Controller design in roll, pitch and yaw channels for flying robot using firefly and grey wolf optimization algorithms

This article presents a controller design method for three-channel flight control of a flying robot using firefly and gray wolf optimization algorithms, both capable of handling system model uncertainties. First, the firefly algorithm enhances controller performance by introducing random variations to optimize parameters and approach the global optimum. Then, the gray wolf algorithm is employed to further improve controller efficiency by mimicking the group hunting behavior of wolves to guide parameter tuning. The main innovation of this study is satisfying controller constraints through a limited search interval and generating a random neighborhood within the feasible region. Results show that using a dynamic adaptive factor to adjust the search speed during optimization, the firefly-based controller achieves a better balance between convergence rate and global search ability. This method significantly improves the stability and performance of flying robots and can be effectively applied to controller design in aerial robotic systems.