基于GA-BP的最优前置角滑模控制算法

宋炣; 杜昌平; 郑耀

doi:10.15892/j.cnki.djzdxb.2025.01.002

弹箭与制导学报 >

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

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

基于GA-BP的最优前置角滑模控制算法

宋炣 ^,¹ ,
杜昌平 ^,² ,
郑耀 ²

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¹ 浙江大学工程师学院,浙江杭州 310015
² 浙江大学航空航天学院,浙江杭州 310007

杜昌平(1978—),男,副教授,博士。E-mail: duchangping@zju.edu.cn

宋炣(2001—),男,硕士研究生。E-mail: 22360044@zju.edu.cn

收稿日期: 2024-10-23

网络出版日期: 2025-03-12

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

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

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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

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摘要

对于导弹精确制导任务,提出一种基于GA-BP(genetic algorithm,GA;back propagation,BP;GA-BP)最优前置角的分段改进滑模控制算法。首先针对固定前置角滑模控制依赖前置角数值且难以预先确定的问题,建立了一个GA-BP神经网络,用于估计具体任务模型下的最优前置角。然后根据剩余时间的估计值设计分段滑模趋近律,得到分段改进滑模控制算法,以期提高制导控制过程的鲁棒性。进而构成完整的基于GA-BP最优前置角的分段改进滑模控制算法。最后对算法进行了仿真分析,结果表明:相较固定前置角的滑模控制算法,本算法平均可以减少约5%的任务时间和12%左右的总体过载,最高可以减少30%的任务时间,具有更高的优越性。

关键词： 制导; 前置角; 滑模控制; GA-BP; 数据预测

本文引用格式

宋炣 , 杜昌平 , 郑耀 . 基于GA-BP的最优前置角滑模控制算法[J]. 弹箭与制导学报, 2025 , 45(1) : 8 -16 . DOI: 10.15892/j.cnki.djzdxb.2025.01.002

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

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