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[1]吕茂隆,孙秀霞,徐光智,等.执行器非线性超低空空投航迹倾角自适应控制[J].电机与控制学报,2017,21(03):111-118.[doi:10.15938/j. emc. 2017.03. 016]
 LU Mao-long,SUN Xiu-xia,XUGuang-zhi,et al. Adaptive controller for ultra-low altitude airdrop flight path angle with actuator nonlinearity[J].,2017,21(03):111-118.[doi:10.15938/j. emc. 2017.03. 016]
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执行器非线性超低空空投航迹倾角自适应控制(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
21
期数:
2017年03
页码:
111-118
栏目:
出版日期:
2018-03-15

文章信息/Info

Title:
 Adaptive controller for ultra-low altitude airdrop flight path angle with actuator nonlinearity
作者:
 吕茂隆孙秀霞徐光智刘树光胡京林
 (1.空军工程大学装备管理与安全工程学院,陕西西安710306;2.中国人民解放军94106部队,陕西西安710613)
Author(s):
 LU Mao-long1 SUN Xiu-xia1 XUGuang-zhi2 LIUShu-guang1 HUJing-lin1
 (1. College of Materiel Management and Safety Engineering Air Force Engineering University Xian 710306,China; 2. Chines People’s Liberation Army 94106,Xi’an 710613, China)
关键词:
超低空空投执行器非线性神经网络自适应控制航迹倾角
Keywords:
ultra-low altitude airdrop actuator nonlinearity neural network adaptive control flight path angle
分类号:
V212.1
DOI:
10.15938/j. emc. 2017.03. 016
文献标志码:
A
摘要:
针对超低空空投下滑阶段执行器非线性、外界不确定性大气扰动以及模型存在未知非线性等因素干扰轨迹精确跟踪问题,提出一种鲁棒自适应神经网络动态面跟踪控制方法。建立了含执行器输入非线性的超低空空投载机纵向非线性模型,采用神经网络逼近模型中未知非线性函数,引入非线性鲁棒补偿项消除了执行器非线性建模误差和外界扰动。应用Lyapunov稳定性理论证明了闭环系统所有信号均是有界收敛的。仿真验证了所提方法既保证了轨迹跟踪的精确性又具有较强的鲁棒性。
Abstract:
For the ultra-low altitude airdrop decline stage,many factors such as actuator nonlinearity the uncertain atmospheric disturbances and model unknown nonlinearity affect the precision of trajectory tracking,a robust adaptive neural network dynamic surface control method was proposed. The ultra-low altitude airdrop longitudinal dynamics with actuator nonlinearity was established,the neural network was used to approximate unknown nonlinear functions of model and a nonlinear robust term was introduced to eliminate the actuator ’ s nonlinear modeling error and external disturbances. From Lyapunov stability the­orem ,it is proved that all tlie signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

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备注/Memo

备注/Memo:
收稿日期:2016 -03 -22
基金项目:国家自然科学基金(60904038);中国博士后科学基金(2014M562629)
作者简介:吕茂隆(1991—),男,博士研究生,研究方向为先进控制理论与应用;
孙秀霞(1962—),女,博士,教授,博士生导师,研究方向为鲁棒控制、自适应控制;
徐光智(1988—),男,博士,研究方向为无人机协同控制;
刘树光(1981—),男,博士,讲师,研究方向为先进控制理论与应用;
胡京林(1991—),男,博士研究生,研究方向为多智能体协同编队控制。
更新日期/Last Update: 2017-04-01