This study presents a model-based closed-loop control strategy to optimize VNS in real time, aiming to enhance its efficacy compared to traditional open-loop methods. A computationally efficient compartmental vago-vagal reflex model is developed, capturing key physiological mechanisms, including intramural and vagal reflex pathways. Model Predictive Control (MPC) is employed to adaptively tune VNS parameters based on real-time feedback of gastric motility and emptying.
The control framework integrates two compartmental models: a healthy gastric model, which provides reference trajectories for variables such as gastric emptying rate, intragastric pressure, mixing efficiency, and reverse transpyloric flow; and a disease-state model, representing pathological gastric function. A cost function minimizes deviations between the two models, allowing the MPC to iteratively adjust VNS inputs to steer gastric behavior toward physiological norms.
Simulation results show that MPC-driven VNS can effectively regulate gastric motility and enhance gastric emptying, underscoring its potential for personalized neuromodulation therapies. This framework lays the groundwork for experimental validation and clinical translation, offering a promising step toward adaptive VNS treatments for gastric disorders.