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基于分布式模型预测控制的欠驱动AUV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. 基于分布式模型预测控制的欠驱动AUV编队控制[J]. 水下无人系统学报, 2023, 31(3): 405-412 doi: 10.11993/j.issn.2096-3920.202204018
引用本文: 郭渊博, 李琦, 闵博旭, 等. 基于分布式模型预测控制的欠驱动AUV编队控制[J]. 水下无人系统学报, 2023, 31(3): 405-412 doi: 10.11993/j.issn.2096-3920.202204018
GUO Yuanbo, LI Qi, MIN Boxu, GAO Jian, CHEN Yimin. Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 405-412. doi: 10.11993/j.issn.2096-3920.202204018
Citation: GUO Yuanbo, LI Qi, MIN Boxu, GAO Jian, CHEN Yimin. Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 405-412. doi: 10.11993/j.issn.2096-3920.202204018

基于分布式模型预测控制的欠驱动AUV编队控制

doi: 10.11993/j.issn.2096-3920.202204018
基金项目: 国家重点研发计划项目(2021YFC2803000); 国防基础科研项目(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: TJ630.33; U674.941

Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control

  • 摘要: 分布式模型预测控制(DMPC)相较于集中式模型预测控制具有更低的计算量、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DMPC的欠驱动自主水下航行器(AUV)编队控制方法, 基于局部邻居信息为各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  单AUV系统模型示意图

    Figure  1.  Model of single AUV system

    图  2  多AUV直线队形示意图

    Figure  2.  Multiple AUVs’ straight line formation configuration

    图  3  考虑避障区域约束的AUV几何示意图

    Figure  3.  Geometry diagram of AUV considering obstacle avoidance zone constraints

    图  4  AUV编队与障碍物碰撞示意图

    Figure  4.  Schematic of AUV formation colliding with obstacles

    图  5  AUV编队直线队形轨迹图

    Figure  5.  Trajactories of AUV straight line formation

    图  6  AUV成员间编队误差图

    Figure  6.  Formation errors between AUV members

    图  7  各AUV控制输入和状态曲线

    Figure  7.  Curves of each AUV’s control inputs and states

    图  8  无避障策略时AUV编队轨迹

    Figure  8.  AUV formation trajectories without obstacle avoidance strategy

    图  9  有避障策略时AUV编队轨迹

    Figure  9.  AUV formation trajectories with obstacle avoidance strategy

    图  10  AUV2和AUV3之间距离变化曲线

    Figure  10.  Distance variation curve between AUV2 and AUV3

    图  11  无时延补偿下AUV编队轨迹

    Figure  11.  AUV formation trajectories without time delay compensation

    图  12  无时延补偿下AUV成员之间编队误差

    Figure  12.  Formation errors between AUV members without time delay compensation

    图  13  有时延补偿下AUV编队轨迹

    Figure  13.  AUV formation trajectories with time delay compensation

    图  14  有时延补偿下AUV成员之间编队误差

    Figure  14.  Formation errors between AUV members with time delay compensation

    表  1  障碍物信息

    Table  1.   Obstacle information

    位置信息${\text{/m}}$${R/{\rm{m}} }$${d}_{{\rm{safe}}}$${\text{/m}}$
    (110, 15)2030
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-24
  • 修回日期:  2022-06-18
  • 网络出版日期:  2022-09-26

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