Quaternion-Based Adaptive Backstepping RFWNN Control of Quadrotors Subject to Model Uncertainties and Disturbances

• Published in: International journal of fuzzy systems Vol. 20; no. 6; pp. 1745 - 1755
• Main Authors: Kuo, Chia-Wei, Tsai, Ching-Chih
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2018; Springer Nature B.V
• Subjects: Adaptive control; Algorithms; Approximation; Artificial Intelligence; Closed loops; Computational Intelligence; Control methods; Controllers; Design; Disturbances; Engineering; Fuzzy control; Fuzzy logic; Hovering; Management Science; Neural networks; Operations Research; Quaternions; Rotary wing aircraft; Tracking control; Trajectory control; Uncertainty; Backstepping; Regulation; Trajectory tracking; Quaternion; Recurrent fuzzy wavelet neural network (RFWNN); Quadrotor
• ISSN: 1562-2479; 2199-3211
• DOI: 10.1007/s40815-018-0471-x

This paper presents a quaternion-based adaptive backstepping control method using recurrent fuzzy wavelet neural network (RFWNN) for regulation and trajectory tracking of quadrotors subject to model uncertainties and disturbances. For the controller synthesis, a more complete model of an uncertain quadrotor is first obtained by incorporating with mass variations and wind disturbances, which are online learned by using the RFWNN. Afterward, a quaternion-based adaptive backstepping RFWNN controller is synthesized by integrating backstepping, quaternion control, and the RFWNN online learner. The closed-loop stability of the overall quadrotor control system is shown semi-globally uniformly ultimately bounded via Lyapunov stability theory. The effectiveness and performance of the proposed control method are well exemplified by conducting four simulations on hovering and three-dimensional sinusoidal trajectory tracking control of a quadrotor. Through the simulation results, the proposed control method is shown superior by comparing to two existing methods.