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Frontiers of Information Technology & Electronic Engineering

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An improvedSMCbased on fuzzy logic for quadrotorUAVs under unmatched uncertainty

Author(s): Qingmei CAO¹, Ruiwen XIANG¹, Yonghong TAN², Weiqing SUN¹, Jiawei CHI¹, Xiaodong ZHOU^3, ⁴, Lei YAO¹
Affiliation(s): ¹School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; more
Corresponding email(s): cqm@usst.edu.cn, tany@shnu.edu.cn, sunwq@usst.edu.cn, zhouxdshpc@163.com
Key Words: Sliding mode control; Fuzzy logic theory; Underactuated system; Unmanned aerial vehicle; Self-learning strategy

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Qingmei CAO¹, Ruiwen XIANG¹, Yonghong TAN², Weiqing SUN¹, Jiawei CHI¹, Xiaodong ZHOU^3,⁴, Lei YAO¹. An improvedSMCbased on fuzzy logic for quadrotorUAVs under unmatched uncertainty[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2500058

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author="Qingmei CAO¹, Ruiwen XIANG¹, Yonghong TAN², Weiqing SUN¹, Jiawei CHI¹, Xiaodong ZHOU^3,⁴, Lei YAO¹",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2500058"
}

%0 Journal Article
%T An improvedSMCbased on fuzzy logic for quadrotorUAVs under unmatched uncertainty
%A Qingmei CAO¹
%A Ruiwen XIANG¹
%A Yonghong TAN²
%A Weiqing SUN¹
%A Jiawei CHI¹
%A Xiaodong ZHOU^3
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%A Lei YAO¹
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doi="https://doi.org/10.1631/FITEE.2500058"

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A1 - Yonghong TAN²
A1 - Weiqing SUN¹
A1 - Jiawei CHI¹
A1 - Xiaodong ZHOU^{3
A1 -}⁴
A1 - Lei YAO¹
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doi="https://doi.org/10.1631/FITEE.2500058"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: A novel fuzzy sliding mode control (FSMC) strategy is proposed to enhance the robustness and stability of position control for underactuated quadcopter unmanned aerial vehicles (UAVs) in the presence of external disturbances and model uncertainties. To realize the adaptive ability and robustness of the system in complex dynamic environments, an intelligent two-dimensional fuzzy controller is designed based on traditional sliding mode control (SMC) to adjust the SMC parameters in real-time, thereby adapting to the variable structure parameters of the system. First, based on the designed filter variables regarding errors, traditional SMC is used to reduce tracking errors. Then, the fuzzy logic module (FLM) combined with SMC, i.e., the self-learning module (FLM+SMC), is developed based on the filter variables aforementioned and their rate of change to adjust the two parameters of the above SMC. Subsequently, the output signals of the FLM are fed back into the SMC module, and then a closed-loop tuning system using FSMC is developed for the UAVs. Moreover, the stability of FSMC is rigorously verified using Lyapunov theory. Finally, comprehensive simulations demonstrate that the designed FSMC not only offers accurate trajectory precision but also has robustness and disturbance rejection, and comparative experiments using SMC and adaptive radial basis function neural network control (RBFNNC) are used to validate the result.

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