2D Density Control of Micro-Particles using Kernel Density Estimation

TL;DR

This paper presents a method for controlling the 2D density distribution of micro-particles in a dielectric fluid using electric fields and kernel density estimation, enabling precise pattern shaping.

Contribution

It introduces a novel optimal control framework combining a nonlinear particle motion model with KDE-based density feedback for pattern formation.

Findings

Successful simulation of particle density shaping from uniform to Gaussian distribution.

Demonstration of electrode potential adjustments to achieve desired density patterns.

Validation of the approach through numerical experiments.

Abstract

We address the problem of 2D particle density control. The particles are immersed in dielectric fluid and acted upon by manipulating an electric field. The electric field is controlled by an array of electrodes and used to bring the particle density to a desired pattern using dielectrophoretic forces. We use a lumped, 2D, capacitive-based, nonlinear model describing the motion of a particle. The spatial dependency of the capacitances is estimated using electrostatic COMSOL simulations. We formulate an optimal control problem, where the loss function is defined in terms of the error between the particle density at some final time and a target density. We use a kernel density estimator (KDE) as a proxy for the true particle density. The KDE is computed using the particle positions that are changed by varying the electrode potentials. We showcase our approach through numerical simulations, where we demonstrate how the particle positions and the electrode potentials vary when shaping the particle positions from a uniform to a Gaussian distribution.