Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems

Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively.