This internship project tackles the critical challenges in anesthesia management by proposing innovative solutions through the integration of advanced AI techniques and control theory. Specifically, it focuses on incorporating Sliding Mode Control (SMC) to enhance robustness and achieve finite-time convergence, while synergizing with machine learning methods for improved adaptability and efficiency.
The core objective is to address the following pivotal research question:
How can the stability and safety of this hybrid approach be rigorously and mathematically guaranteed?
This project not only seeks to establish a solid theoretical foundation for the AI-SMC coupling but also aims to deliver practical implementations for safer and more reliable anesthesia management systems.
