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输入饱和下AUV自适应神经网络预设性能控制

徐文峰 刘加朋 于金鹏 韩亚宁

徐文峰, 刘加朋, 于金鹏, 等. 输入饱和下AUV自适应神经网络预设性能控制[J]. 水下无人系统学报, 2024, 32(2): 376-382 doi: 10.11993/j.issn.2096-3920.2023-0041
引用本文: 徐文峰, 刘加朋, 于金鹏, 等. 输入饱和下AUV自适应神经网络预设性能控制[J]. 水下无人系统学报, 2024, 32(2): 376-382 doi: 10.11993/j.issn.2096-3920.2023-0041
XU Wenfeng, LIU Jiapeng, YU Jinpeng, HAN Yaning. Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 376-382. doi: 10.11993/j.issn.2096-3920.2023-0041
Citation: XU Wenfeng, LIU Jiapeng, YU Jinpeng, HAN Yaning. Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 376-382. doi: 10.11993/j.issn.2096-3920.2023-0041

输入饱和下AUV自适应神经网络预设性能控制

doi: 10.11993/j.issn.2096-3920.2023-0041
基金项目: 山东省自然科学基金资助项目(ZR2020QF063).
详细信息
    作者简介:

    徐文峰(1998-), 男, 在读硕士, 主要研究方向为水下无人机跟踪控制

  • 中图分类号: TJ630.33; U674

Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation

  • 摘要: 针对自主水下航行器(AUV)系统不确定性及输入饱和问题, 提出了一种改进的自适应神经网络预设性能控制策略, 完成对期望轨迹的跟踪。首先, 引入非线性变换, 使位置误差始终处在预设时变范围内, 提高了控制精度, 并基于反步法和Lyapunov函数设计系统虚拟控制律; 然后, 利用神经网络技术处理系统模型未知参数, 并重构系统真实控制律, 使传统反步控制策略得以简化, 有效降低了计算复杂度, 并在Lyapunov稳定性理论的基础上, 证明了AUV系统的误差信号均有界; 最后, 与传统动态面控制方法进行对比, 仿真结果表明所提出的控制策略控制性能更好, 在考虑输入饱和情况下可有效克服不确定性对系统性能的影响, 实现对目标轨迹的有效跟踪。

     

  • 图  1  AUV坐标系定义

    Figure  1.  Coordinate system definition of an AUV

    图  2  AUV位置误差曲线

    Figure  2.  Position error curves of the AUV

    图  3  AUV在xoy平面运行轨迹

    Figure  3.  The AUV running trajectory in the xoy plane

    图  4  AUV位置跟踪曲线

    Figure  4.  Position tracking curves of the AUV

    图  5  AUV执行器力矩曲线

    Figure  5.  Actuator torque curves of the AUV

    图  6  20%模型误差下AUV在xoy平面运行轨迹

    Figure  6.  The AUV running trajectory in the xoy plane at 20% model errors

    图  7  20%模型误差下AUV位置跟踪曲线

    Figure  7.  Position tracking curves of the AUV at 20% model errors

    图  8  20%模型误差下AUV执行器力矩曲线

    Figure  8.  Actuator torque curves of the AUV at 20% model errors

    表  1  模型参数

    Table  1.   Model parameters

    参数数值参数数值
    $ {m / {{\text{kg}}}} $116.0$ {{{N_r}} / {({{{\text{kg}} \cdot {\text{m}}} \mathord{\left/ {\vphantom {{{\text{kg}} \cdot {\text{m}}} {\text{s}}}} \right. } {\text{s}}})}} $3.5
    $ {{{I_z}} / {({\text{kg}} \cdot {{\text{m}}^2})}} $13.1$ {{{D_u}} / {({{{\text{kg}}} \mathord{\left/ {\vphantom {{{\text{kg}}} {\text{m}}}} \right. } {\text{m}}})}} $241.3
    $ {{{X_{\dot u}}} / {{\text{kg}}}} $−167.6$ {{{D_v}} / {({{{\text{kg}}} \mathord{\left/ {\vphantom {{{\text{kg}}} {\text{m}}}} \right. } {\text{m}}})}} $503.8
    $ {{{Y_{\dot v}}} / {{\text{kg}}}} $−477.2$ {{{D_r}} / {({{{\text{kg}} \cdot {\text{m}}} \mathord{\left/ {\vphantom {{{\text{kg}} \cdot {\text{m}}} {\text{s}}}} \right. } {\text{s}}})}} $76.9
    $ {{{N_{\dot r}}} / {{\text{kg}}}} $−15.9$ {{{M_{\dot u}}} / {{\rm{kg}}}} $283.6
    $ {{{X_u}} / {({{{\text{kg}}} \mathord{\left/ {\vphantom {{{\text{kg}}} {\text{s}}}} \right. } {\text{s}}})}} $26.9$ {{{M_{\dot v}}} / {{\rm{kg}}}} $593.2
    $ {Y}_{v}/(\text{kg}/\text{s}) $35.8$ {{{M_{\dot r}}} /{{\rm{kg}}}} $29.0
    下载: 导出CSV

    表  2  各方向均方误差对比

    Table  2.   Comparison of the meansquare error in different directions

    参数 xmse/m2 ymse/m2 $ {\psi_{\rm{mse}}} $/rad2
    文中方法 0.000 3 0.000 1 0.021 4
    动态面控制 0.005 1 0.002 0 0.082 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-18
  • 修回日期:  2023-05-21
  • 录用日期:  2023-07-12
  • 网络出版日期:  2023-12-13

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