基于多核融合RBF神经网络的高超声速飞行器自适应滑模控制

Adaptive Sliding Mode Control of Hypersonic Vehicle Based on a Multi-kernel Fused RBF Neural Network

摘要

针对高超声速飞行器由于模型存在未知复杂非线性函数而引起的控制难题,提出了一种基于多核融合径向基函数神经网络的自适应滑模控制方法。该方法首先构建多核融合径向基神经网络,以精确逼近高超声速飞行器模型中的复杂非线性函数;其次,基于自适应理论,设计了神经网络权值的实时更新策略,从而确保估计的收敛性与精度。在此基础上,将高超声速飞行器控制系统分解为速度和高度两个子系统,对状态变量较多的高度子系统采用反演理论进行简化。根据滑模控制理论,对两个子系统分别设计了控制律,并利用Lyapunov稳定性理论证明了所设计控制器的渐近收敛性,确保了控制策略的稳定性。仿真结果表明,在存在复杂扰动的情况下,本方法能够有效跟踪速度、高度等关键状态参数,且跟踪性能优于传统方法,显著提升了高超声速飞行器控制系统的稳定性和控制精度。

Abstract

To address the control challenges of hypersonic vehicle arising from unknown complex nonlinearities in the model, an adaptive sliding mode control method based on a multi-kernel fused radial basis function (RBF) neural network was proposed. By constructing a multi-kernel fused radial basis function neural network, the complex nonlinear functions in the hypersonic vehicle model were accurately estimated. Based on adaptive theory, a real-time update strategy for the neural network weights was designed to ensure estimation convergence and accuracy. Furthermore, the hypersonic vehicle control system was decomposed into velocity and altitude subsystems. The altitude subsystem, which contains more state variables, was simplified using backstepping theory. According to sliding mode control theory, control laws for both subsystems were designed, and the asymptotic convergence of the designed controllers was proven based on Lyapunov stability theory, ensuring the stability of the control strategy. Simulation results show that under complex disturbance conditions, the proposed method enables accurate tracking of key state parameters such as velocity and altitude, outperforming traditional methods and significantly improving both stability and control precision of the hypersonic vehicle control system.

关键词

高超声速飞行器 / 径向基函数神经网络 / 滑模控制 / 自适应理论 / 控制律

Key words

hypersonic vehicle / radial basis function neural network / sliding mode control / adaptive theory / control law

参考文献

[1] MORELLI E A. Flight-test experiment design for characterizing stability and control of hypersonic vehicles [J]. Journal of Guidance, Control, and Dynamics, 2009, 32(3): 949-959.

[2] BERTIN J J, CUMMINGS R M. Fifty years of hypersonics: where we've been, where we're going [J]. Progress in Aerospace Sciences, 2003, 39(6-7): 511-536.

[3] PRELLER D, SMART M K. Longitudinal control strategy for hypersonic accelerating vehicles [J]. Journal of Spacecraft and Rockets, 2015, 52(3): 993-999.

[4] VOLAND R T, HUEBNER L D, MCCLINTON C R. X-43A hypersonic vehicle technology development [J]. Acta Astronautica, 2006, 59(1-5): 181-191.

[5] HUANG L, DUAN Z, YANG J. Challenges of control science in near space hypersonic aircrafts [J]. Control Theory & Applications, 2011, 28(10): 1496-1505.

[6] 罗金玲, 李超, 徐锦. 高超声速飞行器机体/推进一体化设计的启示[J]. 航空学报, 2015, 36(1): 39-48.

[7] LUO Jinling, LI Chao, XU Jin. Inspiration of hypersonic vehicle with airframe/propulsion integrated design [J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(1): 39-48. (in Chinese)

[8] OPPENHEIMER M, SKUJINS T, CESNIK C, et al. Canard-elevon interactions on a hypersonic vehicle [R]. AIAA 2008-6383, 2008.

[9] XU H, MIRMIRANI M D, IOANNOU P A. Adaptive sliding mode control design for a hypersonic flight vehicle [J]. Journal of Guidance, Control, and Dynamics, 2004, 27(5): 829-838.

[10] 葛东明. 临近空间高超声速飞行器鲁棒变增益控制[D]. 哈尔滨:哈尔滨工业大学, 2011.

[11] GE Dongming. Robust gain-scheduling control of hypersonic vehicle in near space [D]. Harbin: Harbin Institute of Technology, 2011. (in Chinese)

[12] 方洋旺, 柴栋, 毛东辉, 等. 吸气式高超声速飞行器制导与控制研究现状及发展趋势[J]. 航空学报, 2014, 35(7): 1776-1786.

[13] FANG Yangwang, CHAI Dong, MAO Donghui, et al. Status and development trend of the guidance and control for air-breathing hypersonic vehicle [J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(7): 1776-1786. (in Chinese)

[14] DING Y, WANG X, BAI Y, et al. An improved continuous sliding mode controller for flexible air-breathing hypersonic vehicle [J]. International Journal of Robust and Nonlinear Control, 2020, 30(14): 5751-5772.

