基于MPC-PIO的无人飞行器集群编队重构控制

廖剑; 高向阳; 闫实; 周绍磊; 王东来; 康宇航

doi:10.13700/j.bh.1001-5965.2022.0398

基于MPC-PIO的无人飞行器集群编队重构控制

doi: 10.13700/j.bh.1001-5965.2022.0398

1.
赣南师范大学物理与电子信息学院，赣州 341000
2.
中国科学院深圳先进技术研究院，深圳 518055
3.
海军航空大学航空基础学院，烟台 264001
4.
北海舰队，青岛 266000

基金项目: 江西省教育厅科学技术研究项目(GJJ201410)；江西省重点研发计划(20203BBF63043)

详细信息

通讯作者:
E-mail：yh.kang1@siat.ac.cn

中图分类号: V249.1；TP273
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-20
- 录用日期: 2022-06-10
- 网络出版日期: 2022-08-16
- 整期出版日期: 2024-05-29

Formation reconfiguration control of UAV swarm based on MPC-PIO

1.
School of Physics and Electronic Information，Gannan Normal University，Ganzhou 341000，China
2.
Shenzhen Institute of Advanced technology，Chinese Academy of Sciences，Shenzhen 518055，China
3.
School of Aviation Basics，Naval Aviation University，Yantai 264001，China
4.
North Sea Fleet，Qingdao 266000，China

Funds: Science & Technology Project of Jiangxi Educational Committee (GJJ201410); Key Research and Development Programs of Jiangxi Province (20203BBF63043)

More Information

Corresponding author: E-mail：yh.kang1@siat.ac.cn

摘要

摘要:
针对存在各种障碍条件下的战场环境，为实现无人飞行器集群安全避障并进行快速精确打击，集群必须具备自主队形重构的能力。因此，建立了无人飞行器运动模型与领航跟随集群编队控制结构，并提出基于模型预测控制(MPC)框架的无人飞行器集群编队重构控制代价函数、避障代价函数及避碰代价函数，进一步运用鸽群优化(PIO)算法对重构问题进行优化求解。基于数值对比仿真实验结果，所提算法在集群编队跟踪误差和寻优速度方面表现出色。结果表明：所提算法能实现集群自主重构，并提高MPC方法的效率。
- 无人飞行器集群 /
- 编队重构控制 /
- 模型预测控制 /
- 鸽群优化 /
- 避障
Abstract:
To realize security and accurate strikes under the battlefield environment with various obstacles, unmanned aerial vehicle swarm must possess the ability of self-formation reconfiguration. The unmanned aerial vehicle movement model and leader follower swarm formation control structure are established. The cost functions of unmanned aerial vehicle swarm formation control, obstacle avoidance and collision avoidance are proposed based upon the model predictive control (MPC) framework. The pigeon inspired optimization (PIO) algorithm is used to optimize the swarm formation reconfiguration control. Based on the results of numerical comparative simulations, the proposed algorithm has shown excellent performance in formation tracking error and optimization speed. The results indicate that the proposed algorithm is able to achieve autonomous formation reconstruction and significantly enhance the efficiency of the MPC method.
- unmanned aerial vehicle swarm /
- formation reconfiguration control /
- model predictive control /
- pigeon inspired optimization /
- obstacle avoidance

