基于结构分析与树种优化算法的无人机可重构性分析与设计

doi:10.12305/j.issn.1001-506X.2026.03.19

摘要/Abstract

摘要：

为了提高无人机系统的自主故障重构能力，提出一种基于结构分析和树种优化算法的可重构分析与设计策略。首先，基于结构分析，给出系统结构可重构的充要条件。其次，设计算法求取系统的可重构路径，建立故障重构矩阵，设计可重构性评价指标。然后，提出基于树种优化算法的可重构优化设计策略，兼顾了系统可重构需求以及可重构路径设计成本。最后，基于固定翼无人机模型，仿真验证所提方法的合理性和有效性。该策略不仅避免了复杂的数值运算，同时也为系统设计阶段的可重构性优化设计提供理论支撑。

关键词: 结构分析, 树种优化算法, 可重构性, 故障, 无人机

Abstract:

A reconfigurability analysis and design strategy is proposed based on structural analysis and tree-seed optimization algorithm to improve unmanned aircraft vehicle (UAV) systems autonomous fault reconfiguration capability. Firstly, based on the structural analysis, the sufficient conditions for the system structure to be reconfigurable are given. Secondly, the design algorithm finds the reconfigurable path of the system, establishes the fault reconfiguration matrix, and designs the reconfigurability evaluation indexes. Then, the reconfigurable optimal design strategy based on the tree-seed optimization algorithm is proposed, which takes into account the system reconfiguration requirements as well as the cost of the design of the reconfigurable path. Finally, based on the fixed-wing UAV model, the reasonableness and effectiveness of the proposed method are simulated and verified. The strategy avoids complex numerical operations and provides theoretical support for the optimal design of reconfigurability in the design phase of the system.

Key words: structural analysis, tree-seed algorithm (TSA), reconfigurability, fault, unmanned aerial vehicle (UAV)

中图分类号:

V 240.2

谷旭平, 史贤俊. 基于结构分析与树种优化算法的无人机可重构性分析与设计[J]. 系统工程与电子技术, 2026, 48(3): 932-945.

Xuping GU, Xianjun SHI. Reconfigurability analysis and design of UAV based on structural analysis and tree-seed optimization algorithm[J]. Systems Engineering and Electronics, 2026, 48(3): 932-945.

