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基于分布式MPC的欠驱动自主水下航行器编队控制

郭渊博 李琦 闵博旭 高剑 陈依民

郭渊博, 李琦, 闵博旭, 等. 基于分布式MPC的欠驱动自主水下航行器编队控制[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.202204018
引用本文: 郭渊博, 李琦, 闵博旭, 等. 基于分布式MPC的欠驱动自主水下航行器编队控制[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.202204018
GUO Yuan-bo, LI Qi, MIN Bo-xu, GAO Jian, CHEN Yi-min. Formation Control of Underactuated AUVs Based on Distributed MPC[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.202204018
Citation: GUO Yuan-bo, LI Qi, MIN Bo-xu, GAO Jian, CHEN Yi-min. Formation Control of Underactuated AUVs Based on Distributed MPC[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.202204018

基于分布式MPC的欠驱动自主水下航行器编队控制

doi: 10.11993/j.issn.2096-3920.202204018
基金项目: 基础科研计划项目(JCKY2019207A019).
详细信息
    作者简介:

    郭渊博(1998-), 男, 硕士, 研究方向为航行器水下导航与控制

  • 中图分类号: TJ630.1;TP24

Formation Control of Underactuated AUVs Based on Distributed MPC

  • 摘要: 分布式模型预测控制(distributed model predictive control, DMPC)相较于集中式模型预测控制需要更低的计算量且具有更强的容错性和鲁棒性, 被广泛应用于多智能体编队控制。文中提出了一种基于分布式模型预测控制的欠驱动自主水下航行器编队控制方法, 基于局部邻居信息为每一个AUV控制器构建预测控制的代价函数和约束条件, 通过优化算法求解一定时域内的最优控制输入。同时, 针对编队系统可能存在的障碍物避碰问题和通信时延问题, 分别设计了基于距离和相对视线差的避障方法, 以及在接收到所有邻居信息后再求解的等待机制。仿真结果表明, 采用文中提出的方法, 多航行器编队能够在障碍及通信时延条件下保持队形稳定。

     

  • 图  1  系统模型的建立

    Figure  1.  Establishment of system model

    图  2  直线队形示意图

    Figure  2.  Schematic diagram of a straight line

    图  3  考虑避障区域约束的AUV几何示意图

    Figure  3.  Geometry diagram

    图  4  碰撞可能性

    Figure  4.  Possible collision conditions

    图  5  直线队形轨迹图

    Figure  5.  Line formation trajectory

    图  6  编队误差图

    Figure  6.  Error of diagram

    图  7  状态变量变化图

    Figure  7.  Changes in state variables

    图  8  避碰前轨迹图

    Figure  8.  Trajectory before collision avoidance

    图  9  避碰后轨迹图

    Figure  9.  Trajectory after collision avoidance

    图  10  成员距离变化图

    Figure  10.  Trajectory after collision avoidance

    图  11  时延未作处理轨迹图

    Figure  11.  Trajectory diagram without processing

    图  12  时延未作处理成员误差图

    Figure  12.  Membership error diagram without processing

    图  13  处理时延后避障轨迹图

    Figure  13.  Trajectory after processing

    图  14  时延成员误差图

    Figure  14.  Membership error diagram after processing

    表  1  算法流程

    Table  1.   The flow of the algorithm

      算法1 通信时延条件下的DMPC控制流程
      确定参数: $ \delta $, $ \tau _k^i $, ${\tau _k}$; 此处假设${\tau _k}$是已经被测量出的常量。
      (1)在$ {t_k} $时刻, AUVi利用邻居信息, 通过求解最优化问题式(5), 得到控制序列$ u_i^ * \left( {s;{t_k}} \right) $。
      (2)在$ s \in [{t_k},{t_k} + \delta + \tau _k^i) $时段内采用优化控制输入$ u_i^ * \left( {s;{t_k}} \right) $, 同时每个AUV在${t_k} + \delta $时刻完成自己状态的采样, 并估计自身${t_k} + \delta + {\tau _k}$时刻的状态, 然后将该时刻的状态信息估计值和$ {\hat u_i}\left( {s;{t_{k + 1}}} \right) $发送给邻居。AUVi在$ {t_k} + \delta + \tau _k^i $时刻接收到所有邻居${t_k} + \delta + {\tau _k}$的控制输入$ {\hat u_j}\left( {s;{t_{k + 1}}} \right) $和状态信息估计值$ {\hat z_j}({t_k} + \delta + {\tau _k}) $。
      (3)在$s \in [{t_k} + \delta + \tau _k^i,{t_k} + \delta + {\tau _k})$时段采用优化控制输入$ u_i^ * \left( {s;{t_k}} \right) $, 并且在${t_k} + \delta + {\tau _k}$时刻, 利用共用的系统时钟同步每一个AUV。
    下载: 导出CSV

    表  2  障碍物信息

    Table  2.   Obstacle information

    位置信息${\text{/m}}$${\text{R/m}}$$ {d}_{safe} $${\text{/m}}$
    (110, 15)2030
    下载: 导出CSV
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  • 网络出版日期:  2022-09-26

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