故障条件下基于同步DMPC的多无人机分组编队控制

doi:10.12305/j.issn.1001-506X.2025.07.28

Abstract

Abstract:

Aiming at the grouping formation control (GFC) problem of multi-unmanned aerial vehicle (UAV) under fault conditions, a GFC algorithm based on synchronous distributed model predictive control (DMPC) is proposed. Firstly, a grouping and layering control framework considering virtual leaders is established. Secondly, the irreparable fault in the formation is modeled, and an interactive health discrimination mechanism and "fault isolation" strategy are proposed. Combining the grouping and layering control framework, a GFC scheme under fault conditions is further proposed. Thirdly, combining synchronous DMPC theory with the fault model, a single group formation control algorithm under fault conditions is designed based on the "fault isolation" strategy. Furthermore, a GFC algorithm is proposed according to the control scheme, and the stability of the formation system under the GFC algorithm is proved based on Lyapunov theory. Finally, the effectiveness and superiority of the designed algorithms under fault conditions are verified through simulation.

Key words: unmanned aerial vehicle (UAV) formation, grouping formation control, irreparable fault, synchronous distributed model predictive control (DMPC)

CLC Number:

V249

Yunfeng HE, Xianjun SHI, Jianhua LU, Chaolun ZHAO, Guorong ZHAO. Multi-UAV grouping formation control based on synchronous DMPC under fault conditions[J]. Systems Engineering and Electronics, 2025, 47(7): 2357-2370.

Figures/Tables 10

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

References 30

1	LI X Q , LU X P , CHEN W H , et al. Research on UAVs reconnaissance task allocation method based on communication preservation[J]. IEEE Trans.on Consumer Electronics, 2024, 70 (1): 684- 695.
2	ZHANG B Q , WANG T , LI M X , et al. Multiple unmanned aerial vehicle (multi-UAV) reconnaissance and search with limi-ted communication range using semantic episodic memory in reinforcement learning[J]. Drones, 2024, 8 (8): 393.
3	HU J Q , WU H S , ZHAN R J , et al. Self-organized searchattack mission planning for UAV swarm based on wolf pack hunting behavior[J]. Journal of Systems Engineering and Electronics, 2021, 32 (6): 1463- 1476. doi: 10.23919/JSEE.2021.000124
4	YU Z Q , ZHANG Y M , JIANG B , et al. A review on faultto-lerant cooperative control of multiple unmanned aerial vehicles[J]. Chinese Journal of Aeronautics, 2022, 35 (1): 1- 18.
5	QIN B Y , ZHANG D , TANG S , et al. Two-layer formation-containment fault-tolerant control of fixed-wing UAV swarm for dynamic target tracking[J]. Journal of Systems Engineering and Electronics, 2023, 34 (6): 1375- 1396. doi: 10.23919/JSEE.2023.000153
6	LI J C , LIU J M , CAO G Y , et al. Leader-follower formation of light-weight UAVs with novel active disturbance rejection con trol[J]. Applied Mathematical Modelling, 2023, 117, 577- 591.
7	YAN X , JIANG D P , MIAO R L , et al. Formation control and obstacle avoidance algorithm of a multi-USV system based on virtual structure and artificial potential field[J]. Journal of Marine Science and Engineering, 2021, 9 (2): 161.
8	GARCIA A P , DEL C J , BARRIENTOS A . Behavior-based control for an aerial robotic swarm in surveillance missions[J]. Sensors, 2019, 19 (20): 4584. doi: 10.3390/s19204584
9	SUN J Y , TANG J , LAO S Y . Collision avoidance for cooperative uavs with optimized artificial potential field algorithm[J]. IEEE Access, 2017, 5, 18382- 18390.
10	ZHANG Z Y , DAI W , LI G Y , et al. Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol[J]. Journal of Computer Applications, 2023, 43 (8): 2644- 2650.
11	TANG J , DUAN H B , LAO S Y . Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review[J]. Artificial Intelligence Review, 2023, 56 (5): 4295- 4327.
12	ZHANG B Y , SUN X X , LIU S G , et al. Adaptive differential evolution-based distributed model predictive control for multi-UAV formation flight[J]. International Journal of Aeronautical and Space Sciences, 2020, 21 (2): 538- 548. doi: 10.1007/s42405-019-00228-8
13	RAWLINGS J B , MAYNE D Q , DIEHL M M . Model predictive control: theory, computation, and design[M]. 2nd ed Santa Barbara, California: Nob Hill Publishing, 2022: 89- 107.
14	何云风, 史贤俊, 卢建华, 等. 基于同步DMPC的无人机编队跟踪与避障控制[J]. 控制与决策, 2024, 39 (11): 3557- 3566. doi: 10.13195/j.kzyjc.2023.0945
	HE Y F , SHI X J , LU J H , et al. UAV formation tracking and obstacle avoidance control based on synchronous DMPC[J]. Control and Decision, 2024, 39 (11): 3557- 3566. doi: 10.13195/j.kzyjc.2023.0945
15	JANG D , SON C Y , YOO J , et al. Efficient networked UAV control using event-triggered predictive control[J]. IFAC-PapersOnLine, 2019, 52 (15): 412- 417.
16	LI K , BIAN Y , LI S E , et al. Distributed model predictive control of multi-vehicle systems with switching communication topologies[J]. Transportation Research Part C: Emerging Technologies, 2020, 118, 102717.
17	LIU A D , ZHANG W A , YU L , et al. Formation control of multiple mobile robots incorporating an extended state observer and distributed model predictive approach[J]. IEEE Trans.on Systems, Man, and Cybernetics: Systems, 2020, 50 (11): 4587- 4597.
18	XIAO G N , LUAN X L , LIU F . Observer-based distributed sensor fault-tolerant model predictive control for intermittent faults[J]. IFAC-PapersOnLine, 2021, 54 (3): 251- 256.
19	ZHANG B Y , SUN X X , LIU S G , et al. Distributed fault tolerant model predictive control for multi-unmanned aerial vehicle system[J]. Asian Journal of Control, 2022, 24 (3): 1273- 1292.
20	KUSHLEYEV A , MELLINGER D , POWERS C , et al. Towards a swarm of agile micro quadrotors[J]. Autonomous Robots, 2013, 35 (4): 287- 300.
21	CHEN H , WANG X K , SHEN L C , et al. Formation flight of fixed-wing UAV swarms: a group-based hierarchical approach[J]. Chinese Journal of Aeronautics, 2021, 34 (2): 504- 515.
22	SHI Y , HUA Y Z , YU J L , et al. Robust output group formation tracking control of heterogeneous multi-agent systems with multiple leaders using reinforcement learning[J]. Systems & Control Letters, 2024, 192, 105897.
23	HU C X , HUA Y Z , DONG X W , et al. Time-varying group formation tracking for multiagent systems with competition and cooperation via distributed nash equilibrium seeking[J]. IEEE Trans.on Industrial Informatics, 2024, 20 (8): 10054- 10064.
24	张毅, 于浩, 杨秀霞, 等. 异构无人集群分组编队自适应跟踪-合围控制[J]. 系统工程与电子技术, 2023, 45 (10): 3274- 3285. doi: 10.12305/j.issn.1001-506X.2023.10.32
	ZHANG Y , YU H , YANG X X , et al. Adaptive group formation tracking-containment control for heterogeneous unmanned swarm[J]. Systems Engineering and Electronics, 2023, 45 (10): 3274- 3285. doi: 10.12305/j.issn.1001-506X.2023.10.32
25	QUAN Q , FU R , CAI K Y . Practical control for multicopters to avoid non-cooperative moving obstacles[J]. IEEE Trans.on Intelligent Transportation Systems, 2021, 23 (8): 10839- 10857.
26	HE Y F , SHI X J , LU J H , et al. Grouping formation and obstacle avoidance control of UAV swarm based on synchronous DMPC[J]. International Journal of Aerospace Engineering, 2024, 2024 (1): 4934194.
27	LIU Z B , WANG L N , SONG Y C , et al. Fault diagnosis and accommodation for multi-actuator faults of a fixed-wing unmanned aerial vehicle[J]. Measurement Science and Technology, 2022, 33 (7): 075903.
28	席裕庚. 预测控制[M]. 2版北京: 国防工业出版社, 2013: 191- 213.
	XI Y G . Predictive control[M]. 2nd ed Beijing: National Defense Industry Press, 2013: 191- 213.
29	王祥科, 陈浩, 赵述龙. 大规模固定翼无人机集群编队控制方法[J]. 控制与决策, 2021, 36 (9): 2063- 2073.
	WANG X K , CHEN H , ZHAO S L . Formation control of large-scale fixed-wing unmanned aerial vehicle swarms[J]. Control and Decision, 2021, 36 (9): 2063- 2073.
30	陈浩. 复杂条件下固定翼无人机集群编队控制研究[D]. 长沙: 国防科技大学, 2022.
	CHEN H. Research on formation control of fixed-wing UAV swarms in complex enivronments[D]. Changsha: National University of Defense Technology, 2022.

