Physics-Based Reduced-Order Modeling of Magnetic Microparticle Swarms for Biomedical Control
Boudehane Fadal, Lyès Mellal, Trung Son DO, David Folio, Antoine Ferreira
AI summary
Problem
Precise control of magnetic particle swarms is hindered by highly nonlinear collective dynamics that lack compact, physics-grounded models suitable for predictive feedback regulation.
Approach
The authors model swarm morphology using two principal radii, explicitly linking them to magnetic field curvature, axial gradients, and actuation frequency through anisotropic stiffness terms.
Key results
- Sub-millimeter prediction accuracy for steady-state swarm radii (RMSE: 0.25 mm and 0.42 mm)
- Quantification of pronounced stiffness anisotropy (Sx/Sy ≈ 0.16) driven by dipole chaining
- Validated frequency-dependent expansion model capturing non-monotonic radius scaling
- Demonstrated tractable MIMO PID feedback control using the compact parametric formulation
Why it matters
Provides a compact, interpretable framework that enables predictive closed-loop control of magnetic swarms for minimally invasive biomedical applications.
Abstract
Magnetic particle swarms are governed by rich nonlinear collective dynamics that complicate predictive, feedback-based control in biomedical microrobotics. We de- velop a physics-based reduced-order ellipse model that de- scribes the swarm morphology by its principal radii (r1, r2). At steady state, these radii depend explicitly on magnetic-field cur- vature, axial gradients, and actuation angular velocity through anisotropic stiffness terms. Model parameters are identified experimentally, yielding low validation errors (RMSE: 0.25 mm for r1 and 0.42 mm for r2) and revealing pronounced stiffness anisotropy (Sx/Sy ≈0.16). The resulting formulation provides compact, interpretable equations that enable tractable control design and feedback regulation of magnetic particle swarms.