Minimization of Cancer Cells with ATS Fuzzy Controller: An Application to Androgen Deprivation therapy

Abstract 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.