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Data-Driven Control for Magnetic Actuation Capsule: Dynamic Compensation and Input Constraints

Abstract

In the magnetic actuation system, MAC (Magnetically Actuated Capsule) motion is disturbed by gastrointestinal resistance, and dynamic constraints exist between MAC and EPM (External Permanent Magnet), leading to control difficulties. This paper presents a data-driven control method for magnetic actuation system. Firstly, a data-driven modeling approach is proposed for addressing the modeling challenges, relying solely on MAC visual positioning input and EPM position output data. Secondly, To effectively track rapidly changing MAC desired trajectories, determining the upper bound of system inputs through analysis of the magnetic field relationship, and integrating it with adaptive parameter reset conditions, enables the establishment of dynamic constraints for the MAC system. Finally, dynamic compensation is applied to account for non-linear resistance terms and inaccuracies in data model representation. A dual-visual positioning experimental platform simulating the gastrointestinal environment is established to validate the proposed algorithm's effectiveness in MAC trajectory tracking under different conditions.

article Article; Early Access
date_range 2024
language English
link Link of the paper
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Featured Keywords

Gastrointestinal tract
Force
Resistance
Dynamics
Robots
Permanent magnets
Magnetic resonance imaging
Magnetically actuated capsule
data-driven adaptive control
dynamic input constraints
dual-visual positioning experimental platform
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