Orientation control of optically trapped micro-rods using iterative learning control

Optical tweezers have facilitated many discoveries in science and engineering by harnessing the momentum of tightly focused laser light to trap and manipulate micron-sized or smaller particles. Many optical trapping experiments involve trapping spherical particles, and the outcomes of these experiments are measurably improved through the use of modern, dynamic control methods. The trapping and manipulation of non-spherical particles is also an important use case for optical tweezers. Extending the success of modern, dynamic control to non-spherical particles is, however, a challenging problem due to the lack of dynamic models suitable for real-time control use. Iterative learning control offers a way to circumvent the modeling issue while still achieving greater automation and trapping performance. Here, an iterative learning controller design is formulated and proven experimentally using the out-of-plane rotation of a glass micro-rod as an instantiating example. The experimental results show the repeatable and robust learning of a control input that achieves the desired out-of-plane rotation, demonstrating the potential of learning-based control methods for optical trapping applications.