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http://dx.doi.org/10.23087/jkicsp.2022.23.4.001

Control of Quadrotor UAV Using Adaptive Sliding Mode with RBFNN  

Han-Ho Tack (Dept. of Convergence Electronic Engineering, Gyeongsang National University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.4, 2022 , pp. 185-193 More about this Journal
Abstract
This paper proposes an adaptive sliding mode control with radial basis function neural network(RBFNN) scheme to enhance the performance of position and attitude tracking control of quadrotor UAV. The RBFNN is utilized on the approximation of nonlinear function in the UAV dynmic model and the weights of the RBFNN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering problems, the sliding mode control term is adjusted by adaptive laws, which can enhance the robust performance of the system. The simulation results of the proposed control method confirm the effectiveness of the proposed controller which applied for a nonlinear quadrotor UAV is presented. Form the results, it's shown that the developed control system is achieved satisfactory control performance and robustness.
Keywords
Quadrotor UAV; Adaptive sliding mode; Approximation error; Chattering problems; Robustness;
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