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Model predictive control of non-interacting active Brownian particles.

Titus Quah1, Kevin J Modica1, James B Rawlings1

  • 1Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. jbraw@ucsb.edu.

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Summary

We developed a feedback control framework using model predictive control (MPC) to precisely manage active matter systems. This method effectively manipulates active Brownian particles for tasks like flocking and population splitting.

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Area of Science:

  • Physics
  • Complex Systems
  • Statistical Mechanics

Background:

  • Active matter systems exhibit non-equilibrium distributions due to self-propulsion, leading to phenomena like flocking and clustering.
  • Controlling these systems is crucial for applications in directed assembly, programmable materials, and microfluidic actuation.
  • Existing intuitive control methods are insufficient for complex, coupled dynamics.

Purpose of the Study:

  • To develop an automatic feedback control framework for managing the spatiotemporal distribution of active matter.
  • To apply model predictive control (MPC) for precise manipulation of active Brownian particles.
  • To demonstrate the controller's capability in achieving complex objectives like population splitting and flocking.

Main Methods:

  • Utilized model predictive control (MPC), a model-based algorithm predicting future states and optimizing control inputs.
  • Developed an MPC model based on the Smoluchowski equation with a self-propulsive convective term.
  • Simulated non-interacting active particles using Brownian dynamics under an actuated external field.

Main Results:

  • Successfully controlled the distribution of active Brownian particles using the MPC framework.
  • Demonstrated the ability to split and juggle sub-populations of particles.
  • Achieved polar order flocking control, showcasing the controller's versatility.

Conclusions:

  • The developed MPC framework provides an effective method for automatic feedback control of active matter systems.
  • This approach enables precise manipulation for complex tasks, advancing applications in programmable materials and microfluidics.
  • The study highlights the potential of MPC in managing non-equilibrium systems with self-propelled particles.