绳系拖曳飞行器高抗扰轨迹跟踪控制

苏子康; 李春涛; 余跃; 徐忠楠; 王宏伦

doi:10.13700/j.bh.1001-5965.2020.0379

绳系拖曳飞行器高抗扰轨迹跟踪控制

doi: 10.13700/j.bh.1001-5965.2020.0379

1.
南京航空航天大学自动化学院, 南京 210016
2.
北京航天自动控制研究所, 北京 100854
3.
北京航空航天大学自动化科学与电气工程学院, 北京 100083

基金项目:

国家自然科学基金 61903190

航空科学基金 2019ZA052006

江苏省自然科学基金 BK20190401

中央高校基本科研业务费专项资金 NT2020005

详细信息

通讯作者:
苏子康, E-mail: zk_su@nuaa.edu.cn

中图分类号: V249
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- 文章访问数: 455
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- PDF下载量: 205
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-01
- 录用日期: 2020-09-11
- 网络出版日期: 2021-11-20

High anti-disturbance trajectory tracking control for cable towed vehicle

1.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
Beijing Aerospace Automatic Control Institute, Beijing 100854, China
3.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China

Funds:

National Natural Science Foundation of China 61903190

Aeronautical Science Foundation of China 2019ZA052006

Natural Science Foundation of Jiangsu Province of China BK20190401

the Fundamental Research Funds for the Central Universities NT2020005

More Information

Corresponding author: SU Zikang, E-mail: zk_su@nuaa.edu.cn

摘要

摘要:
针对受未知风扰动作用下的绳系拖曳飞行器轨迹精确控制问题，设计了一种基于最小学习参数神经网络估计器的拖曳飞行器轨迹动态面控制方法。首先，结合绳系拖曳系统多刚体动力学模型，构建拖曳飞行器六自由度非线性模型，并完成其仿射非线性化处理。其次，考虑到拖曳飞行器可能受到前方飞机尾涡、紊流和阵风等未知气流及不可测量瞬变缆绳拉力等扰动的综合影响，构建了基于最小学习参数神经网络的拖曳飞行器状态/扰动在线估计器，以准确重构系统不可测量集总扰动。然后，基于所提状态/扰动在线估计器，设计了一种基于最小学习参数神经网络状态/扰动在线估计器的拖曳飞行器轨迹动态面控制方法，并分析了系统稳定性。最后，仿真表明，所提方法能够在多重气流扰动下实现拖曳飞行器位置稳定和机动轨迹跟踪。
- 拖曳飞行器 /
- 绳系拖曳系统 /
- 飞行控制 /
- 轨迹控制 /
- 最小学习参数神经网络 /
- 干扰估计 /
- 动态面控制
Abstract:
To handle the precise trajectory control problem of the cable towed vehicle under unknown airflow disturbances, the minimal learning parameter neural network estimator based dynamic surface trajectory control method is proposed for the towed vehicle. Firstly, combined with the multi-body dynamic model of the cable towed system, the towed vehicle's six-degree-of-freedom nonlinear model is established and then formulated in the affine nonlinear form. Secondly, considering the comprehensive influence on the towed vehicle by the unknown airflow disturbances (such as the trailing vortex, atmospheric turbulence, gust, etc.) and the variably unmeasurable cable tensions, the minimal learning parameter neural network based state/disturbance online estimators are established to accurately reconstitute the unmeasurable lumped disturbance of system. Thirdly, on the basis of the above state/disturbance online estimators, the minimal learning parameter neural network state/disturbance estimator based dynamic surface trajectory control method is proposed. Finally, the simulation results show that the proposed method can achieve the towed vehicle's trajectory stabilization and maneuvering trajectory tracking control.
- towed vehicle /
- cable towed system /
- flight control /
- trajectory control /
- minimal learning parameter neural network /
- disturbance estimation /
- dynamic surface control

HTML全文

图 1 拖曳飞行器建模示意图

Figure 1. Schematic diagram of towed vehicle modeling

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图 2 拖曳飞行器轨迹跟踪控制框图

Figure 2. Block diagram of towed vehicle trajectory tracking control

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图 3 气流扰动

Figure 3. Airflow disturbances

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图 4 情景1中MLPNN干扰估计效果

Figure 4. Estimated disturbance approximations by MLPNN in Case 1

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图 5 轨迹稳定控制结果

Figure 5. Control results of trajectory stabilization

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图 6 无稳定控制器下垂直平面轨迹

Figure 6. Trajectory in vertical plane without stabilizer

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图 7 本文方法控制下垂直平面轨迹

Figure 7. Trajectory in vertical plane controlled by MLPNN-DSC method

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图 8 无稳定控制器下缆绳-拖曳飞行器历史形态

Figure 8. Towed cable-vehicle system's geometry history without stabilizer

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图 9 本文方法控制下缆绳-拖曳飞行器历史形态

Figure 9. Towed cable-vehicle system's geometry history controlled by MLPNN-DSC method

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图 10 轨迹稳定控制角度结果

Figure 10. Control angle results of trajectory stabilization

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图 11 轨迹稳定控制舵面偏量

Figure 11. Control surface deflections of trajectory stabilization

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图 12 情景2中MLPNN干扰估计效果

Figure 12. Estimated disturbance approximations by MLPNN in case 2

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图 13 机动轨迹跟踪结果

Figure 13. Tracking results of maneuvering trajectory

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图 14 机动轨迹跟踪误差

Figure 14. Tracking errors of maneuvering trajectory

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图 15 机动轨迹跟踪曲线

Figure 15. Maneuvering trajectory tracking curves

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图 16 机动轨迹跟踪控制角度结果

Figure 16. Control angle results of maneuvering trajectory tracking

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图 17 机动轨迹跟踪控制舵面偏量

Figure 17. Control surface deflections of maneuvering trajectory tracking

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