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Optimal Power Extraction from Active Particles with Hidden States.

Luca Cocconi1,2, Jacob Knight2, Connor Roberts2

  • 1The Francis Crick Institute, London NW1 1AT, United Kingdom.

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Summary
This summary is machine-generated.

We found optimal ways to extract power from active particles using feedback control, even when only their movement, not their force, is observed. These methods work even for simple particle models.

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Area of Science:

  • Physics
  • Statistical Mechanics
  • Active Matter

Background:

  • Active particles exhibit complex dynamics driven by internal energy sources.
  • Feedback control offers a pathway to manipulate and harness energy from active matter systems.
  • Observational limitations, such as inaccessible self-propulsion forces, pose challenges in controlling active particles.

Purpose of the Study:

  • To identify generic protocols for optimal power extraction from a single active particle.
  • To explore power extraction under continuous feedback control with limited observation.
  • To establish theoretical bounds for active matter engine performance.

Main Methods:

  • Utilized a Bayesian approach combined with the Onsager-Machlup path integral formalism.
  • Applied the framework to specific models: free run-and-tumble and active Ornstein-Uhlenbeck dynamics in 1D.
  • Assumed only spatial trajectory is observable, not instantaneous self-propulsion force.

Main Results:

  • Derived generic protocols for optimal power extraction.
  • Demonstrated positive work extraction even in models with time-symmetric trajectories and zero informational entropy production.
  • Established theoretical performance bounds for active particle systems.

Conclusions:

  • Optimal power extraction is achievable with feedback control under partial observation.
  • The developed protocols provide a benchmark for realistic active matter engines.
  • Insights into energy harvesting from non-equilibrium systems are provided.