Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump

Abstract

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.

Machine learning for microfluidic design and control

“…99,100 Abe, Oh-Hara, and Ukita built a proof-of-concept system for applying RL to the control of microvalving to set the flow rate of a persitstaltic pump. 101 A 3-valve state was modeled as a Markov process, and was used to simulate flow rates produced from different state cycles. While used for a simple system, this principle could be particularly impactful when applied to more complex microfluidic valving networks.…”

Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm

“…However, certain proposed models may be susceptible to variations in working conditions, leading to performance limitations. Abe et al 6 employed RL in the continuous decision‐making process for optimizing the phase of microperistaltic pumps. The findings indicated that RL performed effectively in the optimization of the pump actuation sequence.…”

Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data

“…Deep learning [14][15][16] and Machine learning (ML) algorithms [17,18] improve the accuracy of microfluidic system performance because they process large amounts of data from experiments, field measurements, and large-scale numerical simulations [19] in fluid dynamics. Therefore, DL and ML facilitate modular and agile modeling frameworks for solving many problems in fluid mechanics, such as experimental data processing, reducedorder modeling, shape optimization, turbulence closure modeling, and control.…”