2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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, 2023, 49(12): 2532−2543 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, 2023, 49(12): 2532−2543 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, 2023, 49(12): 2532−2543 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, 2023, 49(12): 2532−2543 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: 国家自然科学基金 (62176109), 甘肃省自然科学基金 (21JR7RA531, 22JR5RA487), 中央高校基本科研业务费项目 (lzujbky-2023-ct05, lzujbky-2023-ey07), 兰州大学超级计算中心资助
详细信息
    作者简介:

    金龙:兰州大学信息科学与工程学院教授. 主要研究方向为神经网络, 机器人和智能信息处理. 本文通信作者. 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. 主要研究方向为机器人, 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. 主要研究方向为神经网络, 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研究员. 主要研究方向为康复机器人, 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 Natural Science Foundation of China (62176109), Natural Science Foundation of Gansu Province (21JR7RA531, 22JR5RA487), Fundamental Research Funds for the Central Universities (lzujbky-2023-ct05, lzujbky-2023-ey07), and Supercomputing Center of Lanzhou University
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. Corresponding author of this paper

    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 and flapping wing aircraft control

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

    CHENG Long Professor at 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. 目前, 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[14]中的控制器(81)姿态控制

    Fig.  3  Attitude control of the controller (81) in [14]

    图  4  文献[14]中的控制器(81)姿态控制误差

    Fig.  4  Attitude control error of the controller (81) in [14]

    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

    $e_\theta $ (rad) $e_\phi$ (rad) $e_ \psi$(rad)
    平均值 标准差 平均值 标准差 平均值 标准差
    参考文献[14]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
  • [1] Mackenzie D. A flapping of wings. Science, 2012, 335(6075): 1430−1433 doi: 10.1126/science.335.6075.1430
    [2] Rege A A. Characterization of Flapping Wing Aerodynamics and Flight Dynamics Analysis Using Computational Methods [Ph.D. dissertation], The University of Texas, USA, 2015.
    [3] Dickinson M H, Lehmann F O, Sane S P. Wing rotation and the aerodynamic basis of insect flight. Science, 1999, 284(5422): 1954−1960 doi: 10.1126/science.284.5422.1954
    [4] 周骥平, 武立新, 朱兴龙. 仿生扑翼飞行器的研究现状及关键技术. 机器人技术与应用, 2004, 4(6): 12−17) doi: 10.3969/j.issn.1004-6437.2004.06.004

    Zhou Ji-Ping, Wu Li-Xin, Zhu Xing-Long. The present research situation and key technology. Robort Techniqae and Application. 2004, 4(6): 12−17 doi: 10.3969/j.issn.1004-6437.2004.06.004
    [5] Rose C, Fearing R S. Comparison of ornithopter wind tunnel force measurements with free flight. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014. 1816−1821
    [6] Keennon M, Klingebiel K, Won H, Andriukov A. Development of the nano hummingbird: A tailless flapping wing micro air vehicle. In: Proceedings of the 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Nashville, USA: AIAA, 2012. 129−134
    [7] Ma K Y, Chirarattananon P, Fuller S B, Wood R J. Controlled flight of a biologically inspired, insect-scale robot. Science, 2013, 340(6132): 603−607 doi: 10.1126/science.1231806
    [8] Xuan J L, Song B F, Song W P, Yang W Q, Xue D, Liang S R. Progress of Chinese “Dove” and future studies on flight mechanism of birds and application system. Transactions of Nanjing University of Aeronautics & Astronautics, 2020, 37(5): 663−675
    [9] 刘晶, 汪超, 谢鹏, 周超英. 基于PD控制的仿昆虫扑翼样机研制. 航空学报, 2020, 41(9): Article No. 223678

    Liu Jing, Wang Chao, Xie Peng, Zhou Chao-Ying. Development of insect-like flapping wing micro air vehicle based on PD control. Acta Aeronautica et Astronautica Sinica, 2020, 41(9): Article No. 223678
    [10] Li J X, Wang C, Liu J, Xie P, Zhou C Y. Design and implementation of hovering flapping wing micro air vehicle. In: Proceedings of the 12th International Conference on Intelligent Robotics and Applications. Shenyang, China: Springer, 2019. 226−233
    [11] Masu K, Machida K, Yamane D, Ito H, Ishihara N, Chang T M, et al. (Invited) CMOS-MEMS based microgravity sensor and its application. ECS Transactions, 2020, 97(5): 91−108 doi: 10.1149/09705.0091ecst
    [12] 曹风魁, 庄严, 闫飞, 杨奇峰, 王伟. 移动机器人长期自主环境适应研究进展和展望. 自动化学报, 2020, 46(2): 205−221

