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基于滑模观测器和非线性动态逆技术的舰载机直接升力控制

甄冲 冯心钰

甄冲,冯心钰. 基于滑模观测器和非线性动态逆技术的舰载机直接升力控制[J]. 北京航空航天大学学报,2026,52(2):507-515 doi: 10.13700/j.bh.1001-5965.2024.0373
引用本文: 甄冲,冯心钰. 基于滑模观测器和非线性动态逆技术的舰载机直接升力控制[J]. 北京航空航天大学学报,2026,52(2):507-515 doi: 10.13700/j.bh.1001-5965.2024.0373
ZHEN C,FENG X Y. Carrier-based aircraft direct lift control based on sliding mode observer and non-linear dynamic inversion technology[J]. Journal of Beijing University of Aeronautics and Astronautics,2026,52(2):507-515 (in Chinese) doi: 10.13700/j.bh.1001-5965.2024.0373
Citation: ZHEN C,FENG X Y. Carrier-based aircraft direct lift control based on sliding mode observer and non-linear dynamic inversion technology[J]. Journal of Beijing University of Aeronautics and Astronautics,2026,52(2):507-515 (in Chinese) doi: 10.13700/j.bh.1001-5965.2024.0373

基于滑模观测器和非线性动态逆技术的舰载机直接升力控制

doi: 10.13700/j.bh.1001-5965.2024.0373
详细信息
    通讯作者:

    E-mail:434455033@qq.com

  • 中图分类号: V249.1

Carrier-based aircraft direct lift control based on sliding mode observer and non-linear dynamic inversion technology

More Information
  • 摘要:

    舰载机是航母战斗群的重要组成部分,在实际运用中有众多技术难点,而着舰技术是其中关键难点之一。针对舰载机系统的多变量耦合特点和着舰过程中环境复杂多变等问题,提出一种基于滑模观测器和非线性动态逆技术的舰载机直接升力控制系统。为补偿舰尾流对控制精度的影响,设计一种自适应滑模观测器,可以有效估计外界干扰对舰载机运动造成的影响。为实现控制输入之间的解耦,利用非线性动态逆控制技术,建立舰载机的直接升力控制系统,并提出自调节鸽群优化(SAPIO)算法用于整定控制系统参数,提升控制精度。仿真对比实验表明:所提舰载机直接升力控制系统相比传统比例-积分-微分控制系统着舰精度更高。

     

  • 图 1  舰载机坐标系示意图

    Figure 1.  Schematic diagram of carrier-based aircraft coordinate system

    图 2  舰载机直接升力控制系统示意图

    Figure 2.  Schematic diagram of carrier-based aircraft direct lift control system

    图 3  航迹倾斜角干扰观测结果

    Figure 3.  Track inclination angle interferes with observation results

    图 4  速度干扰观测结果

    Figure 4.  Velocity interference observational results

    图 5  角速度干扰观测结果

    Figure 5.  Angular velocity interference observational results

    图 6  不同算法优化结果

    Figure 6.  Optimization results of different algorithms

    图 7  不同方法下滑道跟踪误差曲线

    Figure 7.  Glide path tracking error curves of different methods

    表  1  不同算法的参数

    Table  1.   Parameters of different algorithms

    参数 数值
    GA[27]交叉概率Pc 0.9
    GA[27]变异概率Pm 0.1
    PSO[28]惯性值w 0.8
    PSO[28]自我学习因子c1 1.3
    PSO[28]社会学习因子c2 1.5
    PIO[20]/SAPIO地图与指南针因数R 0.02
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-06-04
  • 录用日期:  2024-07-12
  • 网络出版日期:  2025-01-20
  • 整期出版日期:  2026-02-28

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