Design and Implementation of an Intelligent Adaptive Control System for Optimizing Radiotherapy Dosage Based on Patients’ Biological Feedback

Abstract

Radiotherapy remains one of the most effective cancer treatment methods, yet it faces the persistent challenge of precisely determining the optimal radiation dose. Overexposure leads to damage of healthy tissues, while underexposure reduces therapeutic efficacy. This study aims to design and implement an intelligent adaptive control system capable of real-time optimization of radiation dosage based on patients’ biological feedback. Real patient data from SEER and IAEA databases were utilized, focusing on breast and prostate cancer cases. The control structure employed is a Model Reference Adaptive Control (MRAC) combined with a fuzzy logic optimization algorithm that dynamically adjusts parameters under nonlinear physiological conditions. In the experimental setup, biological signals such as tissue temperature, blood pressure, and oxygen saturation during irradiation were captured as system feedback inputs. The findings demonstrated that compared to conventional PID control, the proposed method achieved 23% reduction in radiation fluctuation and 18% improvement in treatment uniformity. This system can serve as an advanced clinical tool to enhance accuracy, reduce side effects, and improve personalized cancer therapy. The integration of intelligent adaptive control with biomedical technologies marks a significant step toward the realization of precision medicine.