Distributed cooperative guidance strategy based on virtual negotiation and rolling horizon optimization
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摘要:
针对目标机动条件下的多无人机协同制导问题，提出基于虚拟协商机制与滚动时域优化的分布式协同制导策略。为同时改善时间协调与导引控制效果，在滚动时域优化框架下设计局部协调变量与目标函数，构建有限时域内的多约束协同制导优化模型；为增强制导系统对机动目标的鲁棒性，设计基于非线性自回归神经网络的干扰观测器；针对无人机之间的控制输入耦合，提出虚拟协商机制，提出虚拟协商机制，实现同步决策、排除内部“分歧”，并利用Nesterov动量的均方根传播算法设计制导指令生成策略；分别进行数字仿真与半实物仿真实验。结果表明：所提策略可在线预测与优化，应对各类干扰，提升协同制导效果，并会在实际任务中得到进一步的应用与检验。
Abstract:The multi-UAV cooperative guidance under target maneuvering is studied, and a distributed cooperative guidance strategy based on virtual negotiation mechanism and rolling horizon optimization is proposed. To improve the effect of time coordination and guidance control at the same time, the local coordination variables and objective function are designed under the rolling horizon optimization framework. The multi-constraint cooperative guidance optimization model is then constructed in the finite time domain. To enhance the robustness of the guidance system to maneuvering targets, a disturbance observer based on nonlinear autoregressive neural network is designed. To address the control input coupling between UAVs, a virtual negotiation mechanism is proposed, realizing synchronous decision-making and eliminating internal “differences”. The guidance command generation strategy is also designed by using the root mean square propagation algorithm of Nesterov momentum. The experiments of digital simulation and hardware in the loop simulation are carried out. The results show that the proposed guidance strategy can deal with various system interferences and improve the effectiveness of the cooperative guidance through online prediction and optimization, thus expected to be further applied and tested in practical projects.
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图 1 无人机i和目标的三维运动示意图
Figure 1. Three-dimensional motion diagram of ${\text{UA}}{{\text{V}}i}$ and target
图 2 基于RHC与VN的分布式协同制导流程
Figure 2. Flow of distributed cooperative guidance based on RHC and VN
图 3 各无人机通信拓扑结构及初始状态示意图
Figure 3. Schematic diagram of communication topology and initial state of each UAV
图 6 剩余攻击时间与平均剩余攻击时间之差变化情况
Figure 6. Variation of difference between time to go and average time to go
图 7 VN-RHC协同制导策略下无人机制导指令变化情况
Figure 7. Variation of UAV guidance command under VN-RHC cooperative guidance strategy
图 13 无人机-目标初始状态及无人机通信拓扑结构
Figure 13. Initial state of UAVs and target and communication topology of UAVs
图 14 半实物仿真实验无人机、目标三维运动轨迹
Figure 14. Three-dimensional motion trajectory of UAVs and target in hardware in loop simulation
表 1 目标初始状态
Table 1. Target initial state
x/m y/m z/m 航迹倾斜角/rad 航迹偏转角/rad 空速/(m·s−1) 1500 800 1500 0 3π/4 20 
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表 2 无人机及目标性能参数
Table 2. Performance parameters of UAVs and target
无人机/
目标最大航迹
倾斜角速度/
(rad·s−1)最大航迹
偏转角速度/
(rad·s−1)最大加速度/
(m·s−2)最大空速/
(m·s−1)最小空速/
(m·s−1)无人机1 0.05 0.05 1 30 20 无人机2 0.05 0.05 1 30 20 无人机3 0.05 0.05 1 30 20 无人机4 0.05 0.05 1 30 20 目标 0.05 0.05
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表 3 2种协同制导策略的制导效果
Table 3. Guidance performance of two cooperative guidance strategies
制导策略 总脱靶量/m 总攻击时间误差/s 纵向随机常值机动、
侧向随机正弦机动纵向随机方波机动、
侧向随机常值机动纵向随机正弦机动、
侧向随机方波机动纵向随机常值机动、
侧向随机正弦机动纵向随机方波机动、
侧向随机常值机动纵向随机正弦机动、
侧向随机方波机动VN-RHC 0.0063 0.0024 0.1439 3.936×10−5 1.127×10−5 6.593×10−4 APN-CC 0.0513 0.0447 0.9027 0.001 0.001 0.01
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表 4 不同算法参数下的仿真结果
Table 4. Simulation results under different algorithm parameters
仿真
次序NARnet最大单步
训练次数无人机最大单步
协商次数平均
脱靶量/m平均攻击
时间误差/s平均单步
训练次数平均单步
协商次数无人机平均
单步决策用时/s计算总时长/s 1 100 100 0.0143 6.193×10−5 19 28 0.0178 112 2 100 50 0.0133 6.386×10−5 19 21 0.0151 95.0469 3 100 20 0.0220 2.432×10−4 19 13 0.0125 79.2656 4 50 100 0.0151 7.838×10−5 18 28 0.0181 114.0781 5 50 50 0.0127 5.917×10−5 18 20 0.0149 93.8594 6 50 20 0.0193 1.750×10−4 18 13 0.0121 76.4375
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