Hybrid PID-fuzzy based adaptive sensor switching strategy for intelligent radiation monitoring systems

Radiation monitoring systems require high sensitivity, stability, and adaptability under dynamically changing environmental conditions. Conventional fixed-sensitivity sensor architectures often suffer from reduced accuracy under high radiation levels, increased noise effects at low radiation levels, and unnecessary energy consumption due to continuous high-sensitivity operation. This study proposes a novel hybrid adaptive control framework that integrates Proportional-Integral-Derivative (PID) control, Sugeno-type fuzzy logic inference, and threshold-based sensor switching within a unified architecture. The proposed method dynamically adjusts sensor sensitivity by evaluating both radiation intensity and PID error behavior. A mathematical control model defined as u(t) = alpha uPID(t)+beta uFLC(t)+gamma R(t)/Rmax is implemented to generate a continuous adaptive control signal for real-time sensor switching. Simulation results under four distinct operating conditions demonstrate that radiation intensity provides the dominant contribution for sensitivity transition, while PID error improves transient regulation and system response. A threshold-based switching mechanism (u(t)>uth = 4) effectively separates low- and high-sensitivity operating regions without causing instability. The results confirm that the proposed hybrid architecture improves responsiveness, robustness, and operational efficiency compared with fixed-sensitivity monitoring approaches. The main contribution of this study is the integration of continuous PID-fuzzy control regulation with discrete sensor selection in a single adaptive framework, providing a scalable solution for intelligent radiation sensing applications and next-generation monitoring systems.