Higher-Order Spatial Iterative Learning Control for Additive Manufacturing
Higher-Order Spatial Iterative Learning Control for Additive Manufacturing
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DOI：
10.1109/cdc45484.2021.9682875
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Zahra Afkhami;David Hoelzle;K. Barton
中科院分区：
文献类型：
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作者：
Zahra Afkhami;David Hoelzle;K. Barton
本文提出了一种高阶空间迭代学习控制（HO-SILC）框架，用于增材制造（AM）技术制造的三维结构的高度图跟踪。在文献中，第一层空间ILC （FO-SILC）已与添加剂工艺一起用于调节单层结构。然而，ILC具有未开发的潜力来调节通过逐层重复添加材料制造的AM结构。由于系统参数的反复变化，在这些结构中估计适当的前馈信号可能具有挑战性。本文采用HO-SILC迭代构造前馈信号，提高三维结构的器件质量。为了更真实地表示加性过程，包括了植物动力学中的迭代变化不确定性和输入信号中的非重复噪声。我们利用文献中现有的FO-SILC模型，并将其扩展到HO-SILC框架，该框架结合了先前打印设备的可用数据，以及多个先前打印的层，以提高整体性能。随后，给出了标称HO-SILC算法的单调稳定性和渐近稳定性条件。
This paper presents a higher-order spatial iterative learning control (HO-SILC) framework for heightmap tracking of 3D structures that are fabricated by additive manufacturing (AM) technology. In the literature, firstorder spatial ILC (FO-SILC) has been used in conjunction with additive processes to regulate single-layer structures. However, ILC has undeveloped potential to regulate AM structures that are fabricated by the repetitive addition of material in a layer-by-layer manner. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying system parameters. In this paper, HO-SILC is used to iteratively construct the feedforward signal to improve device quality of 3D structures. To have a more realistic representation of the additive process, iteration varying uncertainties in the plant dynamics and non-repetitive noise in the input signal are included. We leverage the existing FO-SILC models in the literature and extend them to a HO-SILC framework that incorporates data available from a previously printed device, as well as multiple previously printed layers to enhance the overall performance. Subsequently, the monotonic and asymptotic stability conditions for the nominal HO-SILC algorithm are illustrated.