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

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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.

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Acknowledgements

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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  1. Department of Electrical Engineering, National Chung Hsing University, 250, Kuo-Kuang Road, Taichung, 40227, Taiwan, ROC

    Chia-Wei Kuo & Ching-Chih Tsai

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  1. Chia-Wei Kuo
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Correspondence to Ching-Chih Tsai.

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Kuo, CW., Tsai, CC. Quaternion-Based Adaptive Backstepping RFWNN Control of Quadrotors Subject to Model Uncertainties and Disturbances. Int. J. Fuzzy Syst. 20, 1745–1755 (2018). https://doi.org/10.1007/s40815-018-0471-x

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  • DOI: https://doi.org/10.1007/s40815-018-0471-x

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