    Cao Feng-Kui, Zhuang Yan, Yan Fei, Yang Qi-Feng, Wang Wei. Long-term autonomous environment adaptation of mobile robots: State-of-the-art methods and prospects. Acta Automatica Sinica, 2020, 46(2): 205−221
    [13] Banazadeh A, Taymourtash N. Adaptive attitude and position control of an insect-like flapping wing air vehicle. Nonlinear Dynamics, 2016, 85(1): 47−66 doi: 10.1007/s11071-016-2666-8
    [14] He W, Yan Z C, Sun C Y, Chen Y. Adaptive neural network control of a flapping wing micro aerial vehicle with disturbance observer. IEEE Transactions on Cybernetics, 2017, 47(10): 3452−3465 doi: 10.1109/TCYB.2017.2720801
    [15] He W, Mu X X, Zhang L, Zou Y. Modeling and trajectory tracking control for flapping-wing micro aerial vehicles. IEEE/CAA Journal of Automatica Sinica, 2021, 8(1): 148−156 doi: 10.1109/JAS.2020.1003417
    [16] 贺威, 丁施强, 孙长银. 扑翼飞行器的建模与控制研究进展. 自动化学报, 2017, 43(5): 685−696

    He Wei, Ding Shi-Qiang, Sun Chang-Yin. Research progress on modeling and control of flapping-wing air vehicles. Acta Automatica Sinica, 2017, 43(5): 685−696
    [17] Jha S K, Bhasin S. Adaptive linear quadratic regulator for continuous-time systems with uncertain dynamics. IEEE/CAA Journal of Automatica Sinica, 2020, 7(3): 833−841 doi: 10.1109/JAS.2019.1911438
    [18] Yang X, Zhao B. Optimal neuro-control strategy for nonlinear systems with asymmetric input constraints. IEEE/CAA Journal of Automatica Sinica, 2020, 7(2): 575−583 doi: 10.1109/JAS.2020.1003063
    [19] Qi Y M, Jin L, Li H X, Li Y M, Liu M. Discrete computational neural dynamics models for solving time-dependent Sylvester equation with applications to robotics and MIMO systems. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6231−6241 doi: 10.1109/TII.2020.2966544
    [20] Wei L, Jin L, Yang C G, Chen K, Li W B. New noise-tolerant neural algorithms for future dynamic nonlinear optimization with estimation on Hessian matrix inversion. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(4): 2611−2623 doi: 10.1109/TSMC.2019.2916892
    [21] Jin L, Yan J K, Du X J, Xiao X C, Fu D Y. RNN for solving time-variant generalized Sylvester equation with applications to robots and acoustic source localization. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6359−6369 doi: 10.1109/TII.2020.2964817
    [22] Wang F Y, Zhang J J, Zheng X H, Wang X, Yuan Y, Dai X X, et al. Where does AlphaGo go: From Church-Turing thesis to AlphaGo thesis and beyond. IEEE/CAA Journal of Automatica Sinica, 2016, 3(2): 113−120 doi: 10.1109/JAS.2016.7471613
    [23] 王飞跃. 平行控制: 数据驱动的计算控制方法. 自动化学报, 2013, 39(4): 293−302)

    Wang Fei-Yue. Parallel control: A method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293−302
    [24] 王飞跃. 关于复杂系统的建模、分析、控制和管理. 复杂系统与复杂性科学, 2006, 3(2): 26−34 doi: 10.3969/j.issn.1672-3813.2006.02.004

    Wang Fei-Yue. On the modeling, analysis, control and management of complex systems. Complex Systems and Complexity Science, 2006, 3(2): 26−34 doi: 10.3969/j.issn.1672-3813.2006.02.004
    [25] Wei Q L, Wang L X, Lu J W, Wang F Y. Discrete-time self-learning parallel control. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(1): 192−204 doi: 10.1109/TSMC.2020.2995646
    [26] 王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5): 485−489, 514 doi: 10.3321/j.issn:1001-0920.2004.05.002

    Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485−489, 514 doi: 10.3321/j.issn:1001-0920.2004.05.002
    [27] 王坤峰, 苟超, 王飞跃. 平行视觉: 基于ACP的智能视觉计算方法. 自动化学报, 2016, 42(10): 1490−1500

    Wang Kun-Feng, Gou Chao, Wang Fei-Yue. Parallel vision: An ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10): 1490−1500
    [28] 王晓, 要婷婷, 韩双双, 曹东璞, 王飞跃. 平行车联网: 基于ACP的智能车辆网联管理与控制. 自动化学报, 2018, 44(8): 1391−1404

