Androgen deprivation therapy (ADT) is commonly used to treat prostate
cancer, but its 5-year survival rate remains low at 57%. Prolonged
ADT treatment can lead to increased toxicity and drug resistance. To
address these challenges, this study proposes adaptive therapy that
combines chemotherapy or immunotherapy with discontinuation of hormone
therapy. The study applies the super-twisting sliding mode control
(STSMC) algorithm to the ADT model to implement adaptive dosing
based on control laws derived from these algorithms. The main
objective is to quickly minimize cancer cells and reduce prolonged drug
exposure. An active control algorithm (ATS)-based Takagi-Sugeno fuzzy
controller is also introduced and compared to the STSMC design. The
ATS fuzzy controller significantly reduces therapy duration to six months
while maintaining global asymptotic stability. The controllers are implemented
using the Linear Matrix Inequality (LMI) algorithm and the
YALMIP toolbox, and their effectiveness is validated through MATLAB
and Simulink simulations. This study presents a novel approach
to improve prostate cancer treatment outcomes by integrating nonlinear
control algorithms and adaptive dosing strategies, aiming to reduce
treatment duration and minimize drug exposure, ultimately enhancing
patient outcomes in prostate cancer management.