[15] ZHANG H, WANG P, TANG G, et al. Fuzzy disturbance observer-based dynamic sliding mode control for hypersonic morphing vehicles [J]. Aerospace Science and Technology, 2023, 142: 108633.

[16] 尹秋霖, 陈琦, 王中原, 等. 一种基于轨迹预测的滑翔制导炮弹时间约束末制导律扩展方法[J]. 弹道学报, 2024, 36(2): 21-28.

[17] YIN Qiulin, CHEN Qi, WANG Zhongyuan, et al. An extended time-constrained terminal guidance law for gliding-guided projectiles based on trajectory prediction [J]. Journal of Ballistics, 2024, 36(2): 21-28. (in Chinese)

[18] 王建敏, 吴云洁, 董小萌. 基于滑模干扰观测器的高超声速飞行器滑模控制[J]. 航空学报, 2015, 36(6): 2027-2036.

[19] WANG Jianmin, WU Yunjie, DONG Xiaomeng. Sliding mode control for hypersonic flight vehicle with sliding mode disturbance observer [J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(6): 2027-2036. (in Chinese)

[20] SACHAN K, PADHI R. Nonlinear robust neuro-adaptive flight control for hypersonic vehicles with state constraints [J]. Control Engineering Practice, 2020, 102: 104526.

[21] GUO J, GU X, GUO Z. Asymptotic adaptive tracking control for hypersonic vehicles with guaranteeing multi-performance requirements [J]. Aerospace Science and Technology, 2020, 105: 106025.

[22] WANG L, QI R, JIANG B. Adaptive actuator fault-tolerant control for non-minimum phase air-breathing hypersonic vehicle model [J]. ISA Transactions, 2022, 126: 47-64.

[23] 张天翼, 周军, 郭建国. 高超声速飞行器鲁棒自适应控制律设计[J]. 宇航学报, 2013, 34(3): 384-388.

[24] ZHANG Tianyi, ZHOU Jun, GUO Jianguo. A study on robust adaptive control method for hypersonic vehicle [J]. Journal of Astronautics, 2013, 34(3): 384-388. (in Chinese)

[25] 付昱, 宋文艳, 汪秋吟. 考虑模态转换的组合动力飞机任务性能快速评估方法[J]. 弹道学报, 2024, 36(1): 1-9.

[26] FU Yu, SONG Wenyan, WANG Qiuyin. A fast evaluation method for mission performance of combined cycle engine aircraft considering mode transition [J]. Journal of Ballistics, 2024, 36(1): 1-9. (in Chinese)

[27] CHANG S, LIU S, CHEN J. Based on RBF neural network of hypersonic re-entry vehicle attitude control [J]. Journal of Physics: Conference Series, 2022, 2213: 012003.

[28] AN H, GUO Z, WANG G, et al. Neural adaptive control of air-breathing hypersonic vehicles robust to actuator dynamics [J]. ISA Transactions, 2021, 116: 17-29.

[29] 赵贺伟, 杨秀霞, 沈如松, 等. 弹性高超声速飞行器预设性能精细姿态控制[J]. 兵工学报, 2017, 38(3): 501-511.

[30] ZHAO Hewei, YANG Xiuxia, SHEN Rusong, et al. Prescribed performance fine attitude control for aeroelastic hypersonic vehicle [J]. Acta Armamentarii, 2017, 38(3): 501-511. (in Chinese)

[31] 苏沛华, 毛奇. 基于神经网络补偿的高超声速飞行器滑模控制[J]. 传感器与微系统, 2018, 37(8): 76-78.

[32] SU Peihua, MAO Qi. Sliding mode control for hypersonic vehicle based on neural network compensation [J]. Transducers and Microsystem Technologies, 2018, 37(8): 76-78. (in Chinese)

[33] BOLENDER M A, DOMAN D B. Nonlinear longitudinal dynamical model of an air-breathing hypersonic vehicle [J]. Journal of Spacecraft and Rockets, 2007, 44(2): 374-387.

[34] POLYCARPOU M M, IOANNOU P A. A robust adaptive nonlinear control design [C] // Proceedings of 1993 American Control Conference. San Francisco, CA: IEEE, 1993.

[35] BUCAK S S, JIN R, JAIN A K. Multiple kernel learning for visual object recognition: a review [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36(7): 1354-1369.

[36] GU Y, LIU T, JIA X, et al. Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3235-3247.

[37] TUIA D, CAMPS-VALLS G, MATASCI G, et al. Learning relevant image features with multiple-kernel classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3780-3791.

[38] VARMA M, BABU B R. More generality in efficient multiple kernel learning [C] // Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec: Association for Computing Machinery, 2009.

[39] PARKER J T, SERRANI A, YURKOVICH S, et al. Control-oriented modeling of an air-breathing hypersonic vehicle [J]. Journal of Guidance, Control, and Dynamics, 2007, 30(3): 856-869.

[40] BU X, WEI D, HE G. A robust constrained control approach for flexible air-breathing hypersonic vehicles [J]. International Journal of Robust and Nonlinear Control, 2020, 30(7): 2752-2776.

基金资助

国家自然科学基金(52202475)