In this thesis, a dynamic model for the growth of cancerous tumors has been presented using a logistic model, which employs predictive control techniques aimed at optimizing chemotherapy and anti-angiogenesis drugs. This model is designed to control the precise and effective administration of medication, based on optimization indices including the reduction of cancer cell population and the optimization of drug dosage.
Initially, equilibrium points of the dynamics were solved analytically by hand, followed by the display of results through precise simulations. In this regard, simulations were used to examine the model's behavior and the impact of drugs. A key aspect of this research is the comprehensive analysis of noise, disturbances, and uncertainties in model parameters, showing that the proposed system maintains optimal performance under real conditions.
This study utilizes advanced robust methods with predictive control techniques to mitigate the adverse effects due to uncertainties and measurement disturbances. The implicit optimization in this method, targeted by specific optimization indices and finely tuned parameters, leads to the design of optimized dosing protocols for individualized cancer treatment, significantly enhancing treatment efficacy. Simulation results indicate a notable reduction in tumor size and optimization of drug dosage, reducing side effects and increasing treatment efficacy.
The findings demonstrate a significant reduction in cancer cell populations and optimized drug dosage in the presence of noise and environmental disturbances. This achievement not only helps improve the quality of life for cancer patients but can also serve as an effective step toward developing advanced, personalized treatment methods.
The current research offers a comprehensive and effective solution in dynamic cancer control, contributing to the improvement of life quality for cancer patients and potentially serving as a significant step in the development of ad