Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31007
Title: A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor
Authors: He, Y
Xiao, L
Wang, Z
Zuo, Q
Li, L
Keywords: fuzzy neural network;nonsingular sliding mode control (SMC);predefined-time convergence;quadrotor;Takagi–Sugeno fuzzy logic system (TSFLS);zeroing neural network (ZNN)
Issue Date: 2-Dec-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: He, Y. et al. (2024) 'A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor', IEEE Transactions on Fuzzy Systems, 32 (12), pp. 6775 - 6788. doi: 10.1109/TFUZZ.2024.3464564.
Abstract: In this article, a novel adaptive robust predefined-time nonsingular sliding mode control (ARPTNSMC) scheme is investigated, which aims to achieve fast and accurate tracking control of a quadrotor subjected to external disturbance. Inspiration is drawn from a fuzzy neural network that is constructed by fuzzy logic and zeroing neural network (ZNN). Distinct from most sliding mode control approaches, two nonsingular sliding mode surfaces are formulated by employing general ZNN approaches and differentiable predefined-time activation functions. Furthermore, for the compensation of external disturbance, a dynamic adaptive parameter and a fuzzy adaptive parameter are designed in the attitude control law. The fuzzy adaptive parameter, generated by the Takagi–Sugeno fuzzy logic system, is incorporated to enhance the robustness while reducing the chattering phenomena resulting from the discontinuous sign function. Theoretical proofs are provided to demonstrate the predefined-time convergence and robustness of the closed-loop system. Finally, two trajectory tracking examples are offered to validate the convergence, robustness, and low-chattering characteristics of the closed-loop system under the developed ARPTNSMC scheme.
URI: https://bura.brunel.ac.uk/handle/2438/31007
DOI: https://doi.org/10.1109/TFUZZ.2024.3464564
ISSN: 1063-6706
Other Identifiers: ORCiD: Yongjun He https://orcid.org/0000-0002-5228-0302
ORCiD: Lin Xiao https://orcid.org/0000-0003-3172-3490
ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401
ORCiD: Qiuyue Zuo https://orcid.org/0000-0001-7385-0792
ORCiD: Linju Li https://orcid.org/0009-0006-0410-9603
Appears in Collections:Dept of Computer Science Research Papers

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