Optimal Closed-Loop Control of Active Particles and a Minimal Information Engine

Area of Science:

• Statistical mechanics
• Active matter physics
• Thermodynamics of information

Background:

• Active particles consume energy to generate motion, deviating from equilibrium thermodynamics.
• Understanding work input and fluctuations is crucial for designing micro-machines.

Purpose of the Study:

• To determine optimal protocols for minimum work input when moving active particles.
• To analyze the impact of particle activity on work and fluctuations.
• To design and evaluate an active information engine.

Main Methods:

• Analytical derivation of optimal protocols for open-loop and closed-loop systems.
• Investigation of work input and fluctuations for different particle types.
• Derivation and analysis of a periodic active information engine.

Main Results:

• Open-loop optimal protocols are independent of particle activity, but increase work fluctuations.
• Closed-loop protocols show minimum average work for finite persistence times.
• The run-and-tumble particle yields higher precision and information efficiency in the active information engine.

Conclusions:

• Activity influences work fluctuations but not the fundamental open-loop optimal path.
• Closed-loop control offers advantages by utilizing initial measurements.
• The run-and-tumble model provides a highly efficient active information engine.