Abstract:
Tumor recurrence is a severe problem of prostate cancer hormone therapy. Long-term
androgen deprivation may result in the regeneration of androgen-independent (AI) cells.
Intermittent androgen suppression (IAS) may be used to postpone or avoid androgen-
independent relapse. Long-term hormone deprivation is associated with side effects
poorly tolerated by patients. As a result, IAS is projected to improve clinical efficacy
while reducing adverse effects and improving patients’ quality of life during off-treatment
times. Numerous mathematical models have been developed in the literature to investi-
gate cancer dynamics in the context of hormone therapy. In this research work, various
nonlinear controllers, including sliding mode control (SMC), integral (SMC), super-
twisting (SMC), integral super-twisting (SMC), and adaptive positive semi-definite bar-
rier function-based (SMC) to investigate the potential of these control strategies to
improve hormone therapy’s effectiveness and patient outcomes, specifically in address-
ing tumor recurrence and improving prostate cancer care. However, using controllers
to diminish cancer cells develops resistance to the treatment in most trials, highlight-
ing the need for accurate drug scheduling. The gains of the proposed controllers have
been tuned using improved grey wolf optimization with integral time absolute error as
an objective function. The stability of the controllers is verified through mathematical
analysis based on the Lyapunov stability theory. The proposed controllers are simulated
in MATLAB/Simulink, and hardware validation is accomplished through a hardware-in-
loop experimental setup with the C2000 DelfinoTM and the MCU F28379D Launchpad
in the real-time scenario.
