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

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 q...

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Published inInternational journal of fuzzy systems Vol. 20; no. 6; pp. 1745 - 1755
Main Authors Kuo, Chia-Wei, Tsai, Ching-Chih
Format Journal Article
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Tsai, Ching-Chih
Kuo, Chia-Wei
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  fullname: Tsai, Ching-Chih
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  organization: Department of Electrical Engineering, National Chung Hsing University
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Cites_doi 10.1109/TCST.2011.2121907
10.1109/ECMR.2015.7324191
10.1016/j.mechatronics.2010.04.009
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10.1109/ACC.2013.6579970
10.1109/URAI.2017.7992893
10.1109/EIConRus.2017.7910700
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10.1109/TMECH.2014.2329945
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10.1109/ACC.2013.6579806
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Issue 6
Keywords Backstepping
Regulation
Trajectory tracking
Quaternion
Recurrent fuzzy wavelet neural network (RFWNN)
Quadrotor
Language English
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– reference: Zhao, S., Dong, W., Farrell, J.A.: Quaternion-based trajectory tracking control of VTOL-UAVs using command filtered backstepping. In: Proceedings of 2013 American Control Conference, pp. 1018–1023 (2013)
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Snippet This paper presents a quaternion-based adaptive backstepping control method using recurrent fuzzy wavelet neural network (RFWNN) for regulation and trajectory...
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SubjectTerms 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
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Title Quaternion-Based Adaptive Backstepping RFWNN Control of Quadrotors Subject to Model Uncertainties and Disturbances
URI https://link.springer.com/article/10.1007/s40815-018-0471-x
https://www.proquest.com/docview/2932419687
Volume 20
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