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基于ACP理论的微型扑翼飞行器的姿态控制

金龙 李嘉昌 常振强 卢经纬 程龙

金龙, 李嘉昌, 常振强, 卢经纬, 程龙. 基于ACP理论的微型扑翼飞行器的姿态控制. 自动化学报, 2021, 47(x): 1001−1013 doi: 10.16383/j.aas.c210646
引用本文: 金龙, 李嘉昌, 常振强, 卢经纬, 程龙. 基于ACP理论的微型扑翼飞行器的姿态控制. 自动化学报, 2021, 47(x): 1001−1013 doi: 10.16383/j.aas.c210646
Jin Long, Li Jia-Chang, Chang Zhen-Qiang, Lu Jing-Wei, Cheng Long. Attitude control for flapping wing micro aerial vehicle based on ACP theory. Acta Automatica Sinica, 2021, 47(x): 1001−1013 doi: 10.16383/j.aas.c210646
Citation: Jin Long, Li Jia-Chang, Chang Zhen-Qiang, Lu Jing-Wei, Cheng Long. Attitude control for flapping wing micro aerial vehicle based on ACP theory. Acta Automatica Sinica, 2021, 47(x): 1001−1013 doi: 10.16383/j.aas.c210646

基于ACP理论的微型扑翼飞行器的姿态控制

doi: 10.16383/j.aas.c210646
基金项目: 国家重点研发计划资助项目(2017YFE0118900), 青海省自然科学基金团队项目(2020-ZJ-903), 青海省物联网重点实验室(2020-ZJ-Y16), 甘肃省自然科学基金项目(20JR10RA639), 中央高校基本科研业务费(lzujbky-2021-65, lzujbky-2021-it35和lzujbky-2021-it36)资助
详细信息
    作者简介:

    金龙:兰州大学信息科学与工程学院正教授. 主要研究兴趣包括神经网络、机器人技术和智能信息处理. E-mail: jinlongsysu@foxmail.com

    李嘉昌:兰州大学信息科学与工程学院硕士研究生. 主要研究兴趣为机器人、神经网络和非线性方程. E-mail: lzdxljc@163.com

    常振强:兰州大学信息科学与工程学院硕士研究生. 他的主要研究兴趣为神经网络, 扑翼飞行器控制. E-mail: changzhq18@lzu.edu

    卢经纬:中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生. 主要研究方向为最优控制, 自适应动态规划和强化学习. E-mail: lujingwei2019@ia.ac.cn

    程龙:中国科学院自动化研究所正研究员. 主要研究方向包括康复机器人、智能控制和神经网络. E-mail: long.cheng@ia.ac.cn

Attitude Control for Flapping Wing Micro Aerial Vehicle Based on Acp Theory

Funds: Supported by National Key Research and Development Program of China under Grant (2017YFE0118900), Qinghai ProvincialNatural Science Foundation Team Project (2020-ZJ-903),Qinghai Provincial Key Laboratory of Internet of Things (2020-ZJ-Y16), Natural Science Foundation of Gansu Province(20JR10RA639), Fundamental Research Funds for the Central Universities (lzujbky-2021-65, lzujbky-2021-it35 and lzujbky-2021-it36)
More Information
    Author Bio:

    JIN Long Professor at the School of Information Science and Engineering, Lanzhou University. His research interest covers neural networks, robotics, and intelligent information processing

    LI Jia-Chang Master student at the School of Information Science and Engineering, Lanzhou University. His research interest covers robotics, neural networks and nonlinear equations

    CHANG Zhen-Qiang Master student at the School of Information Science and Engineering, Lanzhou University. His research interest covers neural networks, flapping wing aircraft control

    LU Jing-Wei Ph.D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers optimal control, adaptive dynamic programming, and reinforcement learning

    CHENG Long Professor with the Institute of Automation, Chinese Academy of Sciences. His research interest covers rehabilitation robot, intelligent control, and neural networks

  • 摘要: 微型扑翼飞行器(Flapping wing micro aerial vehicle, FWMAV)因飞行效率高、质量轻、耗能低、机动性强等显著优点, 在飞行器研究和应用中占据重要地位. 当下, FWMAV姿态控制成为飞行器控制研究领域的研究热点. 针对FWMAV姿态控制问题, 基于平行智能理论框架提出了一种FWMAV抗扰动姿态控制器. 通过建立人工系统(Artificial systems, A)、计算实验(Computational experiments, C)、平行执行(Parallel execution, P)三个过程, 得到一个能够有效解决FWMAV姿态控制过程中扰动问题的控制器, 并通过理论分析和数值仿真证明了该控制器的有效性.
  • 图  1  FWMAV结构示意图

    Fig.  1  Schematic diagram of FWMAV

    图  2  FWMAV姿态控制ACP过程

    Fig.  2  ACP processes of FWMAV attitude control

    图  3  文献[8]中的控制器(81)姿态控制

    Fig.  3  Attitude control of the controller(81) in reference [8]

    图  4  文献[8]中的控制器(81)姿态控制误差

    Fig.  4  Attitude control error of the controller (81) in reference [8]

    图  5  恒定扰动下抗扰动ND控制器姿态控制

    Fig.  5  Attitude control of the anti-disturbance ND controller with constant disturbance

    图  6  恒定扰动下抗扰动ND控制器姿态控制误差

    Fig.  6  Attitude control error of the anti-disturbance ND controller with constant disturbance

    图  7  线性时变扰动下抗扰动ND控制器姿态控制

    Fig.  7  Attitude control of the anti-disturbance ND controller with linear time-varying disturbance

    图  8  线性时变扰动下抗扰动ND控制器姿态控制误差

    Fig.  8  Attitude control error of the anti-disturbance ND controller with linear time-varying disturbance

    图  9  有界随机扰动下抗扰动ND控制器姿态控制

    Fig.  9  Attitude control of the anti-disturbance ND controller with bounded random disturbance

    图  10  有界随机扰动下抗扰动ND控制器姿态控制误差

    Fig.  10  Attitude control error of the anti-disturbance ND controller with bounded random disturbance

    图  11  正弦扰动下抗扰动ND控制器姿态控制

    Fig.  11  Attitude control of the anti-disturbance ND controller with sine disturbance

    图  12  正弦扰动下抗扰动ND控制器姿态控制误差

    Fig.  12  Attitude control error of the anti-disturbance ND controller with sine disturbance

    图  13  FWMAV系统硬件在环Simulink仿真框图

    Fig.  13  FWMAV HIL Simulink simulation block diagram

    图  14  FWMAV ND姿态控制器硬件在环仿真误差曲线图

    Fig.  14  HIL simulation error curve of FWMAV ND attitude controller

    表  1  控制器性能比较

    Table  1  Comparison of performance among different controllers

    eθ(rad) eϕ(rad) eψ(rad)
    平均值 标准差 平均值 标准差 平均值 标准差
    参考文献[8]中的控制器(81) −0.0288 0.2483 −0.0166 0.3890 −0.0241 0.6696
    ND抗扰动控制器(恒定扰动) −0.0162 0.0040 −0.0192 0.0185 −0.0101 0.0019
    ND抗扰动控制器(线性时变扰动) −0.0038 0.0040 −0.0065 0.0200 −0.0029 0.0029
    ND抗扰动控制器(有界随机扰动) −0.0021 0.0032 −0.0048 0.0199 −0.0020 0.0028
    ND抗扰动控制器(正弦扰动) −0.0004 0.0043 −0.0033 0.0202 −0.0010 0.0038
    下载: 导出CSV
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