Robust Higher-Order Spatial Iterative Learning Control for Additive Manufacturing Systems
Robust Higher-Order Spatial Iterative Learning Control for Additive Manufacturing Systems
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DOI：
10.1109/tcst.2023.3243397
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发表时间：
2023-07
影响因子：
4.8
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
在这篇文章中，高阶空间迭代学习控制（HO-SILC）计划，提出了针对高度跟踪一类3-D结构制造的材料重复添加在一个逐层的方式，使用增材制造（AM）技术。AM过程本质上是迭代变化的，由于迭代变化的系统参数和表面变化导致大的模型不确定性。HO-SILC已被证明是有用的，在重复系统的模型不确定性，提高系统的性能方面的收敛速度和鲁棒性。在这篇文章中，HO-SILC被用来迭代地构建前馈控制信号，以提高多层AM结构中的零件质量。系统动力学近似的离散2-D空间卷积内核，包括层内和层到层的变化。所提出的HO-SILC框架结合了来自先前印刷的器件以及多个先前印刷的层的可用数据，以增强整体性能。基于李雅普诺夫稳定性准则，给出了变迭代HO-SILC算法鲁棒单调收敛的条件。模拟结果表明，一个良好的设计HO-SILC框架是有效的，可以提高60%的性能，称为电流体动力学射流（e-jet）打印AM过程。此外，HO-SILC对迭代变化的模型不确定性是鲁棒的，特别是在迭代变化的表面变化更明显的高层。
In this article, a higher-order spatial iterative learning control (HO-SILC) scheme is proposed, targeting heightmap tracking for a class of 3-D structures fabricated by repetitive addition of material in a layer-by-layer fashion using additive manufacturing (AM) technology. AM processes are innately iteration-varying, resulting in large model uncertainties due to iteration-varying system parameters and surface variations. HO-SILC has been shown to be useful in repetitive systems with model uncertainties by improving system performance with respect to convergence speed and robustness. In this article, HO-SILC is used to iteratively construct a feedforward control signal to improve part quality in multilayered AM constructs. The system dynamics are approximated by discrete 2-D spatial convolution kernels that incorporate in-layer and layer-to-layer variations. The proposed HO-SILC framework incorporates data available from previously printed devices, as well as multiple previously printed layers, to enhance the overall performance. The condition for robust monotonic convergence (RMC) of the iteration-varying HO-SILC algorithm is based on the Lyapunov stability criteria. Simulation results of an AM process termed electrohydrodynamic jet (e-jet) printing demonstrate that a well-designed HO-SILC framework is effective and can improve the performance by 60%. In addition, HO-SILC is robust to iteration-varying model uncertainties, especially at higher layers where iteration-varying surface variations are more pronounced.