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应用科学学报 >

2021 , Vol. 39 >Issue 6: 1006 - 1020

DOI: https://doi.org/10.3969/j.issn.0255-8297.2021.06.011

控制与系统

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

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  • 南京航空航天大学 自动化学院, 江苏 南京 211106

收稿日期: 2020-06-03

  网络出版日期: 2021-12-04

基金资助

装备预研中国电科联合基金（No.6141B08231110a）；江苏省自然科学基金项目（No.BK20171417）；国家自然科学基金应急管理项目（No.61751219）；江苏省“333高层次人才培养工程”科研项目（No.BRA2019051）资助

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

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  • College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Received date: 2020-06-03

  Online published: 2021-12-04

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摘要

针对存在非线性、强耦合、外部未知有界干扰和建模不确定性的平面运动下无人直升机吊装系统,研究了一种基于径向基函数神经网络(radial basis function neural networks,RBFNNs)和干扰观测器的无人直升机吊装系统滑模减摆控制方法。首先将系统模型转换成仿射非线性形式,利用RBFNNs逼近系统不确定性,设计干扰观测器估计神经网络逼近误差与外界未知有界干扰的复合值。然后基于RBFNNs和干扰观测器设计了滑模减摆控制器,并用Lyapunov方法证明闭环系统稳定性;最后通过仿真验证了所设计控制器的有效性。

关键词： 无人直升机吊装系统; 非线性; 径向基函数神经网络; 干扰观测器; 减摆控制

本文引用格式

刘楠, 陈谋, 吴庆宪, 邵书义 . 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[J]. 应用科学学报, 2021 , 39(6) : 1006 -1020 . DOI: 10.3969/j.issn.0255-8297.2021.06.011

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

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