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

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. Note to Practitioners—This article aims to design a data-driven method that incorporates dynamic constraint relationships between MAC and EPM, enabling capsules to move rapidly within the gastrointestinal system. This paper aims to increase control speed, enabling the energy to be primarily used for capturing the lesion area rather than during motion, and improving MAC tracking for desired trajectory accuracy. To validate the algorithm’s effectiveness under various resistances and driving forces, a dual-camera positioning system is designed and tested on a simulated gastrointestinal platform. In future work, we plan to replace the external camera with an internal capsule camera for MAC localization and design a medical system that actively utilizes patient lesion data to drive WCE for examinations.