Paper Title
IGA-Optimized PID and H2 Controllers for Thermal Drones: Improving Stability and Efficiency in Emergency Response

Abstract
This study examines the optimization of Proportional-Integral-Derivative (PID) and H₂ controllers for thermal drones using the Iterative Genetic Algorithm (IGA), focusing on enhancing stability and efficiency in emergency response operations. The research addresses critical challenges in drone control systems, particularly concerning thermal imaging quality and flight stability under varying environmental conditions. A comprehensive mathematical model was developed using Newton-Euler dynamics, incorporating thermal camera payload effects and environmental disturbances.The experimental results demonstrated significant improvements in both control strategies. The H₂ controller exhibited superior performance in disturbance rejection, achieving a 25% improvement in stability under varying payloads and a 30% reduction in image blur during flight operations. The PID controller showed 15% better performance in hover conditions. The IGA optimization resulted in substantial improvements across all control parameters: 58.3% in Kp, 66.7% in Ki, and 50% in Kd for the PID controller, while the H₂ controller achieved a 12% reduction in power consumption.Statistical analysis revealed significant enhancements over conventional systems, with a 40% reduction in settling time and 35% improvement in disturbance rejection. The mean position errors were reduced to ±0.6m for the H₂ controller and ±0.8m for the PID controller, meeting stringent requirements for emergency response operations. These improvements were statistically significant (p < 0.05) across all major performance metrics.This research contributes to the field by demonstrating the effectiveness of IGA-optimized controllers in maintaining stable flight conditions for thermal imaging applications while improving energy efficiency. The findings suggest that the optimized H₂ controller is particularly suitable for emergency response applications where environmental conditions are unpredictable. Keywords - Thermal Drones, PID Controller, H₂ controller, Iterative Genetic Algorithm, Emergency Response