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基于层级结构的空−地协同预设时间最优容错控制

成旺磊 张柯 姜斌

成旺磊, 张柯, 姜斌. 基于层级结构的空−地协同预设时间最优容错控制. 自动化学报, 2024, 50(8): 1−12 doi: 10.16383/j.aas.c230699
引用本文: 成旺磊, 张柯, 姜斌. 基于层级结构的空−地协同预设时间最优容错控制. 自动化学报, 2024, 50(8): 1−12 doi: 10.16383/j.aas.c230699
Cheng Wang-Lei, Zhang Ke, Jiang Bin. Hierarchical-based prescribed-time optimal fault-tolerant control for air-ground cooperative system. Acta Automatica Sinica, 2024, 50(8): 1−12 doi: 10.16383/j.aas.c230699
Citation: Cheng Wang-Lei, Zhang Ke, Jiang Bin. Hierarchical-based prescribed-time optimal fault-tolerant control for air-ground cooperative system. Acta Automatica Sinica, 2024, 50(8): 1−12 doi: 10.16383/j.aas.c230699

基于层级结构的空−地协同预设时间最优容错控制

doi: 10.16383/j.aas.c230699
基金项目: 国家自然科学基金(62020106003, 62173180, 62233009), 江苏省自然科学基金(BK20222012), 高等学校学科创新引智计划(B20007), 中央高校基本科研业务费(NC2022003, NE2022002), 江苏高校“青蓝工程”, 国家资助博士后研究人员计划(GZB20240974)资助
详细信息
    作者简介:

    成旺磊:南京航空航天大学自动化学院博士研究生. 主要研究方向为多智能体系统的容错控制及应用. E-mail: cwl2020nuaa@163.com

    张柯:南京航空航天大学自动化学院教授. 主要研究方向为故障诊断与容错控制及应用. E-mail: kezhang@nuaa.edu.cn

    姜斌:南京航空航天大学自动化学院教授. 主要研究方向为故障诊断与容错控制及应用. 本文通信作者. E-mail: binjiang@nuaa.edu.cn

Hierarchical-based Prescribed-time Optimal Fault-tolerant Control for Air-ground Cooperative System

Funds: Supported by National Natural Science Foundation of China (62020106003, 62173180, 62233009), Natural Science Foundation of Jiangsu Province of China (BK20222012), 111 Project of the Programme of Introducing Talents of Discipline to Universities of China (B20007), the Fundamental Research Funds for the Central Universities (NC2022003, NE2022002), Qing Lan Project of Jiangsu Province of China, and Postdoctoral Fellowship Program of CPSF (GZB20240974)
More Information
    Author Bio:

    CHENG Wang-Lei Ph.D. candidate at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. His research interest covers fault-tolerant control for multiagent systems and their application

    ZHANG Ke Professor at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. His research interest covers fault diagnosis and fault-tolerant control and their applications

    JIANG Bin Professor at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. His research interest covers fault diagnosis and fault-tolerant control and their applications. Corresponding author of this paper

  • 摘要: 研究了发生执行器故障的无人机−无人车异构编队系统的层级预设时间最优编队控制问题. 以保容错性能和收敛速度的优化控制为研究主线, 以层级控制、图博弈理论和预设时间控制为技术基础, 构建了一种预设时间最优容错控制算法. 虚拟层设计了基于一致性跟踪误差和能量消耗的二次型性能指标函数, 借助耦合哈密顿−雅克比−贝尔曼(Hanmilton-Jacobi-Bellman, HJB)方程和强化学习求解近似最优控制策略, 实现多智能体的同步最优控制和交互纳什均衡. 实际控制层基于最优信号并利用滑模控制和自适应技术, 设计了预设时间容错跟踪控制器, 实现对最优编队轨迹的有限时间跟踪. 在保证全局收敛时间完全不依赖于系统的初始状态和控制器参数的同时, 也有效实现对执行器故障参数的逼近. 最后, 通过仿真实验验证了所提控制策略的有效性.
  • 图  1  异构多智能体系统的结构图

    Fig.  1  Schematic diagram of the studied HMASs

    图  2  通讯拓扑结构

    Fig.  2  Communication topology

    图  3  虚拟层多智能体X轴一致性跟踪误差

    Fig.  3  Consensus tracking errors for agents in X axis of virtual layer

    图  4  虚拟层多智能体Y轴一致性跟踪误差

    Fig.  4  Consensus tracking errors for agents in Y axis of virtual layer

    图  5  虚拟层多智能体Z轴一致性跟踪误差

    Fig.  5  Consensus tracking errors for agents in Z axis of virtual layer

    图  6  实际层多智能体X轴编队跟踪误差

    Fig.  6  Formation tracking errors for agents in X axis of actual layer

    图  7  实际层多智能体Y轴编队跟踪误差

    Fig.  7  Formation tracking errors for agents in Y axis of actual layer

    图  8  实际层多智能体Z轴编队跟踪误差

    Fig.  8  Formation tracking errors for agents in Z axis of actual layer

    图  9  基于文献[35]所提方法的X轴编队跟踪误差

    Fig.  9  Formation tracking errors for agents in X axis based on the method proposed in reference [35]

    图  10  基于文献[35]所提方法的Y轴编队跟踪误差

    Fig.  10  Formation tracking errors for agents in Y axis based on the method proposed in reference [35]

    图  11  基于文献[35]所提方法的Z轴编队跟踪误差

    Fig.  11  Formation tracking errors for agents in Z axis based on the method proposed in reference [35]

    图  12  执行器故障参数估计图

    Fig.  12  Profiles of the estimated fault parameters

    图  13  智能体2的控制输入图

    Fig.  13  Control input of agent 2

    表  1  无人机和无人车的模型参数

    Table  1  Model parameters of UAVs and UGVs

    序号 参数 数值
    1 ${\xi _{xi}},\;{\xi _{yi}},\;{\xi _{zi}}$ $1.2 \times {10^{ - 2}}\ {\rm{N}}\cdot{\rm{s}}/{\rm{rad}}$
    2 $L_i$ $0.5\ {\rm{m}}$
    3 $m_i$ $2\ {\rm{kg}}$
    4 ${\kappa _i}$ $2.98 \times {10^{ - 6}}\ {\rm{N}}\cdot{{\rm{s}}^{\rm{2}}}{\rm{/ra}}{{\rm{d}}^{\rm{2}}}$
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  • 收稿日期:  2023-11-09
  • 录用日期:  2024-03-15
  • 网络出版日期:  2024-04-11

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