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

Connor Edlund, Murti V Salapaka

|November 11, 2025

Summary

This summary is machine-generated.

Researchers developed an iterative learning control method for optical tweezers to precisely manipulate non-spherical particles. This approach enhances automation and performance in optical trapping experiments without needing complex dynamic models.

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Area of Science:

Physics
Engineering
Control Systems

Background:

Optical tweezers use laser light momentum to trap and manipulate micro-particles.
Dynamic control methods significantly improve trapping of spherical particles.
Controlling non-spherical particles with dynamic methods is challenging due to model limitations.

Purpose of the Study:

To develop and experimentally validate a learning-based control strategy for non-spherical particle manipulation.
To overcome the need for precise dynamic models in optical trapping.
To enhance automation and performance in optical tweezer applications.

Main Methods:

Formulation of an iterative learning controller (ILC).
Experimental validation using the out-of-plane rotation of a glass micro-rod.
Application of ILC to circumvent dynamic modeling requirements for real-time control.

Main Results:

Successful demonstration of repeatable and robust learning of control inputs.
Achieved desired out-of-plane rotation of a non-spherical micro-particle.
Validated the efficacy of ILC for complex particle manipulation.

Conclusions:

Iterative learning control is a viable and effective method for optical trapping of non-spherical particles.
This approach offers enhanced automation and performance without explicit dynamic models.
Learning-based control shows significant potential for advancing optical tweezer applications.