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

Tsai M.-S.; Yen C.-L.; Yau H.-T.

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

机译：经验模式分解算法与迭代学习控制的集成，用于高精度加工

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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.

机译：本文提出了一种将迭代学习控制（ILC）与经验模式分解（EMD）相结合的新算法（ILC-EMD），以改善学习过程。为了解释传统ILC下的发散行为，利用EMD将跟踪误差信号分解为11个固有模式函数（IMF）。通过在迭代过程中观察均方根和IMF的相关值，可以确定第一个IMF是不需要的信号，该信号无法通过学习过程进行降低。此外，包含第一IMF的命令可能会由于共振效应而进一步激发机床，并导致误差信号放大。 ILC-EMD可以滤除不想要的信号并防止放大效果。跟踪蝴蝶和龙不均匀有理B样条曲线的实验结果验证了ILC-EMD算法的有效性。

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来源
《Mechatronics, IEEE/ASME Transactions on》 |2013年第3期|p.878-886|共9页
作者
Tsai M.-S.; Yen C.-L.; Yau H.-T.;
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Advanced Institute of Manufacturing with High-tech Innovations and the Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi, Taiwan;

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正文语种 eng
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关键词
Bandwidth; Convergence; Frequency domain analysis; Machine tools; Servomotors; Transfer functions; Empirical mode decomposition (EMD); intrinsic mode function (IMF); iterative learning control (ILC); resonance; zero-phase filtering;

机译：带宽;收敛;频域分析;机械工具;伺服电机传递函数;经验模式分解（EMD）;固有模式函数（IMF）;迭代学习控制（ILC）;谐振;零相位滤波;

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

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