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• W2944532051 abstract "Abstract In this paper, a novel method is suggested for the position and attitude tracking control of a quadrotor UAV in the presence of parametric uncertainties and external disturbance. The proposed method combines neural network adaptive scheme with sliding mode control, which preserves the advantages of the two methods. Firstly, dynamic model of quadrotor is divided into two fully actuated and under actuated subsystems. Secondly, sliding mode controllers are corresponding designed for each subsystem, and their coefficients in sliding manifolds are adaptively tuned by the neural network method. In each section, using Lyapunov theory, stability of closed loop system is proven. Finally, the method is examined for a square path tracking and a maximum overshoot of 7.5133% and a settling time 5.6648 s are obtained. By comparing the results obtained through different methods, it is concluded that the proposed controller provides the following main advantages: (1) good transient and steady state behaviors, (2) insensitivity to parameter variations, (3) disturbance rejection capability, and (4) remarkable stability and performance robustness. Hence, for operational purposes in which the fast and accurate response are of crucial importance, using the neural network-based adaptive sliding mode control approach is recommended." @default.
• W2944532051 created "2019-05-16" @default.
• W2944532051 creator A5018561971 @default.
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• W2944532051 date "2019-08-01" @default.
• W2944532051 modified "2023-10-10" @default.
• W2944532051 title "Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV" @default.
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• W2944532051 doi "https://doi.org/10.1016/j.ast.2019.04.055" @default.
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