变量	符号
最优控制输入/最优状态	$\boldsymbol{u}_i^(k+l \mid k) / \boldsymbol{z}_i^(k+l \mid k)$
可行控制输入/可行状态	$\boldsymbol{u}_i^{-}(k+l \mid k) / \boldsymbol{z}_i^{-}(k+l \mid k)$
假设控制输入/假设状态	$\hat{\boldsymbol{u}}_i(k+l \mid k) / \hat{\boldsymbol{z}}_i(k+l \mid k)$

[1]	Zeyang YIN, Hao LIANG, Yuxin LIAO, Xiaofang CHEN, Yongfang XIE. UAV formation path planning based on reachable envelope analysis of dynamic obstacle [J]. Systems Engineering and Electronics, 2025, 47(4): 1275-1284.
[2]	Jinxin LIU, Wei SHENG, Yumin ZHANG. Optimization algorithm for UAV formation consensus based on motion constraint [J]. Systems Engineering and Electronics, 2025, 47(1): 230-243.
[3]	Tianye SUN, Wei SUN, Jianjun WU. UAV formation rapid assembly method based on improved Quatre algorithm [J]. Systems Engineering and Electronics, 2022, 44(9): 2840-2848.
[4]	Xiaowei FU, Zihao CHEN. Cooperative capture control method for multi-UAV based on consensus protocol [J]. Systems Engineering and Electronics, 2021, 43(9): 2501-2507.
[5]	LIN Juncan, JIA Gaowei, HOU Zhongxi. Research on task assignment of heterogeneous UAV formation in the anti-radar combat [J]. Systems Engineering and Electronics, 2018, 40(9): 1986-1992.
[6]	LI Xiang, XING Qing-hua, DONG Tao. Deployment optimization of UAV formation based on multi-index orthogonal experimental design [J]. Journal of Systems Engineering and Electronics, 2013, 35(2): 331-337.

Multi-UAV grouping formation control based on synchronous DMPC under fault conditions

RichHTML

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 30

Related Articles 6

Recommended Articles

Metrics

Comments