基于自适应扰动观测器的旋转弹神经网络过载驾驶仪设计

doi:10.12382/bgxb.2023.1085

摘要/Abstract

摘要：

旋转弹在飞行过程中受多种干扰的影响,包括跨域飞行气动参数剧烈变化引起的模型不确定性以及飞行过程中受到的外部扰动。为了解决高动态飞行环境中双通道旋转弹的鲁棒控制问题,基于轨迹线性化控制方法,设计伪逆反馈控制器。采用径向基函数神经网络,设计自适应前馈补偿控制器,有效实现对模型不确定性的精确逼近。将神经网络逼近误差和外部扰动处理为总扰动,并基于固定时间稳定理论设计一种自适应扰动观测器,实现对总扰动的精确估计及补偿。通过Lyapunov理论,严格证明了闭环系统的最终一致有界性。通过数值仿真验证了所设计方法的有效性。

关键词: 旋转弹, 双通道控制, 径向基函数神经网络, 自适应扰动观测器, 固定时间稳定理论

Abstract:

The spinning projectiles are influenced by various disturbances during flight, includingthe model uncertainties due to the drastic variations in aerodynamic parameters during cross-domain flight, as well as the external perturbations caused by external forces and moments. The research aims to address the robust control challenges of dual-channel spinning projectiles in high-dynamic flight environments. A pseudo-inverse feedback controller is designed based on the trajectory linearization control method, and an adaptive feedforward compensation controller is developed using radial basis function neural networks to accurately approximate the model uncertainties. Finally, an adaptive disturbance observer is designedby treating the neural network approximation errors and external disturbances as total disturbance based on the fixed-time stability theory, which is used to accurately estimate and compensate for total disturbance. The ultimate uniform boundness (UUB) of the closed-loop system is rigorously proven through Lyapunov theory. The effectiveness of the proposed methodology is illustrated through numerical simulations.

Key words: spinning projectile, dual-channel control, radial basis function neural network, adaptive disturbance observer, fixed-time stability theory

中图分类号:

王伟, 杨婧, 南宇翔, 李俊辉, 王雨辰. 基于自适应扰动观测器的旋转弹神经网络过载驾驶仪设计[J]. 兵工学报, 2024, 45(11): 3841-3855.

WANG Wei, YANG Jing, NAN Yuxiang, LI Junhui, WANG Yuchen. Design of a Neural Network Acceleration Autopilot for Spinning Projectile Based on Adaptive Disturbance Observer[J]. Acta Armamentarii, 2024, 45(11): 3841-3855.

图/表 20

图1 RBFNN示意图

Fig.1 Schematic diagram of RBFNN

图2 旋转弹示意图

Fig.2 Schematic diagram of spinning projectile

图3 过载驾驶仪结构

Fig.3 Frame work of acceleration autopilot

表1 观测器参数

Table 1 Parameters of observer

观测器类别	观测器形式	参数取值
FTDO	$z · 0$ =v₀-f₁-b₁u,v₀=-λ₂L¹^/³\|z₀-x\|²^/³sgn(z₀-x)+z₁ $z · 1$ =v₁,v₁=-λ₁L¹^/²\|z₁-v₀\|¹^/²sgn(z₁-v₀)+z₂ $z · 2$ =-λ₀Lsgn(z₂-v₁)	z_i(0),i=1,2 λ₀=1.1,λ₁=1.5,λ₂=2 L=50
FxTESO	$z · 1$ =f₁+b₁u+α₁si $g o 1$ (x₁-z₁)+β₁si $g θ 1$ (x₁-z₁) $z · 2$ =α₂si $g o 2$ (x₁-z₁)+β₂si $g θ 2$ (x₁-z₁)+γsign(x₁-z₁)	z_i(0)=0,i=1,2,α₁=3,β₁=15 α₂=1.5,β₂=7.5,o₁=0.9,θ₁=1.01 o₂=2o₁-1,θ₂=2θ₁-1,γ=50
AFxTSMDO	$z ˜$ =z-x, $z ·$ =-f₁-b₁u-κ(sig^p( $z ˜$ )+sig^q( $z ˜$ ))+v_d σ= $z ˜ ·$ +κ(sig^p( $z ˜$ )+sig^q( $z ˜$ )) $v · d$ =-κ(sig^p(σ)+sig^q(σ))-K(t)sgn(σ) $K ·$ (t)= $K ¯ σ \|, \| σ \| > ε 0, 其他$	z(0)=0,v_d(0)=0,κ=2,p=0.5 q=1.5,K(t)=0, $K ¯$ =10,ε=0.1

