光学 精密工程, 2019, 27 (11): 2392, 网络出版: 2020-01-07   

高超声速飞行器的反步滑模神经网络控制系统

Backstepping sliding mode neural network control system for hypersonic vehicle
作者单位
1 南京航空航天大学 无人机研究院, 江苏 南京 210016
2 中国人民解放军第5311工厂, 江苏 南京 210000
摘要
针对高超声速飞行器一体化气动布局导致弹性机体与推进系统间的强耦合性, 以及跨大空域及高速飞行过程中导致气动特性存在强非线性、不确定性和明显的时变特性, 提出一种基于小脑神经网络的高超声速飞行器反步滑模控制策略。首先建立高超声速飞行器纵向非线性数学模型, 并采用输入-输出反馈线性化方法, 解除多变量之间的耦合关系; 然后设计基于反步法的滑模变结构控制器解决系统非匹配不确定性难题; 同时为弥补反步滑模控制器鲁棒性不足缺点, 利用自回归小脑神经网络(RCMAC)的在线非线性逼近、自学习能力和相应控制结构, 设计基于RCMAC的反步滑模控制器。仿真试验结果表明, 该方法下高超声速飞行器纵向的高度控制精度可达到0.5 m, 速度控制精度为0.1 m/s, 可以保证闭环系统全局稳定, 且拥有良好的跟踪性能和鲁棒性能。
Abstract
Strong coupling between the elastic body and the propulsion system in a hypersonic vehicle is caused by its integrated pneumatic layout and strong nonlinearity uncertainty, and obvious time-varying characteristics of aerodynamics, when the vehicle spans a large airspace and is flying at high speed. To eliminate the influence of this coupling, we propose a backstepping sliding mode control scheme based on a recurrent cerebellar model articulation controller (RCMAC). The input-output feedback linearization approach is used to resolve coupling between multiple variables. Firstly, we established the nonlinear mathematical longitudinal model of a hypersonic vehicle. Secondly, the sliding mode variable structure controller was designed to do away with the uncertainty of mismatch. Finally, the RCMAC-based backstepping sliding mode controller was designed. The controller makes up for the shortcoming of robustness of the hypersonic vehicle by its control structure and ability of nonlinear approximation and self-learning. The results of the simulation experiment indicate that the longitudinal altitude and velocity control precisions of a hypersonic vehicle can reach 0.5 m and 0.1 m/s, respectively and can therefore satisfy the system requirements of global stability, good dynamic responses, and robustness.
参考文献

[1] 熊柯. 高超声速飞行器巡航控制技术研究[D]. 长沙: 国防科学技术大学, 2012.

    XIONG K. Investigation on Control Technology for Hypersonic Vehicle during Cruise Phase[D]. Changsha: National University of Defense Technology, 2012. (in Chinese)

[2] 王剑颖, 梁海朝, 吴限德, 等. 高超声速飞行器连续终端滑模姿态控制方法[J]. 哈尔滨工程大学学报, 2016, 37(2): 187-191.

    WANG J Y, LIANG H ZH, WU X D, et al.. Continuous terminal sliding mode attitude control for hypersonic aircrafts[J]. Journal of Harbin Engineering University, 2016, 37(2): 187-191.(in Chinese)

[3] 吴雨珊. 近空间可变翼飞行器主动控制技术研究[D]. 南京: 南京航空航天大学, 2016.

    WU Y SH. Research on Active Control Technology of Near Space Morphing Vehicle[D].Nanjing: Nanjing University of Aeronautics and Astronautics,2016.(in Chinese)

[4] 余朝军, 江驹, 肖东, 等. 一种高超声速飞行器鲁棒自适应控制方法[J].宇航学报, 2017, 38(10): 1088-1096.

    YU CH J, JIANG J, XIAO D, et al..A novel robust adaptive control scheme for hypersonic vehicles[J].Journal of Astronautics, 2017, 38(10): 1088-1096.(in Chinese)

[5] BAHM C, BAUMANN E, MARTIN J, et al.. The X-43A hyper-X Mach 7 flight 2 guidance, navigation, and control overview and flight test results[C]. AIAA/CIRA 13th International Space Planes and Hypersonics Systems and Technologies Conference, Capua, Italy. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005: 3275.

[6] AL SAIDI R, MINAKER B. An Adaptive Sliding Mode Control for Trajectory Tracking of A Self-Reconfigurable Robotic System[M] Berlin: Springer International Publishing, 2014,27(4): 381-391.

[7] SNELL. Nonlinear Dynamic-Inversion Flight Control of Super-Maneuverable Aircraft[D]. Minnesota: University of Minnesota, 1991.

[8] 张军, 王玫, 赵德安. 高超飞行器的再入非线性鲁棒控制[J]. 动力学与控制学报, 2011, 9(1): 91-96.

    ZHANG J, WANG M, ZHAO D A. Re-entry nonlinear robust control law for hypersonic vehicles[J]. Journal of Dynamics and Control, 2011, 9(1): 91-96.(in Chinese)

[9] JOHNSON E, CALISE A, EL-SHIRBINY H, et al.. Feedback linearization with Neural Network augmentation applied to X-33 attitude control[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit, Dever, CO, USA. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000: 1-11.

