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基于RBF神经网络补偿的ROV运动控制算法

张帅军 刘卫东 李乐 柳靖彬 郭利伟 徐景明

张帅军, 刘卫东, 李乐, 等. 基于RBF神经网络补偿的ROV运动控制算法[J]. 水下无人系统学报, 2024, 32(2): 311-319 doi: 10.11993/j.issn.2096-3920.2023-0033
引用本文: 张帅军, 刘卫东, 李乐, 等. 基于RBF神经网络补偿的ROV运动控制算法[J]. 水下无人系统学报, 2024, 32(2): 311-319 doi: 10.11993/j.issn.2096-3920.2023-0033
ZHANG Shuaijun, LIU Weidong, LI Le, LIU Jingbin, GUO Liwei, XU Jingming. ROV Motion Control Algorithm Based on RBF Neural Network Compensation[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 311-319. doi: 10.11993/j.issn.2096-3920.2023-0033
Citation: ZHANG Shuaijun, LIU Weidong, LI Le, LIU Jingbin, GUO Liwei, XU Jingming. ROV Motion Control Algorithm Based on RBF Neural Network Compensation[J]. Journal of Unmanned Undersea Systems, 2024, 32(2): 311-319. doi: 10.11993/j.issn.2096-3920.2023-0033

基于RBF神经网络补偿的ROV运动控制算法

doi: 10.11993/j.issn.2096-3920.2023-0033
基金项目: 国家自然科学基金(61903304); 中央高校基本科研业务费项目(3102020HHZY030010); “111”引智计划项目(B18041.0).
详细信息
    作者简介:

    张帅军(1999-), 男, 在读硕士, 主要研究方向为水下航行器运动控制

  • 中图分类号: TJ630.33; U675.91

ROV Motion Control Algorithm Based on RBF Neural Network Compensation

  • 摘要: 针对作业型遥控水下航行器(ROV)在模型参数不确定和外部环境干扰下的运动控制问题, 提出了一种基于径向基函数(RBF)神经网络的自适应双环滑模控制策略。首先, 对于ROV外环位置控制采用改进趋近律的积分滑模控制方法, 对于ROV内环速度控制采用指数趋近律的积分滑模控制方法; 其次, 为进一步改善滑模控制的抖振问题, 引入双曲正切函数作为滑模切换项; 然后, 利用RBF神经网络控制技术对ROV模型的不确定参数和外部扰动进行估计与补偿; 最后, 利用李雅普诺夫稳定性理论证明了整个闭环系统的稳定性, 并对作业型ROV的运动控制进行了数值仿真。仿真结果验证了所设计的控制器可以实现ROV航行的精确控制, 并能够有效抑制模型不确定参数和外部扰动对ROV运动的影响。

     

  • 图  1  ROV控制算法框图

    Figure  1.  Block diagram of ROV control algorithm

    图  2  RBF神经网络结构

    Figure  2.  Structure of RBF neural network

    图  3  ROV位置和航向跟踪曲线

    Figure  3.  Curves of ROV position and heading tracking

    图  4  ROV速度跟踪曲线

    Figure  4.  Curves of ROV speed tracking

    图  5  扰动实际值和估计值曲线

    Figure  5.  Curves of disturbance actual values and estimated values

    图  6  RBF-DISMC控制方法下推进器推力曲线图

    Figure  6.  Thrust curve of thruster under RBF-DISMC method

    图  7  CSMC控制方法下推进器推力曲线图

    Figure  7.  Thrust Curve of Thruster under CSMC Control Method

    表  1  控制算法仿真性能比较

    Table  1.   Comparison of simulation performance of control algorithms

    t′
    性能参数CSMCDSMCRBF-DISMC
    $ {t_x} $/s86.928.228.0
    $ {t_y} $/s67.651.147.9
    $ {t_z} $/s119.457.649.0
    $ {t_\psi } $/s47.836.029.6
    $ {{{M}}_{{\text{MAE}}}} $
    $ {x_m} $/m0.108 60.071 20.057 6
    $ {y_m} $/m0.075 50.122 60.054 1
    $ {z_m} $/m0.217 20.186 00.163 5
    $ {\psi _m} $/(°)$0.003\;2$$0.038\;0$$0.002\;7$
    $ {R_{{\text{RMSE}}}} $
    $ {x_r} $/m0.225 90.172 60.162 8
    $ {y_r} $/m0.133 80.170 30.115 4
    $ {z_r} $/m0.404 10.353 60.350 6
    $ {\psi _r} $/(°)$0.006\;7$$0.042\;3$$0.006\;6$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-06
  • 修回日期:  2023-05-17
  • 录用日期:  2023-06-14
  • 网络出版日期:  2024-01-12

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