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基于时变障碍李雅普诺夫函数的变体无人机有限时间控制

李新凯 张宏立 范文慧

李新凯, 张宏立, 范文慧. 基于时变障碍李雅普诺夫函数的变体无人机有限时间控制. 自动化学报, 2022, 48(8): 2062−2074 doi: 10.16383/j.aas.c200712
引用本文: 李新凯, 张宏立, 范文慧. 基于时变障碍李雅普诺夫函数的变体无人机有限时间控制. 自动化学报, 2022, 48(8): 2062−2074 doi: 10.16383/j.aas.c200712
Li Xin-Kai, Zhang Hong-Li, Fan Wen-Hui. Finite-time control for morphing aerospace vehicle based on time-varying barrier Lyapunov function. Acta Automatica Sinica, 2022, 48(8): 2062−2074 doi: 10.16383/j.aas.c200712
Citation: Li Xin-Kai, Zhang Hong-Li, Fan Wen-Hui. Finite-time control for morphing aerospace vehicle based on time-varying barrier Lyapunov function. Acta Automatica Sinica, 2022, 48(8): 2062−2074 doi: 10.16383/j.aas.c200712

基于时变障碍李雅普诺夫函数的变体无人机有限时间控制

doi: 10.16383/j.aas.c200712
基金项目: 国家自然科学基金(51967019, 52065064), 新疆维吾尔自治区天山雪松计划(2020XS03), 新疆维吾尔自治区天山青年计划(2019Q064, 2020Q066) 资助
详细信息
    作者简介:

    李新凯:新疆大学电气工程学院博士研究生. 主要研究方向为电力巡检无人机控制, 非线性动力学. E-mail: lxk318@foxmail.com

    张宏立:新疆大学电气工程学院教授. 主要研究方向为复杂系统动力学分析, 非线性控制理论, 群智能优化和机器学习. 本文通信作者. E-mail: zhlxju@163.com

    范文慧:清华大学自动化系教授. 主要研究方向为多智能体建模与仿真, 复杂网络, 仿真系统和应用工程. E-mail: fanwenhui@tsinghua.edu.cn

Finite-time Control for Morphing Aerospace Vehicle Based on Time-varying Barrier Lyapunov Function

Funds: Supported by National Natural Science Foundation of China (51967019, 52065064), Tianshan Cedar Program of Xinjiang Uygur Autonomous Region (2020XS03), and Tianshan Youth Program of Xinjiang Uygur Autonomous Region (2019Q064, 2020Q066)
More Information
    Author Bio:

    LI Xin-Kai Ph.D. candidate at the School of Electrical Engineering, Xinjiang University. His research interest covers electric power inspection UAV control and nonlinear dynamics

    ZHANG Hong-Li Professor at the School of Electrical Engineering, Xinjiang University. His research interest covers complex system dynamics analysis, nonlinear control theory, swarm intelligence optimization, and machine learning. Corresponding author of this paper

    FAN Wen-Hui Professor in the Department of Automation, Tsinghua University. His research interest covers multi-agent modeling and simulation, complex network, simulation system, and application engineering

  • 摘要: 针对复杂扰动下可执行多种任务的复合式变体无人机, 提出了一种基于浸入与不变(Immersion and invariance, I&I)理论和隐含系统状态受限条件的复合时变障碍Lyapunov函数(Composite time-varying barrierLyapunov function, CTV-BLF)的控制方案. 设计了一种基于浸入与不变理论的扰动观测器, 构建了一种基于监督因子的有限时间动态尺度因子(Finite-time dynamic scaling factor, FT-DSF)调节器. 在此基础上, 设计了一种基于复合时变障碍Lyapunov函数和动态滑模面的控制器, 保证系统状态始终在约束条件之内. 通过衍生定理证明轨迹跟踪误差是有限时间稳定的. 最终仿真结果验证了所提方案的有效性.
  • 图  1  飞行模式切换示意图

    Fig.  1  Schematic diagram of flight mode switch

    图  2  变体无人机机体受力及坐标示意图

    Fig.  2  Schematic diagram of body force and coordinates of the morphing aerospace vehicle

    图  3  所提控制策略示意图

    Fig.  3  Schematic diagram of the proposed control strategy

    图  4  算例1中三维轨迹跟踪效果

    Fig.  4  The 3D trajectory tracking effects in case 1

    图  5  算例1中轨迹及姿态跟踪误差

    Fig.  5  Trajectory and attitude tracking errors in case 1

    图  6  算例1中2种基于I&I理论扰动观测器的扰动观测误差

    Fig.  6  Disturbances observation errors of two observers based on I&I theory in case 1

    图  7  算例1中2种方法的输入信号

    Fig.  7  Input signals of the two methods in case 1

    图  8  算例2中3种方法的空间位置跟踪效果

    Fig.  8  Position tracking effects of the three methods in case 2

    图  9  算例 2中3种方法轨迹跟踪误差及箱线图分析

    Fig.  9  Trajectory tracking errors and boxplot analysis of the three methods in case 2

    图  10  算例 2中3种方法姿态角跟踪误差及箱线图分析

    Fig.  10  Attitude tracking errors and boxplot analysis of the three methods in case 2

    图  12  算例2中3种方法的控制输入信号响应

    Fig.  12  Control input signal responses of the three methods in case 2

    图  13  算例2中3种方法的虚拟控制输入信号响应

    Fig.  13  Virtual control input signal responses of the three methods in case 2

    图  11  算例2中3种方法对外部扰动的估计效果

    Fig.  11  Estimation effects on external disturbances of the three methods in case 2

    图  15  算例2测试周期中本文方法机翼与机身夹角${\theta _f}$与机体速度变化的对应过程

    Fig.  15  The corresponding process of the angle ${\theta _f}$ between wing and fuselage, and the change of airframe velocity of the proposed method in case 2

    图  14  算例2测试周期中机翼与机身夹角${\theta _f}$自适应响应过程

    Fig.  14  The adaptive response of the angle ${\theta _f}$ between wing and fuselage during the test period in case 2

    图  16  ${\alpha _i} = 2$时6个控制通道FT-DSF自适应过程

    Fig.  16  FT-DSF adaptive process of six control channels when ${\alpha _i} = 2$

    图  17  监督因子${\alpha _1}$的变化对FT-DSF${r_1}$及动态尺度误差${z_1}$的响应过程(以$x$子系统为例) ((a) 随着${\alpha _1}$不同取值${r_1}$的自适应收敛响应; (b) ${\alpha _1}$不同取值对应${r_1}$的最终收敛值的变化趋势; (c) 随着${\alpha _1}$不同取值${z_1}$的自适应收敛响应)

    Fig.  17  The responses of FT-DSF ${r_1}$ and dynamic scaling error ${z_1}$ in response to the supervision factor ${\alpha _1}$ (take the $x$ subsystem as an example) ((a) Adaptive convergent response of ${r_1}$ with different values of ${\alpha _1}$; (b) Different values of ${\alpha _1}$ correspond to the change trend of the final convergence value of ${r_1}$; (c) Adaptive convergent response of ${z_1}$with different values of ${\alpha _1}$)

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出版历程
  • 收稿日期:  2020-09-01
  • 网络出版日期:  2021-04-30
  • 刊出日期:  2022-08-20

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