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Iterative Learning Control of Two-Phase Laminar Flow Interface in Y-Shaped Microfluidic Channel

机译:Y型微流控通道中两相层流界面的迭代学习控制

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In Y-shaped microfluidic chip, the laminar flow refers to a phenomenon that two fluids introduced from two inlets flow side by side without turbulence and form two stable streams in outlet with a common interface. The interface position of laminar flow has significant influence in some experiment analysis of life science, such as molecule diffusion and solvent extraction, where there are still a series of problems associated with the manipulation of interface position. In this brief, an iterative learning control (ILC) scheme is proposed for precise control of the laminar flow position. ILC can improve the current input signal iteratively based on the experimental results achieved in the previous trials and eventually produce the desired interface position in output channel. To verify the effectiveness of the proposed ILC scheme, we design and fabricate the Y-shaped microfluidic chips. Furthermore, two different scenarios are considered, where the results show that an appropriate input signal achieving the desired output can be promptly obtained via ILC. The three main advantages of the proposed control scheme lie in: 1) the simple structure and the feedforward characteristic of the control scheme make it implementable in an easy way; 2) it is a partially model-free method, and hence, no accurate model of laminar flow is required and system uncertainties can be dealt with rigorously when designing the controller; and 3) compared with the well-adopted traversal methods in life science research, the idea of ILC reduces the number of experimental trials remarkably.
机译:在Y形微流控芯片中,层流是指从两个入口引入的两种流体并排流动而没有湍流,并在出口处形成具有共同界面的两个稳定流的现象。层流的界面位置在生命科学的一些实验分析中具有重要影响,例如分子扩散和溶剂萃取,在这些领域中,界面位置的操作仍然存在一系列问题。在此简介中,提出了一种迭代学习控制(ILC)方案,用于精确控制层流位置。 ILC可以根据先前试验中获得的实验结果迭代地改善当前输入信号,并最终在输出通道中产生所需的接口位置。为了验证所提出的ILC方案的有效性,我们设计并制造了Y形微流控芯片。此外,考虑了两种不同的情况,结果表明可以通过ILC快速获得实现所需输出的适当输入信号。所提出的控制方案的三个主要优点在于:1)控制方案的简单结构和前馈特性使其易于实现。 2)它是部分无模型的方法,因此,不需要精确的层流模型,并且在设计控制器时可以严格处理系统不确定性; 3)与生命科学研究中广泛采用的遍历方法相比,ILC的思想显着减少了实验次数。

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