[10] JOHNSON E N, CALISE A J. Limited authority adaptive flight control for reusable launch vehicles[J]. Journal of Guidance, Control, and Dynamics, 2003, 26(6): 906-913.

[11] VALASEK J, GEORGIE J. Selection of longitudinal desired dynamics for dynamic inversion controlled re-entry vehicles[C].AIAA Guidance, Navigation, and Control Conference and Exhibit, Montreal, Canada. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001: 4382.

[12] 周丽, 姜长生, 都延丽. 简化的鲁棒自适应模糊动态面控制及其应用[J]. 航空学报, 2008, 29(5): 1274-1280.

    ZHOU L, JIANG CH SH, DU Y L. Simplified robust adaptive fuzzy dynamic surface control and its application[J]. Acta Aeronautica Et Astronautica Sinica, 2008, 29(5): 1274-1280.(in Chinese)

[13] MUNOZ D, SBARBARO D. An adaptive sliding-mode controller for discrete nonlinear systems[J]. IEEE Transactions on Industrial Electronics, 2000, 47(3): 574-581.

[14] CHAMITOFF G E. Robust Intelligent Flight Control for Hypersonic Vehicles[D]. Boston: Massachusetts Institute of Technology, 1992.

[15] WU S F, ENGELEN C, BABUSKA R, et al.. Intelligent flight controller design with fuzzy logic for an atmospheric re-entry vehicle[C].38th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000.

[16] 王玉惠. 空天飞行器基于模糊理论的鲁棒自适应控制研究[D]. 南京: 南京航空航天大学, 2008.

    WANG Y H. Robust Adaptive Control Based on Fuzzy Theory for Aerospace Vehicle[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008.(in Chinese)

[17] SHTESSEL Y B, HALL C E. Multiple time scale sliding mode control of reusable launch vehicles in ascent and descent modes[C].Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), June 25-27, 2001. Arlington, VA, USA. New York, USA: IEEE, 2001: 25-27.

[18] KRGER T, SCHNETTER P, PLACZEK R, et al.. Fault-tolerant nonlinear adaptive flight control using sliding mode online learning[J]. Neural Networks, 2012, 32: 267-274.

[19] 蒲明, 吴庆宪, 姜长生, 等. 基于二阶动态Terminal滑模的近空间飞行器控制[J]. 宇航学报, 2010, 31(4): 1056-1062.

    PU M, WU Q X, JIANG CH SH, et al.. Near space vehicle control based on second-order dynamic terminal sliding mode[J]. Journal of Astronautics, 2010, 31(4): 1056-1062.(in Chinese)

[20] 蒲明, 吴庆宪, 姜长生, 等. 基于模糊干扰观测器的自适应二阶动态滑模控制, [J]. 控制理论与应用, 2011, 28(6): 805-812.

    PU M, WU Q X, JIANG CH SH, et al.. Adaptive second-order dynamic sliding-mode control based on fuzzy disturbance-observer[J]. Control Theory & Applications, 2011, 28(6): 805-812.(in Chinese)

[21] 范金锁, 张合新, 张明宽, 等. 基于自适应二阶终端滑模的飞行器再入姿态控制[J]. 控制与决策, 2012, 27(3): 403-407.

    FAN J S, ZHANG H X, ZHANG M K, et al.. Adaptive second-order terminal sliding mode control for aircraft re-entry attitude[J]. Control and Decision, 2012, 27(3): 403-407.(in Chinese)

[22] 刘燕斌, 陆宇平. 基于反步法的高超音速飞机纵向逆飞行控制[J]. 控制与决策, 2007, 22(3): 313-317.

    LIU Y B, LU Y P. Longitudinal inversion flight control based on backstepping for hypersonic vehicle[J]. Control and Decision, 2007, 22(3): 313-317.(in Chinese)

[23] 管萍, 和志伟. 高超声速飞行器姿态的自适应模糊滑模控制[J]. 控制工程, 2018, 25(7): 1139-1144.

    GUAN P, HE ZH W. Adaptive fuzzy sliding mode control for hypersonic vehicle attitude[J]. Control Engineering of China, 2018, 25(7): 1139-1144.(in Chinese)

[24] 方雪, 杨文骏, 樊征, 等. 高超声速飞行器的多幂次滑模控制[J]. 固体火箭技术, 2018, 41(2): 258-264.

    FANG X, YANG W J, FAN ZH, et al.. Multi power sliding mode control of hypersonic flight vehicle[J]. Journal of Solid Rocket Technology, 2018, 41(2): 258-264.(in Chinese)

刘蓉, 黄大庆, 姜定国. 高超声速飞行器的反步滑模神经网络控制系统[J]. 光学 精密工程, 2019, 27(11): 2392. LIU Rong, HUANG Da-qing, JIANG Ding-guo. Backstepping sliding mode neural network control system for hypersonic vehicle[J]. Optics and Precision Engineering, 2019, 27(11): 2392.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!