Abe Takaaki, Oh-Hara Shinsuke, Ukita Yoshiaki
Department of Engineering, Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8510, Japan.
Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8510, Japan.
Biomicrofluidics. 2021 May 6;15(3):034101. doi: 10.1063/5.0032377. eCollection 2021 May.
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.
我们在此报告一项关于使用强化学习对微流控系统进行智能控制的研究。集成微阀用于实现各种微流控功能模块，如流路切换、微泵送和微混合。应用人工智能控制微阀可能有助于扩展微流控系统的多功能性。作为朝着这一目标的初步尝试，我们研究了强化学习算法在微型蠕动泵中的应用。首先，我们假设微型蠕动泵中隔膜的运行具有马尔可夫性质。此后，为适应微型泵定义了马尔可夫决策过程的组件。为了获得使流速最大化的泵送序列，奖励被定义为微阀状态转换中获得的流速。本系统成功地通过实验确定了最优序列，该序列考虑了作者未识别的系统组件的物理特性。因此，证明了强化学习可应用于微型蠕动泵，并且对于更大、更复杂的微系统的运行具有前景。