图/表 13

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

图11

图12

参考文献 31

3	NAPOLEONE A, POZZETTI A, MACCHI M. Core characteristics of reconfigurability and their influencing elements[J]. IFAC-PapersOnLine, 2018, 51 (11): 116- 121. doi: 10.1016/j.ifacol.2018.08.244
4	徐赫屿, 王大轶, 李文博. 卫星姿态控制系统的可重构性量化评价方法研究[J]. 航天控制, 2016, 34 (4): 29- 35. doi: 10.16804/j.cnki.issn1006-3242.2016.04.005
	XU H Y, WANG D Y, LI W B. Research on quantitative evaluation method of reconfigurability of satellite attitude control system[J]. Aerospace Control, 2016, 34 (4): 29- 35. doi: 10.16804/j.cnki.issn1006-3242.2016.04.005
5	YANG H, JIANG B, STAROSWIECKI M. Fault recoverability analysis of switched systems[J]. International Journal of Systems Science, 2012, 43 (3): 535- 542. doi: 10.1080/00207721.2010.517861
6	徐赫屿, 王大轶, 李文博. 一类带挠性附件卫星的控制系统可重构性评价方法[C]//中国自动化大会暨国际智能制造创新大会, 2018: 298-305.
	XU H Y, WANG D Y, LI W B. A class of control system reconfigurability evaluation methods with flexible attachment satellites[C]//Proc. of the China Automation Conference and International Conference on Intelligent Manufacturing Innovation, 2018: 298–305.
7	段文杰, 王大轶, 刘成瑞. 一种线性系统可重构控制分析方法[J]. 自动化学报, 2014, 40 (12): 2726- 2736.
	DUAN W J, WANG D Y, LIU C R. A reconfigurable control analysis method for linear systems[J]. Journal of Automation, 2014, 40 (12): 2726- 2736.
8	屠园园, 王大轶, 张香燕, 等. 航天器的可重构性与自主重构方法[J]. 航空学报, 2023, 44 (23): 135- 148.
	TU Y Y, WANG D Y, ZHANG X Y, et al. Reconfigurability of spacecraft and autonomous reconfiguration method[J]. Journal of Aeronautics, 2023, 44 (23): 135- 148.
9	申富媛, 李炜. 四旋翼无人机执行器可重构性量化评价方法研究[J]. 北京航空航天大学学报, 2020, 46 (11): 2077- 2086. doi: 10.13700/j.bh.1001-5965.2019.0568
	SHEN F Y, LI W. Research on quantitative evaluation method of reconfigurability of quadrotor UAV actuator[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46 (11): 2077- 2086. doi: 10.13700/j.bh.1001-5965.2019.0568
10	徐赫屿, 王大轶, 李文博, 等. 基于算子理论的系统可重构性评价与自主重构[J]. 航空学报, 2020, 41 (S1): 53- 60.
	XU H Y, WANG D Y, LI W B, et al. Reconfigurability evaluation and autonomous reconfiguration of systems based on operator theory[J]. Journal of Aeronautics, 2020, 41 (S1): 53- 60.
11	屠园园, 王大轶, 李文博. 考虑时间特性影响的控制系统可重构性定量评价方法研究[J]. 自动化学报, 2018, 44 (7): 1260- 1270. doi: 10.16383/j.aas.2018.c160752
	TU Y Y, WANG D Y, LI W B. Research on quantitative evaluation method of control system reconfigurability considering the effect of time characteristics[J]. Journal of Automation, 2018, 44 (7): 1260- 1270. doi: 10.16383/j.aas.2018.c160752
12	LIU F Q, MA Z X, MU B X, et al. Review on fault-tolerant control of unmanned underwater vehicles[J]. Ocean Engineering, 2023, 285, 115471. doi: 10.1016/j.oceaneng.2023.115471
13	MILECKI A, NOWAK P. Review of fault-tolerant control systems used in robotic manipulators[J]. Applied Sciences, 2023, 13 (4): 2675. doi: 10.3390/app13042675
14	GU X P, SHI X J. A review of research on diagnosability of control systems based on structural analysis[J]. Applied Sciences, 2023, 13 (22): 12241. doi: 10.3390/app132212241
15	CHENG Y, D’ARPINO M, RIZZONI G. Fault diagnosis in lithium-ion battery of hybrid electric aircraft based on structural analysis[C]//Proc. of the IEEE Transportation Electrification Conference & Expo, 2022: 997–1004.
16	CHEN Q, TIAN W F, CHEN W W, et al. Model-based fault diagnosis of an anti-lock braking system via structural analysis[J]. Sensors, 2018, 18 (12): 4468. doi: 10.3390/s18124468
17	PARK N J, KIM Y U, AHN H S, et al. Strong structural controllability and observability of individual nodes: a graph-theoretical approach[J]. IEEE Trans. on Automatic Control, 2024, 69 (4): 2598- 2604. doi: 10.1109/TAC.2023.3331241
18	MOHAMMADI A, SHEIKHOLESLAM F. Intelligent optimization: literature review and state-of-the-art algorithms （1965–2022）[J]. Engineering Applications of Artificial Intelligence, 2023, 126, 106959.
19	XU M H, CAO L, LU D W, et al. Application of swarm intelligence optimization algorithms in image processing: a comprehensive review of analysis, synthesis, and optimization[J]. Biomimetics, 2023, 8 (2): 235. doi: 10.3390/biomimetics8020235
1	AL-DOSARI K, HUNAITI Z, BALACHANDRAN W. Systematic review on civilian drones in safety and security applications[J]. Drones, 2023, 7 (3): 210. doi: 10.3390/drones7030210
2	MOHSAN S A H, KHAN M A, NOOR F, et al. Towards the unmanned aerial vehicles （UAVs）: a comprehensive review[J]. Drones, 2022, 6 (6): 147. doi: 10.3390/drones6060147
20	KIRAN M S. TSA: tree-seed algorithm for continuous optimization[J]. Expert Systems with Applications, 2015, 42 (19): 6686- 6698. doi: 10.1016/j.eswa.2015.04.055
21	BESKIRLI A, DAG I. Parameter extraction for photovoltaic models with tree seed algorithm[J]. Energy Reports, 2023, 9, 174- 185. doi: 10.1016/j.egyr.2022.10.386
22	KOSE E. Optimal control of AVR system with tree seed algorithm-based PID controller[J]. IEEE Access, 2020, 8, 89457- 89467. doi: 10.1109/ACCESS.2020.2993628
23	CINAR A C. Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm[J]. Arabian Journal for Science and Engineering, 2020, 45 (12): 10915- 10938. doi: 10.1007/s13369-020-04872-1
24	BESKIRLI A, TEMURTAS H, OZDEMIR D. Determination with linear form of Turkey’s energy demand forecasting by the tree seed algorithm and the modified tree seed algorithm[J]. Advances in Electrical and Computer Engineering, 2020, 20, 27- 34. doi: 10.4316/aece.2020.02004
25	GU X P, SHI X J. Diagnosability-optimized design of unmanned aerial vehicles based on structural analysis and maximum mean covariance differences[J]. Measurement, 2024, 238, 115334. doi: 10.1016/j.measurement.2024.115334
26	DULMAGE A L, MENDELSOHN N S. Coverings of bipartite graphs[J]. Canadian Journal of Mathematics, 1958, 10, 517- 534.
27	OROMI A, PUIG V, GALVE S, et al. Robust fault diagnosis using a data-based approach and structural analysis[J]. IFAC-PapersOnLine, 2022, 55 (6): 211- 216. doi: 10.1016/j.ifacol.2022.07.131
28	KRYSANDER M, FRISK E. Sensor placement for fault diagnosis[J]. IEEE Trans. on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2008, 38 (6): 1398- 1410. doi: 10.1109/systol.2016.7739840
29	KATOCH S, CHAUHAN SS, KUMAR V. A review on genetic algorithm: past, present, and future[J]. Multimedia Tools and Applications, 2021, 80 (5): 8091- 8126.
30	CUNHA M, MARQUES J. A new multiobjective simulated annealing algorithm—MOSA-GR: application to the optimal design of water distribution networks[J]. Water Resources Research, 2020, 56 (3): e2019WR025852. doi: 10.1029/2019WR025852
31	ABUALIGAH L, DIABAT A. Advances in sine cosine algorithm: a comprehensive survey[J]. Artificial Intelligence Review, 2021, 54 (4): 2567- 2608. doi: 10.1007/s10462-020-09909-3

