Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Abstract

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.