基于预测槽位引导灰狼优化的三无人机受限三维协同围控
Predictive-Slot-Guided Grey Wolf Optimization for Constrained Three-Dimensional Cooperative Encirclement by Three UAVs
摘要: 针对机动目标速度突变时三无人机围控队形易偏离、有限迭代灰狼优化难以及时恢复有效候选的问题，文章提出预测槽位引导的状态触发自适应灰狼优化方法。首先，利用有限差分加速度预测目标短时位置，并在预测目标周围构造3个具有高度差的参考槽位；其次，将槽位反解为九维加速度引导向量，在固定迭代位置对部分候选实施等预算恢复。水平突转场景的30次配对结果表明，相对随机恢复GWO，本方法的平均恢复时间减少0.0867 s，半径均方根误差减少0.0128 m，有效围控比例提高0.0048，三项95%置信区间均未跨越0。与粒子群优化和改进灰狼优化的比较进一步验证了水平场景下的恢复优势，但其控制代价和跨场景表现仍具有一定局限性。
Abstract: Aiming at the problems that the three-UAV encirclement formation is prone to deviating when the speed of a maneuvering target changes abruptly, and that grey wolf optimization under limited iterations struggles to promptly restore effective candidates, this paper proposes a prediction-slot-guided state-triggered adaptive grey wolf optimization method. First, a finite-difference acceleration estimate is used to predict the short-term target position, around which three reference slots with prescribed altitude offsets are constructed. Second, the slots are inverse-mapped into a nine-dimensional acceleration guidance vector, and several candidates are recovered at a fixed internal iteration under an equal per-recovery evaluation budget. Thirty paired runs in the horizontal abrupt-turn scenario show that, compared with random-recovery GWO, ST-A-GWO reduces the mean recovery time by 0.0867 s and the radius root-mean-square error by 0.0128 m, while increasing the eligible encirclement ratio by 0.0048. The corresponding 95% confidence intervals do not cross zero. Comparisons with particle swarm optimization and improved grey wolf optimization support the recovery advantage in the horizontal scenario, although limitations remain in control effort and cross-scenario performance.
文章引用：杨海涛, 杨艺. 基于预测槽位引导灰狼优化的三无人机受限三维协同围控[J]. 传感器技术与应用, 2026, 14(5): 880-893. https://doi.org/10.12677/jsta.2026.145084

参考文献

[1] Hafez, A.T., Marasco, A.J., Givigi, S.N., Iskandarani, M., Yousefi, S. and Rabbath, C.A. (2015) Solving Multi-UAV Dynamic Encirclement via Model Predictive Control. IEEE Transactions on Control Systems Technology, 23, 2251-2265.
https://doi.org/10.1109/tcst.2015.2411632
[2] Wang, W., Chen, X., Jia, J. and Fu, Z. (2022) Target Localization and Encirclement Control for Multi‐UAVs with Limited Information. IET Control Theory & Applications, 16, 1396-1404.
https://doi.org/10.1049/cth2.12314
[3] Gao, Y., Bai, C., Zhang, L. and Quan, Q. (2022) Multi-UAV Cooperative Target Encirclement within an Annular Virtual Tube. Aerospace Science and Technology, 128, Article ID: 107800.
https://doi.org/10.1016/j.ast.2022.107800
[4] Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014) Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
https://doi.org/10.1016/j.advengsoft.2013.12.007
[5] Yao, P., Wang, H. and Ji, H. (2016) Multi-UAVs Tracking Target in Urban Environment by Model Predictive Control and Improved Grey Wolf Optimizer. Aerospace Science and Technology, 55, 131-143.
https://doi.org/10.1016/j.ast.2016.05.016
[6] Wang, Y., Zhang, T., Cai, Z., Zhao, J. and Wu, K. (2020) Multi-UAV Coordination Control by Chaotic Grey Wolf Optimization Based Distributed MPC with Event-Triggered Strategy. Chinese Journal of Aeronautics, 33, 2877-2897.
https://doi.org/10.1016/j.cja.2020.04.028
[7] 秦冬燕, 闫晓辉, 邵桂伟, 等. 基于事件触发灰狼优化算法的四旋翼无人机三维航迹规划[J]. 智能系统学报, 2025, 20(3): 699-706.
[8] Peng, J., Tian, X. and Wang, N. (2025) Railway Multi UAV Collaborative Encirclement Strategy Based on Grey Wolf Optimization Dynamic Encirclement Points. Aviation, 29, 231-241.
https://doi.org/10.3846/aviation.2025.25360
[9] Nadimi-Shahraki, M.H., Taghian, S. and Mirjalili, S. (2021) An Improved Grey Wolf Optimizer for Solving Engineering Problems. Expert Systems with Applications, 166, Article ID: 113917.
https://doi.org/10.1016/j.eswa.2020.113917
[10] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of ICNN’95—International Conference on Neural Networks, Perth, 27 November-1 December 1995, 1942-1948.
https://doi.org/10.1109/icnn.1995.488968
[11] Clerc, M. and Kennedy, J. (2002) The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, 6, 58-73.
https://doi.org/10.1109/4235.985692
[12] Olfati-Saber, R. (2006) Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. IEEE Transactions on Automatic Control, 51, 401-420.
https://doi.org/10.1109/tac.2005.864190
[13] Cao, Y., Yu, W., Ren, W. and Chen, G. (2013) An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination. IEEE Transactions on Industrial Informatics, 9, 427-438.
https://doi.org/10.1109/tii.2012.2219061
[14] Chung, S., Paranjape, A.A., Dames, P., Shen, S. and Kumar, V. (2018) A Survey on Aerial Swarm Robotics. IEEE Transactions on Robotics, 34, 837-855.
https://doi.org/10.1109/tro.2018.2857475
[15] Qin, S.J. and Badgwell, T.A. (2003) A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice, 11, 733-764.
https://doi.org/10.1016/s0967-0661(02)00186-7
[16] Rong Li, X. and Jilkov, V.P. (2003) Survey of Maneuvering Targettracking. Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems, 39, 1333-1364.
https://doi.org/10.1109/taes.2003.1261132
[17] Faris, H., Aljarah, I., Al-Betar, M.A. and Mirjalili, S. (2018) Grey Wolf Optimizer: A Review of Recent Variants and Applications. Neural Computing and Applications, 30, 413-435.
https://doi.org/10.1007/s00521-017-3272-5
[18] Blackwell, T. and Branke, J. (2006) Multiswarms, Exclusion, and Anti-Convergence in Dynamic Environments. IEEE Transactions on Evolutionary Computation, 10, 459-472.
https://doi.org/10.1109/tevc.2005.857074

为你推荐