函数表达式	极值	维度	自变量范围
$ {F_1}\left( x \right) = \displaystyle\sum\limits_{i = 1}^n {x_i^2} $	0	30	[−100,100]
$ {F_2}\left( x \right) = \displaystyle\sum\limits_{i = 1}^n {i \cdot x_i^4} + {\mathrm{rand}} $	0	30	[−5.12,5.12]

[1]	冯蕴雯, 吕世春, 柯倩云, 王锐, 刘晚移. 故障逻辑与贝叶斯时变评价网络融合的民机空调系统健康评估方法[J]. 系统工程与电子技术, 2026, 48(3): 859-871.
[2]	魏建林, 林彦超, 唐慧龙, 张旺, 王伟. 基于改进MCTS的多无人机多任务联合决策[J]. 系统工程与电子技术, 2026, 48(2): 556-568.
[3]	晏彪, 吴晓莉, 张蓝, 刘潇, 方泽茜, 韩炜毅, 李琦桉. 有人/无人机协同指挥员的事件相关电位特征[J]. 系统工程与电子技术, 2026, 48(2): 578-587.
[4]	张森, 庞岩, 周福亮. 基于改进Informed-RRT^*算法的无人机三维路径规划[J]. 系统工程与电子技术, 2026, 48(2): 660-668.
[5]	乔毅涛, 李爽. 空地异构无人系统固定时间事件触发编队包含控制[J]. 系统工程与电子技术, 2026, 48(2): 669-683.
[6]	李淑凤, 韩璐羽. 面向飞机表面巡检的多无人机覆盖路径规划[J]. 系统工程与电子技术, 2026, 48(2): 684-693.
[7]	杨许鑫, 季薇. 无人机辅助的安全MEC系统中的能耗优化策略[J]. 系统工程与电子技术, 2026, 48(2): 719-726.
[8]	林志康, 刘甲磊, 马佳智, 施龙飞, 徐进宝. 利用分布式辐射源闪烁诱偏的抗反辐射方法[J]. 系统工程与电子技术, 2026, 48(1): 1-11.
[9]	陆则宇, 王瑶, 吴蔚楠, 孙亦鸣, 龚春林. 三维动态环境下的无人机集群双层航迹规划[J]. 系统工程与电子技术, 2026, 48(1): 132-143.
[10]	洪芳宇, 叶青, 张利宁, 伍国华. 面向区域搜索的车载多无人机协同任务规划方法[J]. 系统工程与电子技术, 2026, 48(1): 144-156.
[11]	徐奇琛, 张朝辉, 李靖. 面向未知拒止环境的分布式自适应多无人机协同航迹规划[J]. 系统工程与电子技术, 2026, 48(1): 172-184.
[12]	单晨宇, 李少凡, 齐瑞云. 多智能体系统故障临机处理下的快速任务重分配[J]. 系统工程与电子技术, 2026, 48(1): 185-197.
[13]	李正杰, 刘光远, 张浩为, 刘斌, 齐铖. 面向射频隐身的多无人机协同区域覆盖航迹优化方法[J]. 系统工程与电子技术, 2026, 48(1): 301-311.
[14]	闻雯, 时晨光, 周建江. 多元威胁环境下无人机集群隐身航迹规划算法[J]. 系统工程与电子技术, 2025, 47(9): 2971-2984.
[15]	魏潇龙, 吴亚荣, 姚登凯, 赵顾颢. 基于深度强化学习的无人机空战机动分层决策算法[J]. 系统工程与电子技术, 2025, 47(9): 2993-3003.