Abstract:
Mixed chemo-immunotherapy is an effective strategy for cancer treatment. This study utilizes a six-state nonlinear mathematical model to describe the dynamics of tumor growth
and immune responses during mixed chemo-immunotherapy. The model incorporates key
components, including tumor cells (T), natural killer cells (N), CD8+T cells (L), circulating lymphocytes (C), chemotherapy concentration (M), and immunotherapy concentration
(I). Advanced nonlinear controllers such as Terminal Sliding Mode Control (TSMC), Super Twisting Sliding Mode Control (STSMC), Adaptive Terminal Sliding Mode Control
(ATSMC), and Adaptive Super Twisting Sliding Mode Control (ASTSMC) are proposed to
optimize drug delivery and achieve rapid tumor regression. These controllers ensure drug
dosages remain within safe toxicity limits while minimizing side effects and supporting immune system recovery. To fine-tune the gain parameters of these controllers, the Improved
Grey Wolf Optimization (IGWO) algorithm is employed with the Mean Squared Error
(MSE) as the cost function. The stability of these controllers is rigorously analyzed using Lyapunov-based stability theory, ensuring reliable performance during treatment. The
proposed controllers are simulated in MATLAB/Simulink and further validated through a
hardware-in-the-loop (HIL) experimental setup using the C2000 Delfino™ MCU F28379D
Launchpad, confirming the practicality and effectiveness of the proposed approach. Simulation results show that ASTSMC achieves tumor regression in just 9 days, approximately
5.42 times faster than the previous study (48.77 days), while maintaining safe toxicity
limits and ensuring optimal drug dosages.
