带有神经网络干扰观测器的视线角约束制导

doi:10.12305/j.issn.1001-506X.2024.04.26

Abstract

Abstract:

Aiming at the problem of maneuvering target interception with terminal line-of-sight (LOS) angle constraint, a LOS angle constraint guidance method based on radial basis function (RBF) neural network interference observer is proposed. Firstly, considering that the acceleration information cannot be obtained during target maneuvering process, an interference observer based on RBF neural network is presented, which realizes high-precision estimation of target maneuvering. Secondly, an improved sliding mold guidance law is designed by introducing the power term by fully considering the terminal angle constraint and combining the idea of super-twisting algorithm, so as to effectively improve the guidance accuracy under limited overload conditions. On this basis, the convergence and stability of the algorithm are proved by Lyapunov's theorem. Finally, the guidance performance of three different methods in four interception scenarios is compared through simulation verification, and Monte Carlo simulation is given for the proposed method, and the simulation results show that the LOS angle constraint guidance law given in this paper has high accuracy and strong robustness for maneuvering target interception.

Key words: improved sliding mold guidance law, line-of-sight (LOS) angle constraint, radial basis function (RBF) neural networks, interference observers

CLC Number:

V488.133

Tong HE, Qing LU, Jun ZHOU, Zongyi GUO. Line-of-sight angle constraint guidance with neural network interference observer[J]. Systems Engineering and Electronics, 2024, 46(4): 1372-1382.

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场景编号	目标加速度	终端角度约束
1	a_tx=0 a_ty=1.5cos(0.1t)+Δa_ty a_tz=1.5cos(0.1t)+Δa_tz	q_εf=20° q_βf=－45°
2	a_tx=0 a_ty=1.5cos(0.1t)+Δa_ty a_tz=1.5cos(0.1t)+Δa_tz	q_εf=15° q_βf=30°
3	a_tx=0 a_ty=0+Δa_ty a_tz=0+Δa_tz	q_εf=20° q_βf=－45°
4	a_tx=0 a_ty=0+Δa_ty a_tz=0+Δa_tz	q_εf=15° q_βf=30°

场景编号	关键参数	方法1	方法2	方法3
1	T/s	38.848 0	34.278 0	37.355 0
	Δq_ε/(°)	0.048 0	10.115 3	0.002 1
	Δq_β/(°)	26.809 8	32.415 1	1.804 8
2	T/s	37.811 0	34.434 0	38.901 0
	Δq_ε/(°)	0.018 3	6.087 82	0.004 9
	Δq_β/(°)	23.769 5	28.142 6	0.134 5
3	T/s	34.650 0	32.323 0	36.342 0
	Δq_ε/(°)	1.021 9	10.455 8	0.012 5
	Δq_β/(°)	11.141 6	33.160 0	1.115 6
4	T/s	33.686 0	32.211 0	34.714 0
	Δq_ε/(°)	0.362 5	6.381 86	0.011 0
	Δq_β/(°)	12.480 9	28.833 9	1.876 5

参数	方法
参数	RBF	SMO
T_m/s	0.611 0	3.959 0
err₁	0.021 3	0.022 2

参数	方法
参数	RBF	SMO
T_n/s	1.159 0	4.097 0
err₂	0.003 2	0.010 1

参数	方法
参数	方法1	方法2	方法3
平均脱靶量/m	1.302 49	1.068 32	1.026 01
平均纵向角误差/(°)	0.395 95	9.140 70	0.161 16
平均侧向角误差/(°)	20.222 20	32.714 50	7.592 64

Line-of-sight angle constraint guidance with neural network interference observer

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