基于元学习与预测控制的导弹自适应制导

doi:10.12305/j.issn.1001-506X.2026.07.26

Abstract

Abstract:

To address the complex impacts of aerodynamic perturbations, target maneuvers, actuator failures, and missile parameter variations, this paper proposes an adaptive guidance framework based on deep reinforcement learning （DRL）. First, the framework employs entropy-regularized DRL, utilizing a deep neural network （DNN） as the predictive model. By integrating a forgetting mechanism into meta-learning, it dynamically adjusts experience replay weights to prioritize the current environment. Subsequently, a dual-model predictive control architecture is designed, combining cross-entropy method model predictive control （CEM-MPC） and adaptive entropy model predictive path integral （MPPI） control. This architecture monitors the rate of change in cost standard deviation and automatically switches control strategies when fluctuations exceed a threshold, thereby leveraging the convergence speed and exploration advantages of both controllers. Simulations demonstrate that compared to traditional learning-based non-adaptive methods, the proposed approach stabilizes line-of-sight angular rates and improves interception success rates by 47.6% and 6.0% respectively, validating its effectiveness.

Key words: reinforcement learning, deep neural network (DNN), meta-learning, model predictive control, missile guidance

CLC Number:

TJ 765.3

Qiang LI, Di ZHOU, Siyuan LI, Yurong LIN. Adaptive guidance for missile based on meta-learning and predictive control[J]. Systems Engineering and Electronics, 2026, 48(7): 2424-2433.

Figures/Tables 20

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References 30

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损失	最小损失	最大损失	平均损失
取值	0.038 014 20	0.626 501 79	0.056 630 45

方法	响应时间
方法1	2.782
方法2	3.031
方法3	2.188
方法4	2.635

参数	值
导弹−目标距离/m	3 000
导弹仰角/rad	−0.21
导弹偏角/rad	−0.16
视线仰角/rad	−0.61
视线偏角/rad	0.60
目标仰角/rad	−0.14
目标偏角/rad	−0.23
导弹初始速度/（m/s）	1000
目标初始速度/（m/s）	300
目标y轴加速度/（m/s⁻²）	30sin（t）
目标z轴加速度/（m/s⁻²）	30sin（t）
执行器故障范围	（−0.3,0）
执行器故障发生时间/s	2.5
零升阻力波动范围/%	（−3,3）
诱导阻力波动范围/%	（−3,3）
大气密度波动范围/%	（−3,3）
推力持续时间/s	3.0

方法	网络	控制器
方法1	本文采用的带有熵正则化的网络	本文采用的双模策略控制器
方法2	本文采用的带有熵正则化的网络	本文改进的CEM-MPC
方法3	基于学习的在线预测方法	未改进的MPPI控制

方法	拦截耗时/s	脱靶量/m	末端速度/（m/s）
方法1	4.633 6	0.000 56	818.19
方法2	4.619 1	0.080 55	820.26
方法3	4.626 6	0.004 69	819.13

Adaptive guidance for missile based on meta-learning and predictive control

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参数	值
导弹−目标距离/m	3 300
导弹仰角/rad	（−0.18,0.18）
导弹偏角/rad	（−0.21,0.21）
视线仰角/rad	（−0.6,−0.4）
视线偏角/rad	（0.65,0.85）
目标仰角/rad	（−0.17,0.17）
目标偏角/rad	（−0.35,0.35）
导弹初始速度/（m/s）	1 000
目标初始速度/（m/s）	300
目标y轴加速度/（m/s⁻²）	（28sin t,30sin t）
目标z轴加速度/（m/s^-2）	（28sin t,30sin t）
执行器故障范围	（−0.3,0）
执行器故障发生时间/s	2.5
零升阻力波动范围/%	（−3,3）
诱导阻力波动范围/%	（−3,3）
大气密度波动范围/%	（−3,3）
推力持续时间/s	3.0

方法	脱靶量/m				末端速度均值/（m/s）	拦截成功率/%
方法	均值	方差	最大值	最小值	末端速度均值/（m/s）	拦截成功率/%
方法1	0.002 728 88	0.000 052 71	0.076 637 21	0.000 000 12	769.196	99.8
方法2	0.071 489 16	0.004 531 86	0.443 361 43	0.000 004 41	768.374	52.2
方法3	0.008 132 62	0.000 339 12	0.150 252 79	0.000 000 78	771.920	93.8

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