Model Predictive Control for Magnetically-Actuated Cellbots

TL;DR

This paper introduces a novel control framework combining Model Predictive Control with Gaussian Processes to improve the trajectory tracking of magnetically-actuated cellbots by effectively estimating unmodeled disturbances.

Contribution

First integration of data-driven Gaussian Process modeling with model-based MPC for magnetic cellbot control, enhancing tracking accuracy with limited data.

Findings

Improved trajectory tracking accuracy in experiments

Effective disturbance estimation with Gaussian Processes

First to combine data-driven modeling with MPC for magnetic cellbots

Abstract

This paper presents a control framework for magnetically actuated cellbots, which combines Model Predictive Control (MPC) with Gaussian Processes (GPs) as a disturbance estimator for precise trajectory tracking. To address the challenges posed by unmodeled dynamics, we integrate data-driven modeling with model-based control to accurately track desired trajectories using relatively small data. To the best of our knowledge, this is the first work to integrate data-driven modeling with model-based control for the magnetic actuation of cellbots. The GP effectively learns and predicts unmodeled disturbances, providing uncertainty bounds as well. We validate our method through experiments with cellbots, demonstrating improved trajectory tracking accuracy.