Enhancing the efficacy of hormone therapy in prostate cancer through Conditional Super-Twisting Sliding Mode Control and Redfox optimization

Abstract

Tumor recurrence remains a significant challenge in prostate cancer hormone therapy, particularly because of the regeneration of androgen-independent cells following prolonged androgen deprivation. Intermittent androgen suppression has been suggested to delay relapse while minimizing side effects, thereby improving patients’ quality of life. This study compares several control strategies, including the Sliding Mode Control (SMC), Integral Sliding Mode Control (ISMC), and Conditional Super-Twisting Sliding Mode Control (CSTSMC), along with a novel Redfox optimization technique for parameter tuning. This study aimed to evaluate the potential of these strategies to enhance the effectiveness of hormone therapy in controlling tumor relapse. Our analysis reveals that the CSTSMC outperforms other control methods in terms of robustness, tracking accuracy, and adaptability, making it the most effective option for this application. Despite the benefits of controller-based tumor cell reduction, treatment resistance remains a major concern, underscoring the importance of precise drug scheduling. To enhance the controller performance, parameter tuning was performed using the Redfox optimization algorithm with the integral of time absolute error serving as the objective function. The system stability was ensured using a Lyapunov-based theoretical framework. The effectiveness of the designed controllers was assessed using MATLAB/Simulink simulations, followed by real-time validation through a hardware-in-the-loop setup employing the C2000 Delfino™ microcontroller and F28379D Launchpad board.