Real-time calibration of a feedback trap

TL;DR

This paper introduces an extended recursive least-squares algorithm enabling real-time calibration of feedback traps, effectively compensating for drift over hours, thus improving manipulation and measurement of small particles.

Contribution

The paper presents a novel extended RLS algorithm for real-time calibration of feedback traps, addressing drift issues in long-duration experiments.

Findings

The extended RLS algorithm accurately recovers known parameters in simulations.

Experimental diffusion coefficient estimates match expected physical properties.

The method enables stable, long-term control of particles in feedback traps.

Abstract

Feedback traps use closed-loop control to trap or manipulate small particles and molecules in solution. They have been applied to the measurement of physical and chemical properties of particles and to explore fundamental questions in the non-equilibrium statistical mechanics of small systems. These applications have been hampered by drifts in the electric forces used to manipulate the particles. Although the drifts are small for measurements on the order of seconds, they dominate on time scales of minutes or slower. Here, we show that an extended recursive least-squares (RLS) parameter-estimation algorithm can allow real-time measurement and control of electric and stochastic forces over time scales of hours. Simulations show that the extended-RLS algorithm recovers known parameters accurately. Experimental estimates of diffusion coefficients are also consistent with expected physical properties.