Volume 50 Issue 5
May  2024
Turn off MathJax
Article Contents
YANG B,JIN F T,LIU Y B,et al. Intelligent solution method based on high-speed aircraft fire control model[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(5):1693-1701 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0503
Citation: YANG B,JIN F T,LIU Y B,et al. Intelligent solution method based on high-speed aircraft fire control model[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(5):1693-1701 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0503

Intelligent solution method based on high-speed aircraft fire control model

doi: 10.13700/j.bh.1001-5965.2022.0503
Funds:  Fundamental Research Funds for the Central Universities (NS2021061); Basic Research Plan of Jiangsu Province for Young Scholars (BK20200437)
More Information
  • Corresponding author: E-mail:liuyb@nuaa.edu.cn
  • Received Date: 20 Jun 2022
  • Accepted Date: 12 Aug 2022
  • Available Online: 13 Jan 2023
  • Publish Date: 12 Jan 2023
  • This paper studies the fire control calculation problem of air-breathing high-speed aircraft. High-speed aircraft has higher requirements for the fire control model calculation, a more complicated flying environment, and a shorter response time than traditional subsonic and supersonic combat aircraft. A calculation method of the high-speed aircraft fire control model is proposed. First of all, the mathematical model of the air-breathing high-speed flight vehicle is established; and the fire control model of the high-speed aircraft platform is constructed. Then, the attack area is solved using the fast simulation method in conjunction with the Archimedes optimization algorithm based on the flight characteristics of the high-speed aircraft, and the carrier aircraft's initial command signal is solved in reverse. The simulation results show that the solution method has high solution accuracy, few control parameters, wide attack area, and can exert strong flight performance of high-speed aircraft.

     

  • loading
  • [1]
    张灿, 王轶鹏, 叶蕾. 国外近十年高超声速飞行器技术发展综述[J]. 战术导弹技术, 2020(6): 81-86.

    ZHANG C, WANG Y P, YE L. Summary of the technological development of overseas hypersonics in the past ten years[J]. Tactical Missile Technology, 2020(6): 81-86(in Chinese).
    [2]
    SHAO X, SHI Y, ZHANG W. Fault-tolerant quantized control for flexible air-breathing hypersonic vehicles with appointed-time tracking performances[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 57(2): 1261-1273.
    [3]
    安昊. 吸气式高超声速飞行器控制方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

    AN H. Research on control method of air-breathing hypersonic vehicles[D]. Harbin: Harbin Institute of Technology, 2017(in Chinese).
    [4]
    寇英信, 李战武, 陈哨东, 等. 火控系统在航空作战中的作用——作战飞机之“魂”[J]. 电光与控制, 2013, 20(12): 1-5.

    KOU Y X, LI Z W, CHEN S D, et al. The important role of fire control system in air combat-soul of fighters[J]. Electronics Optics & Control, 2013, 20(12): 1-5(in Chinese).
    [5]
    程江涛. 航空火力控制原理[M]. 北京: 国防工业出版社, 2017: 2-8.

    CHEN J T. Principles of aviation fire control[M]. Beijing: National Defense Industry Press, 2017: 2-8(in Chinese).
    [6]
    SHEN H, CAO R, LIU Y, et al. Control-oriented low-speed dynamic modeling and trade-off analysis of air-breathing aerospace vehicles[J]. Journal of Zhejiang University-Science A, 2019, 20(12): 893-907. doi: 10.1631/jzus.A1900366
    [7]
    杨超, 许赟, 谢长川. 高超声速飞行器气动弹性力学研究综述[J]. 航空学报, 2010, 31(1): 1-11.

    YANG C, XU Y, XIE C C. Review of studies on aeroelasticity of hypersonic vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(1): 1-11(in Chinese).
    [8]
    张安, 陈伟, 李相民. 战斗机智能火力与指挥控制系统的发展和关键技术[J]. 电光与控制, 2006, 13(4): 1-5.

    ZHANG A, CHEN W, LI X M, On development and critical technologies of intelligent fire and command control control system for modern fighters[J]. Electronics Optics & Control, 2006, 13(4): 1-5(in Chinese).
    [9]
    胡晓峰, 荣明. 智能化作战研究值得关注的几个问题[J]. 指挥与控制学报, 2018, 4(3): 195-200.

    HU X F, RONG M. Several important questions of intelligent warfare research[J]. Journal of Command and Control, 2018, 4(3): 195-200(in Chinese).
    [10]
    陈哨东, 刘跃峰. 论航空火力控制系统的智能化[J]. 火力与指挥控制, 2020, 45(8): 1-8. doi: 10.3969/j.issn.1002-0640.2020.08.001

    CHEN S D, LIU Y F. Intelligentization of airborne fire control system[J]. Fire Control & Command Control, 2020, 45(8): 1-8(in Chinese). doi: 10.3969/j.issn.1002-0640.2020.08.001
    [11]
    孔维仁, 周德云, 赵艺阳, 等. 基于深度强化学习与自学习的多无人机近距空战机动策略生成算法[J]. 控制理论与应用, 2022, 39(2): 352-362.

