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基于ESO的水下机器人机械臂系统鲁棒模型预测控制

王红都 高枫 黎明 付东飞

王红都, 高枫, 黎明, 等. 基于ESO的水下机器人机械臂系统鲁棒模型预测控制[J]. 水下无人系统学报, 2023, 31(6): 827-838 doi: 10.11993/j.issn.2096-3920.2022-0074
引用本文: 王红都, 高枫, 黎明, 等. 基于ESO的水下机器人机械臂系统鲁棒模型预测控制[J]. 水下无人系统学报, 2023, 31(6): 827-838 doi: 10.11993/j.issn.2096-3920.2022-0074
WANG Hongdu, GAO Feng, LI Ming, FU Dongfei. ESO-Based Robust Model Predictive Control for Undersea Vehicle Manipulator System[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 827-838. doi: 10.11993/j.issn.2096-3920.2022-0074
Citation: WANG Hongdu, GAO Feng, LI Ming, FU Dongfei. ESO-Based Robust Model Predictive Control for Undersea Vehicle Manipulator System[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 827-838. doi: 10.11993/j.issn.2096-3920.2022-0074

基于ESO的水下机器人机械臂系统鲁棒模型预测控制

doi: 10.11993/j.issn.2096-3920.2022-0074
基金项目: 山东省自然科学基金(ZR2021MF119); 河南省水下智能装备重点实验室开放基金.
详细信息
    作者简介:

    王红都(1984-), 男, 博士, 副教授, 主要研究方向为智能控制、抗干扰控制及海洋运动体控制

  • 中图分类号: TJ630.33; U664.82

ESO-Based Robust Model Predictive Control for Undersea Vehicle Manipulator System

  • 摘要: 考虑到海洋环境的复杂性、不确定性及水下机器人机械臂系统(UVMS)的强非线性、强耦合性等特点, 提出一种基于扩张状态观测器(ESO)的鲁棒模型预测控制(RMPC)方法。首先基于UVMS的动力学特性, 建立其动力学模型, 并忽略不确定项和干扰给出其名义模型系统。然后, 基于名义系统设计了RMPC算法。将原系统的不确定项、干扰以及建模误差等影响因素集总为扩张状态, 设计了ESO对其进行估计, 并在名义模型的RMPC基础上进行了补偿, 以得到应用于UVMS系统的RMPC方法。最后通过仿真实验证明, 基于ESO的RMPC具有很好的轨迹跟踪性能和抗扰动能力。

     

  • 图  1  水下机器人机械臂系统结构图

    Figure  1.  Structure of UVMS

    图  2  鲁棒模型预测控制算法流程

    Figure  2.  Robust model predictive control algorithm flow

    图  3  工况1下UVMS本体三维跟踪情况

    Figure  3.  The 3D tracking situation of UVMS in case 1

    图  4  工况1下轨迹跟踪情况

    Figure  4.  UVMS tracking situation for case 1

    图  5  工况1下ESO干扰估计情况

    Figure  5.  The situation of disturbance by ESO in case 1

    图  6  工况2下的UVMS本体三维跟踪情况

    Figure  6.  The 3D tracking situation of UVMS in case 2

    图  7  工况2下UVMS跟踪情况

    Figure  7.  UVMS tracking situation for case 2

    图  8  工况2下ESO干扰估计情况

    Figure  8.  The situation of disturbance by ESO in case 2

    表  1  工况1下各控制器的跟踪误差RMS指标

    Table  1.   The tracking error of each controller in case 1

    RMS${x_e}$/m${y_e}$/m${z_e}$/m${\psi _e}$/rad${\theta _{e1}}$/rad${\theta _{e2}}$/rad
    RMPCESO0.001 10.002 20.000 20.008 10.004 80.009 1
    NTSMC0.001 20.010 90.001 30.060 60.033 90.015 9
    PID0.062 10.029 90.080 40.040 80.021 70.049 2
    下载: 导出CSV

    表  2  工况2下各控制器跟踪误差RMS指标

    Table  2.   The tracking error of each controller in case 2

    RMS${x_e}$/m${y_e}$/m${z_e}$/m${\psi _e}$/rad${\theta _{e1}}$/rad${\theta _{e2}}$/rad
    RMPCESO0.000 30.000 30.000 10.002 30.004 90.000 5
    NTSMC0.008 30.111 70.036 10.040 30.004 20.730 1
    PID0.030 20.145 20.044 80.011 40.013 30.073 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-11-16
  • 修回日期:  2023-01-12
  • 录用日期:  2023-11-23
  • 网络出版日期:  2023-11-30

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