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基于自适应RBF神经网络的超空泡航行体反演控制

李洋 刘明雍 张小件

李洋, 刘明雍, 张小件. 基于自适应RBF神经网络的超空泡航行体反演控制. 自动化学报, 2020, 46(4): 734-743. doi: 10.16383/j.aas.2018.c170387
引用本文: 李洋, 刘明雍, 张小件. 基于自适应RBF神经网络的超空泡航行体反演控制. 自动化学报, 2020, 46(4): 734-743. doi: 10.16383/j.aas.2018.c170387
LI Yang, LIU Ming-Yong, ZHANG Xiao-Jian. Adaptive RBF Neural Network Based Backsteppting Control for Supercavitating Vehicles. ACTA AUTOMATICA SINICA, 2020, 46(4): 734-743. doi: 10.16383/j.aas.2018.c170387
Citation: LI Yang, LIU Ming-Yong, ZHANG Xiao-Jian. Adaptive RBF Neural Network Based Backsteppting Control for Supercavitating Vehicles. ACTA AUTOMATICA SINICA, 2020, 46(4): 734-743. doi: 10.16383/j.aas.2018.c170387

基于自适应RBF神经网络的超空泡航行体反演控制

doi: 10.16383/j.aas.2018.c170387
基金项目: 

国家自然科学基金 51379176

国家自然科学基金 61473233

详细信息
    作者简介:

    刘明雍  西北工业大学教授.主要研究方向为群集控制, 地磁仿生导航, 水下航行体导航, 制导与控制. E-mail: liumingyong@nwpu.edu.cn

    张小件   西北工业大学航海学院博士研究生.主要研究方向为水下航行体制导律设计. E-mail:xiaojiansin@mail.nwpu.edu.cn

    通讯作者:

    李洋   西北工业大学航海学院博士研究生.主要研究方向为水下超空泡航行体导航与控制.本文通信作者.E-mail: liyang_116@yeah.net

  • 本文责任编委 倪茂林

Adaptive RBF Neural Network Based Backsteppting Control for Supercavitating Vehicles

Funds: 

National Natural Science Foundation of China 51379176

National Natural Science Foundation of China 61473233

More Information
    Author Bio:

    LIU Ming-Yong   Professor at the School of Marine Science and Technology, Northwestern Polytechnical University. His research interest covers control of flocking system, bio-inspired geomagnetic navigation and navigation guidance and control of underwater vehicle

    ZHANG Xiao-Jian   Ph.D. candidate at the School of Marine Science and Technology, Northwestern Polytechnical University. His research interest covers guidance and control of underwater vehicle

    Corresponding author: LI Yang   Ph.D. candidate at the School of Marine Science and Technology, Northwestern Polytechnical University. His research interest covers navigation and control of underwater supercavitating vehicle. Corresponding author of this paper
  • Recommended by Associate Editor NI Mao-Lin
  • 摘要: 针对超空泡航行体姿轨控制普遍存在的模型不确定性问题进行相关研究.为此, 首先对其动力学特性进行分析, 并建立了超空泡航行体的动力学名义模型, 随后将其改写为不确定反馈系统, 然后利用反演控制方法设计超空泡航行体姿轨控制器, 针对模型中的未知函数利用径向基函数(Radial basis function, RBF)神经网络进行逼近并补偿, 由基于Lyapunov稳定理论设计的自适应方法计算神经网络的权重, 并给出稳定性证明.仿真研究验证了控制器设计的有效性.
    Recommended by Associate Editor NI Mao-Lin
    1)  本文责任编委 倪茂林
  • 图  1  非全包裹超空泡航行体模式

    Fig.  1  Incomplete-encapsulated supercavitating vehicle

    图  2  坐标系

    Fig.  2  The reference frame

    图  3  航行体受力分析

    Fig.  3  The vehicle force analysis

    图  4  不同空化数下的空泡轮廓

    Fig.  4  Cavity profiles according to the varying cavitation numbers

    图  5  空化器示意图

    Fig.  5  Cavitator

    图  6  沾湿尾部受力

    Fig.  6  Forces acting on the wetted body

    图  7  深度$z$及俯仰角$\theta$设定信号与实际跟踪响应

    Fig.  7  Desired trajectory and actual trajectory of $z$ and $\theta$

    图  8  纵向速度$w$及俯仰角速度$q$状态响应

    Fig.  8  State responses of $w$ and $q$

    图  9  控制输入响应

    Fig.  9  Control inputs response

    图  10  深度$z$及俯仰角$\theta$设定轨迹与实际跟踪响应

    Fig.  10  Desired trajectories and actual trajectories of $z$ and $\theta$

    图  11  纵向速度$w$及俯仰角速度$q$状态响应

    Fig.  11  Longitudinal velocity $w$ and Pitching angular velocity $q$ responses

    图  12  控制输入响应

    Fig.  12  Control inputs response

    图  13  $f_1$, $f_2$与其估计值

    Fig.  13  $f_1$, $f_2$ and its estimation

    图  14  $g_1$, $g_2$与其估计值

    Fig.  14  $g_1$, $g_2$ and its estimation

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出版历程
  • 收稿日期:  2017-07-12
  • 录用日期:  2017-12-06
  • 刊出日期:  2020-04-24

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