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Androgen deprivation therapy, also known as hormone therapy, is a standard treatment for prostate cancer, but its 5-year survival rate is only 57 percent. Moreover, the prolonged therapy duration results in increased medication toxicity and drug resistance. A solution to this problem is adaptive therapy, which applies chemotherapy or immunotherapy after hormone therapy is withdrawn. In the proposed work, the idea of adaptive therapy is used by applying nonlinear control algorithms to the proposed ADT model and making the drug dosage based on the control laws that result from applying these algorithms. The overall objective is to reduce the cancer cells to zero in a minimum amount of time to reduce prolonged exposure to drugs. For this, a super-twisting sliding mode controller (STSMC) is applied to reduce the number of androgenindependent (AI) and androgen-dependent (AD) cells. Next, an active control algorithm (ATS)-based Takagi-Sugeno fuzzy controller is introduced and compared to the STSMC design. The ATS fuzzy controller has significantly reduced the therapy duration to two months and is also globally asymptotically stable. Using the Linear Matrix Inequality (LMI) algorithm and the YALMIP toolbox, the controller has been built. Theoretical outcomes are validated using MATLAB and Simulink.

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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.