International Journal of Fuzzy Systems

, Volume 20, Issue 6, pp 1745–1755 | Cite as

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

  • Chia-Wei Kuo
  • Ching-Chih Tsai


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.


Backstepping Quaternion Quadrotor Recurrent fuzzy wavelet neural network (RFWNN) Regulation Trajectory tracking 



The authors deeply acknowledge finance support from Ministry of Science and Technology (MOST), Taiwan, ROC, under contract MOST 104-2221-E-005-054 -MY2.


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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Chung Hsing UniversityTaichungTaiwan, ROC

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