التحكم في انظمة الطائرات بدون طيار باستخدام الخوارزميات الهجين بين PID والذكاء الاصطناعي

This study aims to design and evaluate an intelligent control system for unmanned aerial vehicles (UAVs) that combines a traditional PID controller with artificial intelligence techniques, specifically neural networks and genetic algorithms. The importance of this hybrid system lies in its enhanced ability to improve the performance and stability of flight systems under complex and changing operating conditions. This makes it more effective in dealing with turbulence and noise compared to traditional systems. The development of a simulation model using the MATLAB/Simulink environment has shown promising results, with significant improvements in performance indicators, including temporal stability, reduced error rate, and increased accuracy in tracking flight paths. However, the applicability of this hybrid system in realistic and sensitive scenarios requires careful consideration of several practical challenges. Prominent among these challenges are the high computational cost that AI components may impose, the need to ensure real-time response to maintain aircraft performance, and its applicability to UAVs with limited resources (in terms of power and processing). These factors are crucial for assessing the effectiveness and feasibility of the system in real-world operational environments. Based on the positive simulation results, the study recommends adopting this methodology in future drone operational systems, emphasizing the need for continuous research and development to overcome the aforementioned practical challenges to ensure their successful deployment in AI-monitored security environments. Keywords: Hybrid control, Unmanned Aerial Vehicle (UAV), PID control, Artificial Intelligence, Neural Networks, Genetic Algorithms, MATLAB/Simulink.