Research on UAV flight attitude control system based on simulink and genetic algorithm

Abstract

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.