Permanent magnet linear synchronous motor (PMLSM) iterative learning control (ILC) empirical mode decomposition (EMD) tracking error

Received: 10 September 2015 Published: 29 March 2017

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TP273

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	Wang Limei
	Sun Lu
	Chu Sheng

Cite this article:

Wang Limei,Sun Lu,Chu Sheng. Iterative Learning Control of Permanent Magnet Linear Synchronous Motor Based on Empirical Mode Decomposition Algorithm[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 164-171.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I6/164

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