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• W2062256672 abstract "This paper proposes an adaptive neural network control with neural state’s observer for quadrotor. The adaptive approach is used to solve the dynamics uncertainty problem of the controller. To perform the control, a Single Hidden Layer Neural Network (SHLNN) is used. Based on the structure of Sliding Mode Observer (SMO), a new neural observer is proposed to estimate the states. The aim of this work is to propose an observer insensitive to the measurement noise. The stability proof of global system is made by Lyapunov direct method. The adaptation laws of both artificial neural networks (ANNs) are derived from Lyapunov theory. The proposed controller is validated by simulation on the quadrotor under measurement noise conditions. A comparative study with SMO is made to highlight the performances of the proposed neural observer." @default.
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• W2062256672 date "2014-07-23" @default.
• W2062256672 modified "2023-10-18" @default.
• W2062256672 title "Adaptive neural network control based on neural observer for quadrotor unmanned aerial vehicle" @default.
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• W2062256672 doi "https://doi.org/10.1080/01691864.2014.913498" @default.
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