@misc{9246017,
  abstract     = {{Acoustophoresis, the manipulation of microscopic particles by acoustic forces, has found growing use in biomedical applications ranging from blood-cell separation to single-cell positioning in microfluidic chips [1,2,3,4]. Existing multimodal control algorithms for such devices were developed under the assumption that particles do not influence one another. This simplification holds when only one particle is present or when particles are widely separated. In scenarios where particle trajectories can cross each other, or particles are present in close proximity, this simplification breaks down.

This thesis extends and evaluates a suite of acoustophoretic control algorithms, originally designed for non-interacting particles by Perticarari [5] and Öhrnberg [6], in the presence of acoustic and hydrodynamic particle-particle interactions. The work has two parts. In the first, the algorithms are re-benchmarked in the non-interaction setting. In the second, the same algorithms are evaluated inside an interaction-enabled simulation framework, where the Acoustic Interaction Force (AIF), hydrodynamic coupling between particles, and contact forces are fully accounted for.

Five controllers are studied: the epsilon-greedy algorithm [7], the model-based quadratic programme, here named (QP) or as named by Perticarari [5] Perfect-Information Local Optimisation (PILOt), the online-learning Vector-based Local Optimisation (VeLO) controller and its no-sweep variant, and a tile-coded tabular Q-learning. To ensure consistent and reproducible evaluation, a deterministic main benchmark consisting of a varied number of samples under multiple environment setups is developed and applied across all controllers.

The results show that QP, VeLO, and VeLO\_No-Sweep can be deployed under interactions without any code changes, while epsilon-greedy was insufficient for multiple particle control. QP proved to be the most efficient and reached 100% success on two and three interacting particles. Its per-step displacement matrix implicitly absorbs the acoustic interaction force. Two algorithmic improvements were identified: 
First, the tile-coded Q-learning with epsilon-decay, alpha-decay, and a shaped reward achieves 100% success at three particles with and without interactions.
Second, a modified Hessian regularisation for the VeLO is introduced in this work, together with the VeLO\_No-Sweep variant, which has no sweep phase.
These modified versions of the controllers are reliable across the tested range, with a success rate between (90-100%) at particle count of 1, 2, 3, with and without interactions. VeLO\_No-Sweep matches and sometimes exceeds standard VeLO at all conditions. 
Q-learning matches QP's success, but at a far greater computational cost. The long training time (roughly three hours per table in the three particle case) and the inflexibility of the learned policy make QP the more practical choice for deployment in simulations. 
A scalability stress test at n_P = 10 and n_P = 20, with targets arranged on a circle, further demonstrates that QP converges successfully under conditions involving particle counts far greater than three, including fully interaction-enabled dynamics.}},
  author       = {{Zowezer, Mohamad}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Acoustic Particle Control with Particle-Particle Interactions}},
  year         = {{2026}},
}