Ornstein-Uhlenbeck information swimmers with external and internal feedback controls

TL;DR

This paper introduces two models of information-driven active particles with feedback controls, demonstrating how measurement timing influences their steady-state velocity and efficiency, with internal control often outperforming external control under high noise.

Contribution

The paper proposes novel feedback-controlled active particle models using Ornstein-Uhlenbeck processes, analyzing their steady-state behavior and efficiency optimization through numerical simulations.

Findings

Both models reach finite average velocities in noisy environments.

Efficiency is maximized by tuning measurement intervals.

Internally controlled swimmers outperform externally controlled ones at high noise levels.

Abstract

Using an underdamped active Ornstein-Uhlenbeck particle, we propose two information swimmer models having either external or internal feedback control and perform their numerical simulations. Depending on the velocity that is measured after every fixed time interval (measurement time), the friction coefficient is modified in the externally controlled model, whereas the persistence time for the activity is changed in the internally controlled one. In the steady state, both of these information swimmers acquire finite average velocities in the noisy environment, and the efficiency can be maximized by tuning the measurement time. The internally controlled swimmer can generally achieve a larger velocity and efficiency than the externally controlled one when the active fluctuation is large.