Thesis title: Advanced nonlinear controllers for the chemotherapy of brain tumor
Brain tumors represent a significant challenge in the medical field due to their complex nature and the critical environment in which they develop. These tumors result from the abnormal proliferation of cells within the brain, leading to the formation of masses that can be either benign or malignant. The treatment approach for brain tumors is highly dependent on the type, the location, and the severity of the tumor. Malignant brain tumors, in particular, present a substantial challenge due to their aggressive growth patterns and their proximity to essential brain tissues. Because of the sensitive nature of brain tissue and the intricate location of these tumors, surgical intervention, which is a common treatment for other types of tumors, is often not recommended for malignant brain tumors. The reason for this caution is that malignant tumors are frequently intertwined with critical and sensitive areas of the brain, making surgical removal risky and potentially leading to severe neurological damage or loss of critical brain functions.
As a result, alternative treatment strategies, such as chemotherapy, become crucial for managing malignant brain tumors. Chemotherapy involves the administration of drugs designed to target and kill rapidly dividing tumor cells; however, the effectiveness of chemotherapy is heavily reliant on precise dosing and timing. The goal is to maximize the elimination of tumor cells, while minimizing damage to healthy brain cells and preserving the patient’s immune function. Striking this balance is critical because an overdose of chemotherapy drugs can lead to toxicity and damage to healthy tissues, while an underdose may not be effective in controlling tumor growth. Therefore, the precise control of chemotherapy dosing is essential to achieve the best possible treatment outcomes. This research focuses on developing advanced nonlinear controllers to optimize chemotherapy dosing. These controllers, including Adaptive Terminal Sliding Mode Control (AT-SMC), Adaptive Super-Twisting Sliding Mode Control (AS-SMC), Terminal Synergetic Control (TSC), Fuzzy Logic Control (FLC), and Barrier Function Based Sliding Mode Control (BF-SMC), are designed to dynamically adjust the chemotherapy dosage in response to the tumor’s progression, ensuring that tumor
cells are effectively targeted while healthy cells are preserved. The controllers are rigorously tested using MATLAB simulations under various conditions to evaluate their effectiveness in maintaining the balance between eliminating tumor cells and preserving healthy tissue. The stability and convergence of these systems are verified using Lyapunov theory, which confirmed that the controllers are capable of achieving the desired outcomes. Among the tested strategies, AT-SMC, AS-SMC, and BF-SMC showed the most promising results, demonstrating minimal steady-state error, fast convergence, and efficient drug usage. These findings suggest that advanced nonlinear control strategies hold significant potential for enhancing the effectiveness of chemotherapy in treating brain tumors, offering a more targeted and safe approach to managing this challenging condition.