ANFIS controller design based on pigeon-inspired optimization to control an UAV trajectory tracking task

Unmanned aerial vehicles (UAVs) are flying platforms that have become increasingly used in a wide range of applications. However, the most recent research has aimed to improve the quality of UAV control to achieve its mission accurately. This paper proposes a robust and intelligent control method based on adaptive neuro-fuzzy inference system (ANFIS), and pigeon-inspired optimization algorithm (PIO) to govern the behavior of a three-degree of freedom (3-DOF) quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of UAVs are highly nonlinear. Intelligent control that uses artificial intelligence computing approach such as fuzzy logic is a suitable choice to better control nonlinear systems. The ANFIS controller is proposed to control the movement of UAV to track a given reference trajectory in 2D vertical plane. The PIO is used to obtain the ANFIS optimal parameters with the aim of improving the quality of the controller and therefore, to minimize tracking error. To evaluate the performance of the ANFIS controller tuned by PIO, a comparison between the proposed ANFIS-PIO, ANFIS and proportional–integral–derivative controllers is illustrated, and comparative results demonstrate that the proposed controller is more effective.