    Wang Xiao, Yao Ting-Ting, Han Shuang-Shuang, Cao Dong-Pu, Wang Fei-Yue. Parallel internet of vehicles: The ACP-based networked management and control for intelligent vehicles. Acta Automatica Sinica, 2018, 44(8): 1391−1404
    [29] 白天翔, 王帅, 沈震, 曹东璞, 郑南宁, 王飞跃. 平行机器人与平行无人系统: 框架、结构、过程、平台及其应用. 自动化学报, 2017, 43(2): 161−175

    Bai Tian-Xiang, Wang Shuai, Shen Zhen, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel robotics and parallel unmanned systems: Framework, structure, process, platform and applications. Acta Automatica Sinica, 2017, 43(2): 161−175
    [30] 王晓, 韩双双, 杨林瑶, 曾轲, 王飞跃. 基于ACP的动态网民群体运动组织建模与计算实验研究. 自动化学报, 2020, 46(4): 653−669

    Wang Xiao, Han Shuang-Shuang, Yang Lin-Yao, Zeng Ke, Wang Fei-Yue. The research on ACP-based modeling and computational experiment for cyber movement organizations. Acta Automatica Sinica, 2020, 46(4): 653−669
    [31] 陈虹宇, 艾红, 王晓, 吕宜生, 陈圆圆, 王飞跃. 社会交通中的社会信号分析与感知. 自动化学报, 2021, 47(6): 1256−1272

    Chen Hong-Yu, Ai Hong, Wang Xiao, Lv Yi-Sheng, Chen Yuan-Yuan, Wang Fei-Yue. Analysis and perception of social signals in social transportation. Acta Automatica Sinica, 2021, 47(6): 1256−1272
    [32] 王飞跃. 平行控制与数字孪生: 经典控制理论的回顾与重铸. 智能科学与技术学报, 2020, 2(3): 293−300

    Wang Fei-Yue. Parallel control and digital twins: Control theory revisited and reshaped. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 293−300
    [33] Wei Q L, Li H Y, Wang F Y. Parallel control for continuous-time linear systems: A case study. IEEE/CAA Journal of Automatica Sinica, 2020, 7(4): 919−928 doi: 10.1109/JAS.2020.1003216
    [34] Lu J W, Wei Q L, Wang F Y. Parallel control for optimal tracking via adaptive dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2020, 7(6): 1662−1674 doi: 10.1109/JAS.2020.1003426
    [35] Han X M, Zhao X D, Sun T, Wu Y H, Xu N, Zong G D. Event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(12): 7850−7859 doi: 10.1109/TSMC.2020.2987136
    [36] Shanmugam L, Joo Y H. Stability and Stabilization for T-S fuzzy large-scale interconnected power system with wind farm via sampled-data control. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(4): 2134−2144 doi: 10.1109/TSMC.2020.2965577
    [37] 薄迎春, 张欣, 刘宝. 延迟深度回声状态网络及其在时间序列预测中的应用. 自动化学报, 2020, 46(8): 1644−1653

    Bo Ying-Chun, Zhang Xin, Liu Bao. Delayed deep echo state network and its application on time series prediction. Acta Automatica Sinica, 2020, 46(8): 1644−1653
    [38] Principi E, Rossetti D, Squartini S, Piazza F. Unsupervised electric motor fault detection by using deep autoencoders. IEEE/CAA Journal of Automatica Sinica, 2019, 6(2): 441−451 doi: 10.1109/JAS.2019.1911393
    [39] Jin L, Ying L F, Lu H Y, Zhang Z J. Saturation-allowed neural dynamics applied to perturbed time-dependent system of linear equations and robots. IEEE Transactions on Industrial Electronics, 2021, 68(10): 9844−9854 doi: 10.1109/TIE.2020.3029478
    [40] 郑君里, 应启珩, 杨为理. 信号与系统. 第3版. 北京: 高等教育出版社, 2000.

    Zheng Jun-Li, Ying Qi-Heng, Yang Wei-Li. Signals and Systems (3rd edition). Beijing: Higher Education Press, 2000.
    [41] 邹祎. 硬件在环仿真系统概述. 价值工程, 2016, 35(35): 97−98

    Zou Yi. Overview of hardware-in-loop simulation System. Value Engineering, 2016, 35(35): 97−98
    [42] Dai X H, Ke C X, Quan Q, Cai K Y. Simulation credibility assessment methodology with FPGA-based Hardware-in-the-Loop platform. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3282−3291 doi: 10.1109/TIE.2020.2982122
  • 加载中
图(14) / 表(1)
计量
  • 文章访问数:  1546
  • HTML全文浏览量:  507
  • PDF下载量:  258
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-03-04
  • 录用日期:  2021-07-15
  • 网络出版日期:  2021-09-17
  • 刊出日期:  2023-12-27

目录

    /

    返回文章
    返回