Acoustic Manipulation of Particles in Microfluidic Chips with an Adaptive Controller that Models Acoustic Fields

Abstract

Acoustic manipulation is a technique that uses sound waves to move particles, droplets, or cells. Closed‐loop control methods based on complex, time‐varying acoustic fields have been demonstrated, but usually require accurate models of the acoustic fields or many training experiments for successful manipulation. Herein, a new adaptive control method is proposed for the acoustic manipulation of single and multiple particles inside microfluidic chips. The method is based on online machine learning of the acoustic fields. Starting with no knowledge of the fields, the controller can manipulate particles even on the first attempt, and its performance improves in subsequent attempts, yet can still readapt if the models are invalidated by a sudden change in system parameters. The controller can generalize: it can use information learned from one task to improve its performance in other tasks. Despite the machine‐learning nature of the controller, the internal models of the controller have a physical interpretation and correspond to the experimentally observed acoustic fields. The online adaptiveness of the controller should make it easier to use in practical applications, such as particle and cell sorting, microassembly, labs‐on‐chips, and diagnostic devices, as the method does not require extensive training or prior models.