Efficient control protocols for an active Ornstein-Uhlenbeck particle
arXiv:2304.12926 · doi:10.1103/PhysRevE.108.024117
Abstract
Designing a protocol to efficiently drive a stochastic system is an active field of research. Here we extend such control theory to an active Ornstein-Uhlenbeck particle (AOUP) in a bistable potential, driven by a harmonic trap. We find that protocols designed to minimize the excess work (up to linear-response) perform better than naive protocols with constant velocity for a wide range of protocol durations.
arXiv comment10 pages, 8 figures, Minor revision
References in corpus (45)
- A nonequilibrium equality for free energy differences
- Active Particles in Complex and Crowded Environments
- Motility-Induced Phase Separation
- How far from equilibrium is active matter?
- Diffusive transport without detailed balance in motile bacteria: Does microbiology need statistical physics?
- Optimal finite-time processes in stochastic thermodynamics
- Active matter
- Thermodynamic metrics and optimal paths
- Statistical Mechanics of Active Ornstein Uhlenbeck Particles
- Steady state, relaxation and first-passage properties of a run-and-tumble particle in one-dimension
- Generalized energy equipartition in harmonic oscillators driven by active baths
- Entropy production of active particles and for particles in active baths
- Optimal protocols for minimal work processes in underdamped stochastic thermodynamics
- The geometry of thermodynamic control
- Active Brownian Motion in Two Dimensions
- Glassy dynamics of athermal self-propelled particles: Computer simulations and a nonequilibrium microscopic theory
- Active Particles Bound by Information Flows
- Jamming and Attraction of Interacting Run-and-Tumble Random Walkers
- Optimal finite-time erasure of a classical bit
- Realization of a feedback controlled flashing ratchet
- The Entropy Production of Ornstein-Uhlenbeck Active Particles: a path integral method for correlations
- Optimal run-and-tumble based transportation of a Janus particle with active steering
- Autonomous engines driven by active matter: Energetics and design principles
- Optimal driving of isothermal processes close to equilibrium
- A geometric approach to optimal nonequilibrium control: Minimizing dissipation in nanomagnetic spin systems
- Dynamic Riemannian Geometry of the Ising Model
- Optimal Control in Stochastic Thermodynamics
- Maximizing power and velocity of an information engine
- Active Brownian particles and run-and-tumble particles separate inside a maze
- Thermodynamic geometry of minimum-dissipation driven barrier crossing
- Optimal transport and control of active drops
- Optimal control of transitions between nonequilibrium steady states
- Reinforcement learning of optimal active particle navigation
- Entropy production of nano systems with timescale separation
- How irreversible are steady-state trajectories of a trapped active particle?
- Information and maximum power in a feedback controlled Brownian ratchet
- Optimal Control of Rotary Motors
- Bayesian information engine that optimally exploits noisy measurements
- Delay-induced transport in a rocking ratchet under feedback control
- Heat fluctuations in a harmonic chain of active particles
- Fluctuations of entropy production of a run-and-tumble particle
- Using Active Matter to Introduce Spatial Heterogeneity to the Susceptible-Infected-Recovered Model of Epidemic Spreading
- Energetic cost of feedback control
- Optimal Control of the F-ATPase Molecular Motor
- Most probable path of an active Brownian particle
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- Active matter under control: Insights from response theory
- Active particles in moving traps: minimum work protocols and information efficiency of work extraction
- Optimal Control in Soft and Active Matter
- Efficiently driving F molecular motor in experiment by suppressing nonequilibrium variation
- Optimal run-and-tumble in slit-like confinement
- Thermodynamic nature of irreversibility in active matter
- Jerky chiral active particles
- Global thermodynamic manifold for conservative control of stochastic systems