表1 观测器参数

Table 1 Parameters of observer

观测器类别	观测器形式	参数取值
FTDO	$z · 0$ =v₀-f₁-b₁u,v₀=-λ₂L¹^/³\|z₀-x\|²^/³sgn(z₀-x)+z₁ $z · 1$ =v₁,v₁=-λ₁L¹^/²\|z₁-v₀\|¹^/²sgn(z₁-v₀)+z₂ $z · 2$ =-λ₀Lsgn(z₂-v₁)	z_i(0),i=1,2 λ₀=1.1,λ₁=1.5,λ₂=2 L=50
FxTESO	$z · 1$ =f₁+b₁u+α₁si $g o 1$ (x₁-z₁)+β₁si $g θ 1$ (x₁-z₁) $z · 2$ =α₂si $g o 2$ (x₁-z₁)+β₂si $g θ 2$ (x₁-z₁)+γsign(x₁-z₁)	z_i(0)=0,i=1,2,α₁=3,β₁=15 α₂=1.5,β₂=7.5,o₁=0.9,θ₁=1.01 o₂=2o₁-1,θ₂=2θ₁-1,γ=50
AFxTSMDO	$z ˜$ =z-x, $z ·$ =-f₁-b₁u-κ(sig^p( $z ˜$ )+sig^q( $z ˜$ ))+v_d σ= $z ˜ ·$ +κ(sig^p( $z ˜$ )+sig^q( $z ˜$ )) $v · d$ =-κ(sig^p(σ)+sig^q(σ))-K(t)sgn(σ) $K ·$ (t)= $K ¯ σ \|, \| σ \| > ε 0, 其他$	z(0)=0,v_d(0)=0,κ=2,p=0.5 q=1.5,K(t)=0, $K ¯$ =10,ε=0.1

图4 算例1状态估计及误差边界

Fig.4 State estimationand tracking error bound for Case 1

图5 算例1扰动估计及误差边界

Fig.5 Disturbance estimation and tracking error bound for Case 1

图6 算例1自适应增益

Fig.6 Adaptive gain for Case 1

图7 算例2状态估计及误差边界

Fig.7 State estimationand tracking error bound for Case 2

图8 算例2扰动估计及误差边界

Fig.8 Disturbance estimation and tracking error bound for Case 2

图9 算例2自适应增益K(t)

Fig.9 Adaptive gain K(t) for Case 2

表2 气动参数

Table 2 Aerodynamic parameters of spinning projectile

参数	取值	参数	取值
b_L	1.1240	a_G	0.0791
b_M	7.667×10^-4	v/(m·s^-1)	1200
a_L	140.6137	k_s	10
a_M	-7.1831	μ_s	0.5
a_ω	2.4018	T_s/s	0.016
a_δ	-11.6520	τ/s	0.015

图10 双通道控制PI校正过载驾驶仪

Fig.10 Scheme of dual-channel controlled PI acceleration autopilot

图11 过载跟踪效果

Fig.11 Tracking result of acceleration

图12 过载跟踪误差

Fig.12 Accelerations tracking error

图13 姿态角速度跟踪效果

Fig.13 Tracking result of accelerations

图14 舵偏指令

Fig.14 Actuator deflection commands

图15 过载回路扰动估计

Fig.15 Disturbance estimation of acceleration loop

图16 姿态回路扰动估计

Fig.16 Disturbance estimation of attitude loop

图17 过载回路网络权重

Fig.17 Weights updating of the NN of acceleration loop

图18 姿态回路网络权重

Fig.18 Weights updating of the NN of attitude loop

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