基于径向基函数神经网络的无人直升机吊装系统滑模减摆控制

doi:10.3969/j.issn.0255-8297.2021.06.011

Abstract

Abstract: Regarding unmanned helicopter slung-load systems which work in plane motion along with nonlinearity, strong coupling, unknown external bounded disturbance and modeling uncertainty, a sliding mode anti-swing control method based on radial basis function neural networks (RBFNNs) and a disturbance observer is proposed in this paper. Firstly, a system model is constructed in a general affine nonlinear form with its modeling uncertainty approximated by the RBFNNs. Secondly, the nonlinear disturbance observer is used to estimate the compound disturbance containing the approximation error of neural networks and external unknown bounded disturbance. Then a sliding mode anti-swing controller is designed based on RBFNNs and the disturbance observer. Furthermore, the stability of the closed-loop system is proved by using Lyapunov function. Finally, numerical simulations demonstrate the effectiveness of the control strategy.

Key words: unmanned helicopter slung-load system, nonlinear, radial basis function neural networks (RBFNNs), disturbance observer, anti-swing control

CLC Number:

V212.4

LIU Nan, CHEN Mou, WU Qingxian, SHAO Shuyi. Sliding Mode Anti-swing Control for Unmanned Helicopter Slung-Load System Based on RBF Neural Networks[J]. Journal of Applied Sciences, 2021, 39(6): 1006-1020.

References

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Sliding Mode Anti-swing Control for Unmanned Helicopter Slung-Load System Based on RBF Neural Networks

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