Autonomous trajectory tracking of a quadrotor UAV using ANFIS controller based on Gaussian pigeon-inspired optimization

• Published in: CEAS aeronautical journal 2021, Vol.12 (1), p.69-83
• Main Authors: Selma, Boumediene, Chouraqui, Samira, Selma, Belkacem, Abouaïssa, Hassane, Bakir, Toufik
• Format: Article
• Language: English
• Subjects: Adaptive control; Aerospace Technology and Astronautics; Algorithms; Artificial intelligence; Artificial neural networks; Control methods; Controllers; Degrees of freedom; Engineering; Flying platforms; Fuzzy control; Fuzzy logic; Heuristic methods; Nonlinear control; Nonlinear systems; Optimization; Original Paper; Performance evaluation; Proportional integral derivative; Robust control; Tracking errors; Trajectories; Unmanned aerial vehicles; Unmanned helicopters
• ISSN: 1869-5582; 1869-5590
• DOI: 10.1007/s13272-020-00475-6

This paper develops a method to tune neuro-fuzzy controllers using metaheuristic optimization. The main purpose of this approach is that it allows neuro-fuzzy controllers to be tuned to achieve global performance requirements. This paper proposes a robust and intelligent control method based on adaptive neuro-fuzzy inference system (ANFIS) and pigeon-inspired optimization (PIO) algorithm to govern the behavior of a three-degree-of-freedom (3-DOF) quadrotor unmanned aerial vehicle (UAV). 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 UAVs control in order to achieve its mission accurately. The quadrotor is chosen due to its simple mechanical structure; nevertheless, these types of UAVs are highly nonlinear. Intelligent control that uses artificial intelligence 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 2-D 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-PIO, a comparison between the proposed controller, ANFIS and proportional–integral–derivative (PID) controllers is illustrated. The results demonstrate that the proposed controller is more effective compared to the other controllers.