Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump
Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump
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DOI：
10.1063/5.0032377
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发表时间：
2021-05-01
期刊：
影响因子：
3.2
通讯作者：
Ukita, Yoshiaki
中科院分区：
文献类型：
--
作者：
Abe, Takaaki;Oh-hara, Shinsuke;Ukita, Yoshiaki
我们在此报告了一项研究，利用强化学习的微流体系统的智能控制。集成微阀可实现流道切换、微泵送、微混合等多种微流控功能模块。人工智能控制微阀的应用可能有助于扩大微流体系统的多功能性。作为对这一动机的初步尝试，我们研究了强化学习算法在微蠕动泵中的应用。首先，我们假设微蠕动泵中膜片的操作具有马尔可夫性质。此后，马尔可夫决策过程的组件被定义为适应微型泵。为了获得最大化流量的泵送序列，奖励被定义为在微阀的状态转换中获得的流量。本系统成功地根据经验确定了最佳顺序，其考虑了作者没有认识到的系统的组件的物理特性。因此，证明了强化学习可以应用于微蠕动泵，并有希望用于更大，更复杂的微系统的操作。
We herein report a study on the intelligent control of microfluidic systems using reinforcement learning. Integrated microvalves are utilized to realize a variety of microfluidic functional modules, such as switching of flow pass, micropumping, and micromixing. The application of artificial intelligence to control microvalves can potentially contribute to the expansion of the versatility of microfluidic systems. As a preliminary attempt toward this motivation, we investigated the application of a reinforcement learning algorithm to microperistaltic pumps. First, we assumed a Markov property for the operation of diaphragms in the microperistaltic pump. Thereafter, components of the Markov decision process were defined for adaptation to the micropump. To acquire the pumping sequence, which maximizes the flow rate, the reward was defined as the obtained flow rate in a state transition of the microvalves. The present system successfully empirically determines the optimal sequence, which considers the physical characteristics of the components of the system that the authors did not recognize. Therefore, it was proved that reinforcement learning could be applied to microperistaltic pumps and is promising for the operation of larger and more complex microsystems.