Optimal Leading Angle Sliding Mode Control Algorithm Based on GA-BP

SONG Ke; DU Changping; ZHENG Yao

doi:10.15892/j.cnki.djzdxb.2025.01.002

Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 1: 8 - 16

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.01.002

Optimal Leading Angle Sliding Mode Control Algorithm Based on GA-BP

SONG Ke ^,¹ ,
DU Changping ^,² ,
ZHENG Yao ²

Expand

¹ School of Engineering, Zhejiang University, Hangzhou 310015, Zhejiang, China
² School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310007, Zhejiang, China

Received date: 2024-10-23

Online published: 2025-03-12

Fold

Abstract

For missile's precision guidance task, an optimal leading angle sliding mode control algorithm based on GA-BP (genetic algorithm-back propagation) is proposed, in which the sliding mode control is improved segmentally. Aiming at the problem of the fixed leading angle sliding mode control depending on a leading angle value, but it is difficult to be predefined, a GA-BP neural network is developed and used to estimate the optimal leading angle for a specific task model. Subsequently, based on the estimated value of remaining time, a piecewise sliding mode reaching law is designed, which lead to a segment-improved sliding mode control algorithm that further enhances robustness during the missile guidance process. And then the complete optimal leading angle segment-improved sliding mode algorithm based on GA-BP is formed. After the algorithm is simulated and analyzed, Simulation results indicate that, compared with the fixed leading angle sliding mode control algorithms, the proposed algorithm can reduce task time approximately 5% in average, and the overload around 12%. As it can reduce the task time by up to 30% in the maximum, so it has a higher superiority.

Key words： guidance; leading angle; sliding mode control; GA-BP; data forecasting

Cite this article

SONG Ke , DU Changping , ZHENG Yao . Optimal Leading Angle Sliding Mode Control Algorithm Based on GA-BP[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(1) : 8 -16 . DOI: 10.15892/j.cnki.djzdxb.2025.01.002

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	秦玉亮, 王建涛, 王宏强, 等. 弹载合成孔径雷达技术研究综述[J]. 信号处理, 2009, 25(4):630-635. QIN Y L, WANG J T, WANG H Q, et al. Review of missile-borne synthetic aperture radar technology[J]. Signal Processing, 2009, 25(4):630-635.

[2]	刘金琨, 孙富春. 滑模变结构控制理论及其算法研究与进展[J]. 控制理论与应用, 2007, 24(3):407-418. LIU J K, SUN F C. Research and progress of sliding mode variable structure control theory and its algorithms[J]. Control Theory and Applications, 2007, 24(3):407-418.

[3]	穆效江, 陈阳舟. 滑模变结构控制理论研究综述[J]. 控制工程, 2007, 14(S2):1-5. MU X J, CHEN Y Z. Overview of sliding mode variable structure control[J]. Control Engineering, 2007, 14(S2):1-5.

[4]	BRIERLEY S D, LONGCHAMP R. Application of sliding-mode control to air-air interception problem[J]. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(2): 306-325.

[5]	严鹏辉, 刘刚, 缪前树. 基于落角约束的偏置比例导引律的研究[J]. 现代防御技术, 2021, 49(6):49-54. YAN P H, LIU G, MIAO Q S. Research on bias proportional guidance law based on fall angle constraint[J]. Modern Defense Technology, 2019, 49(6):49-54.

[6]	郭鸿武, 林维菘, 刘明俊. 基于相对速度偏角的变结构导引律[J]. 宇航学报, 2001, 22(3):33-37. GUO H W, LIN W S, LIU M J. Variable structure guidance law based on relative velocity declination[J]. Journal of Astronautics, 2001, 22(3):33-37.

[7]	杨锁昌, 张宽桥, 陈鹏. 带攻击角度约束的自适应终端滑模导引律[J]. 北京航空航天大学学报, 2016, 42(8):1566-1574. YANG S C, ZHANG K Q, CHEN P. With attack angle constraint adaptive terminal sliding mode guidance law[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(8) : 1566-1574.

[8]	赵斌, 梁乐成, 蒋瑞民, 等. 终端角度约束制导及制导控制一体化方法综述[J]. 宇航学报, 2022, 43(5):563-579. ZHAO B, LIANG L C, JIANG R M, et al. Review of terminal angle constrained guidance and guidance control integration methods[J]. Journal of Astronautics, 2022, 43(5):563-579.

[9]	KIM M, GRIDER K V. Terminal guidance for impact attitude angle constrained flight trajectories[J]. IEEE Transactions on Aerospace and Electronic Systems, 1973 (6): 852-859.

[10]	KIM B, LEE J, HAN H, et al. Homing guidance with terminal angular constraint against nonmaneuvering and maneuvering targets[C]// AIAA Guidance, Navigation, and Control Conference. New Orleans: AIAA, 1997: 3474.

[11]	LYU S, ZHU Z H, TANG S, et al. Prescribed performance slide mode guidance law with terminal line-of-sight angle constraint against maneuvering targets[J]. Nonlinear Dynamics, 2017, 88: 2101-2110.

[12]	ZHANG W J, FU S N, LI W, et al. An impact angle constraint integral sliding mode guidance law for maneuvering targets interception[J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 168-184. DOI

[13]	HU Q, HAN T, XIN M. Sliding-mode impact time guidance law design for various target motions[J]. Journal of Guidance, Control, and Dynamics, 2019, 42(1): 136-148.

[14]	张宽桥, 张德锋, 王振兴, 等. 系统收敛时间和攻击角度控制的滑模导引律[J]. 弹道学报, 2024, 36(1):18-25. ZHANG K Q, ZHANG D F, WANG Z X, et al. Sliding mode guidance law for system convergence time and attack angle control[J]. Journal of Ballistics, 2019, 36(1):18-25.

[15]	王崇骏, 于汶滌, 陈兆乾, 等. 一种基于遗传算法的BP神经网络算法及其应用[J]. 南京大学学报(自然科学版), 2003, 39(5):459-466. WANG C J, YU W D, CHEN Z G, et al. A BP neural network algorithm based on genetic algorithm and its application[J]. Journal of Nanjing University (Natural Science Edition), 2003, 39(5):459-466.

[16]	WANG S, ZHANG N, WU L, et al. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method[J]. Renewable Energy, 2016, 94: 629-636.

[17]	XIONG S, WANG W, LIU X, et al. Guidance law against maneuvering targets with intercept angle constraint[J]. ISA Transactions, 2014, 53(4): 1332-1342. DOI PMID

[18]	马清华, 成高, 苗昊春, 等. 基于变结构控制的固定前置角制导律设计[J]. 计算机测量与控制, 2020, 28(8):165-170. MA Q H, CHENG G, MIAO H C, et al. Design of fixed lead angle guidance law based on variable structure control[J]. Computer Measurement and Control, 20, 28(8):165-170.

[19]	韩京清. 自抗扰控制技术[J]. 前沿科学, 2007(1):24-31. HAN J Q. Active disturbance rejection control technology[J]. Frontiers in Science, 2007(1):24-31.

[20]	WU S, ZHAO G, WU B. Real-time prediction of the mechanical behavior of suction caisson during installation process using GA-BP neural network[J]. Engineering Applications of Artificial Intelligence, 2022, 116: 105475.

[21]	DING S F, SU C Y, YU J Z. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review, 2011, 36(2): 153-162.

Options

Outlines