    KONG W R, ZHOU D Y, ZHAO Y Y, et al. Maneuvering strategy generation algorithm for multi-UAV in close-range air combat based on deep reinforcement learning and self-play[J]. Control Theory & Applications, 2022, 39(2): 352-362(in Chinese).
    [12]
    林诗洁, 董晨, 陈明志, 等. 新型群智能优化算法综述[J]. 计算机工程与应用, 2018, 54(12): 1-9.

    LIN S J, DONG C, CHEN M Z, et al. Summary of new group intelligent optimization algorithms[J]. Computer Engineering and Applications, 2018, 54(12): 1-9(in Chinese).
    [13]
    GUTJAHR W J. ACO algorithms with guaranteed convergence to the optimal solution[J]. Information Processing Letters, 2002, 82(3): 145-153. doi: 10.1016/S0020-0190(01)00258-7
    [14]
    HARADA T, ALBA E. Parallel genetic algorithms: a useful survey[J]. ACM Computing Surveys (CSUR), 2020, 53(4): 1-39.
    [15]
    TALAMONTI M, GALLUZZO M, CHIRICOZZI A, et al. Characteristic of chronic plaque psoriasis patients treated with biologics in Italy during the COVID-19 pandemic: risk analysis from the PSO-BIO-COVID observational study[J]. Expert Opinion on Biological Therapy, 2021, 21(2): 271-277. doi: 10.1080/14712598.2021.1853698
    [16]
    DUAN H, QIAO P. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning[J]. InternAtional Journal of Intelligent Computing and Cybernetics, 2014, 7(1): 24-37. doi: 10.1108/IJICC-02-2014-0005
    [17]
    陈磊, 寇英信, 李战武, 等. 新型CCAR与CCRP融合式轰炸瞄准原理建模与分析[J]. 电光与控制, 2013, 20(8): 58-62.

    CHEN L, KOU Y X, LI Z W, et al. Modeling and analysis of a fused bomb aiming principle integrating CCAR with CCRP[J]. Electronics Optics & Control, 2013, 20(8): 58-62(in Chinese).
    [18]
    XU Z P, GENG L N. Study on release region of guided bombs based on multi-island genetic algorithm and quadratic programming[C]//Proceedings of 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). Piscataway: IEEE Press, 2017: 687-691.
    [19]
    SEONG-GYUN K, JUNGHO P. Computation for launch acceptability region of air-to-surface guided bomb using artificial neural network[J]. Journal of the Korean Society for Aeronautical & Space Sciences, 2018, 46(4): 283-289.
    [20]
    REN Y, YANG J, XIONG W. Hybrid guidance for common aero vehicle equilibrium glide reentry with multi-constraints[C]//2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI). Piscataway: IEEE Press, 2019: 340-346.
    [21]
    石国祥, 张科, 王佩, 等. 基于侧向机动能力预测的高超声速飞行器再入制导算法研究[J]. 西北工业大学学报, 2020, 38(3): 523-532. doi: 10.3969/j.issn.1000-2758.2020.03.010

    SHI G X, ZHANG K, WANG P, et al. Algorithm of reentry guidance for hypersonic vehicle based on lateral maneuverability prediction[J]. Journal of Northwestern Polytechnical University, 2020, 38(3): 523-532(in Chinese). doi: 10.3969/j.issn.1000-2758.2020.03.010
    [22]
    SUSHNIGDHA G, JOSHI A. Trajectory design of re-entry vehicles using combined pigeon inspired optimization and orthogonal collocation method[J]. IFAC-PapersOnLine, 2018, 51(1): 656-662. doi: 10.1016/j.ifacol.2018.05.110
    [23]
    RICHIE G. The Common Aero Vehicle-Space delivery system of the future[C]//Proceedings of Space Technology Conference and Exposition. Reston: AIAA, 1999: 4435.
    [24]
    汤新民, 李腾, 陈强超, 等. 基于交互式多模型的短期4D航迹预测[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(1): 39-45.

    TANG X M, LI T, CHEN Q C, et al. Short-term 4D trajectory porediction based on interactive multi-models[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2020, 44(1): 39-45(in Chinese).
    [25]
    HASHIM F A , HUSSAIN K , HOUSSEIN E H , et al. Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems[J]. Applied Intelligence, 2021, 51(3): 1531-1551.
    [26]
    石宏庆, 侯庆, 徐榛, 等. 优化算法测试函数综述及应用分析[J]. 计算机科学与应用, 2021, 11(11): 2633-2645.

    SHI H Q, HOU Q, XU Z , et al. Summary and application analysis of optimization algorithm test function[J]. Computer Science and Application, 2021, 11(11): 2633-2645(in Chinese).
    [27]
    张亚平. 鸽群智能算法的改进及其在高超声速飞行控制中的应用[D]. 南京: 南京航空航天大学, 2017.

    ZHANG Y P. Improvement of pigeon-inspired optimization algorithm and its application in hypersonic flight control[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017(in Chinese).
    [28]
    黄威, 任洋. 基于自适应步长的空空导弹攻击区解算方法[J]. 电光与控制, 2019, 26(5): 55-58.

    HUANG W, REN Y. Adaptive step-sizebased calculation of air-to-air missile launch envelopes[J]. Electronics Optics & Control, 2019, 26(5): 55-58(in Chinese).
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(4)

    Article Metrics

    Article views(329) PDF downloads(9) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return