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图 1 集群编队控制结构

Figure 1. Control structure of UAV formation

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图 2 距离-角度编队示意图

Figure 2. Diagram of distance-angle formation

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图 3 无人飞行器避障

Figure 3. Obstacle avoidance of UAV

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图 4 安全避碰保护区

Figure 4. Protected area for safe collision avoidance

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图 5 集群避碰流程

Figure 5. Flow chart of swarm collision avoidance

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图 6 无人飞行器集群飞行轨迹

Figure 6. Flight track of UAV swarm

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图 7 位置跟踪误差曲线

Figure 7. Error curves of tracking position

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图 8 速度跟踪误差曲线

Figure 8. Error curves of tracking velocity

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图 9 航向角跟踪误差曲线

Figure 9. Error curves of tracking course angle

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图 10 滚转角跟踪误差曲线

Figure 10. Error curves of tracking roll angle

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图 11 PIO与PSO迭代对比曲线

Figure 11. Curves of iterative comparison between PIO and PSO

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表 1 无人飞行器初始状态

Table 1. Initial state of UAV

无人飞行器	$\left( {x,y} \right)/{\mathrm{km}}$	$ v/ ( {{\mathrm{m}} \cdot {{\mathrm{s}}^{ - 1}}} ) $	$ \chi /\left( ^\circ \right) $	$ \alpha /\left( ^\circ \right) $
无人飞行器1	$\left( {3.8,4.1} \right)$	$160$	90	0
无人飞行器2	$ \left( {2.8,4.9} \right) $	$199$	85	0
无人飞行器3	$ \left( {3.2,3.4} \right) $	$202$	91	0
无人飞行器4	$ \left( {1.8,6.5} \right) $	$201$	100	0
无人飞行器5	$ \left( {1.9,1.7} \right) $	$196$	82	0

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表 2 集群编队期望构型

Table 2. Expected configuration of swarm formation

编队期望构型	$ {\rho ^{\mathrm{d}}}/{\mathrm{m}} $	$ {\theta ^{\mathrm{d}}}/\left( ^\circ \right) $
无人飞行器1-2	500	30
无人飞行器1-3	500	−30
无人飞行器1-4	1000	30
无人飞行器1-5	1000	−30

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表 3 领导者无人飞行器预定飞行速度与滚转角

Table 3. Pre-set speed and roll angle of leader UAV

时间段/s	$ {v_{\mathrm{L}}}/ ( {{\mathrm{m}} \cdot {{\mathrm{s}}^{ - 1}}} ) $	$ {\alpha _{\mathrm{L}}}/\left( ^\circ \right) $
0～74.5	$160$	0
75～124.5	$160$	−20
125～200	$160$	0

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表 4 约束条件

Table 4. Constraint condition

${v_{\min }}$/(m·s⁻¹)	v_max/(m·s⁻¹)	$\Delta {v_{\max }}$/(m·s⁻²)	${\chi _{\min }}$/(°)	${\chi _{\max }}$/(°)	$\Delta {\chi _{\max }}$/((°)·s⁻¹)	${\alpha _{\min }}$/(°)	${\alpha _{{{\mathrm{m}}} {\text{ax}}}}$/(°)	$\Delta {\alpha _{\max }}$/((°)·s⁻¹)
$120$	$240$	$50$	$0$	$360$	$15$	$ - 50$	$50$	$100$

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表 5 无人飞行器模型参数与避障避碰参数

Table 5. Model parameters of UAV model & parameters of obstacle avoidance and collision avoidance

${\beta _v}$	${\beta _\alpha }$	$ {r_{{j_1}}} $	$ {r_{{j_2}}} $
3.0	0.6	0.4	0.4

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表 6 MPC、PIO及PSO算法参数

Table 6. Parameters of MPC, PIO and PSO

算法	$ {{\boldsymbol{Q}}_{{\text{F1}}}} $	$ {{\boldsymbol{Q}}_{{\text{F2}}}} $	$ {{\boldsymbol{Q}}_{{\text{F3}}}} $	$ {{\boldsymbol{Q}}_{{\text{F4}}}} $	$ {{\boldsymbol{Q}}_{{\text{F5}}}} $	$ {{\boldsymbol{R}}_{{\text{F1}}}} $
MPC	diag[0.4 0.4 0.05]	diag[0.1 0.1 0.04]	diag[0.1 0.1 0.03]	diag[0.1 0.1 0.02]	diag[0.1 0.1 0.01]	diag[0.0004 0.0004]
PIO
PSO

算法	$ {{\boldsymbol{R}}_{{{\text{F}}_{\text{2}}}}} $	$ {{\boldsymbol{R}}_{{\text{F3}}}} $	$ {{\boldsymbol{R}}_{{\text{F4}}}} $	$ {N_{{\text{r}} 1 {\mathrm{max}} }} $	$ {N_{{\text{r2max}}}} $	$ P $
MPC	diag[0.0003 0.0003]	diag[0.0002 0.0002]	diag[0.0001 0.0001]
PIO				300	30	0.2
PSO

算法	$ {N_{{\mathrm{P}} - {\mathrm{pio}}}} $	$ {N_{{\mathrm{P}} - {\mathrm{pso}}}} $	$ {N_{\mathrm{c}}} $	$ {w_{{\mathrm{pso}}}} $	$ {c_1} $	$ {c_2} $
MPC
PIO	120
PSO		120	500	0.8	2.0	2.0

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