Adaptive Predictive Control Based Therapy of Bone Marrow Cancer

摘要

This paper starts by reviewing the mathematical model for tumor growth as well as the pharmacokinetics and pharmacodynamics models of the drug, so that the therapy can be as close as possible to reality. A Nonlinear Model Predictive Control algorithm (NMPC) is used to find the optimal drug dose, in order to reduce the bone marrow tumor density. The Recursive Least Squares algorithm is used to learn the parameters of the tumor growth model, in order to obtain an adaptive NMPC strategy. This control strategy is applied to a bone microenvironment model to schedule a therapy for reducing tumor density.