北京理工大学 宇航学院,北京 100081
北京理工大学 中国-阿联酋智能无人系统“一带一路”联合实验室,北京 100081
中北大学 电气与控制工程学院,山西 太原 030051
通信作者邮箱:wanghuibit@bit.edu.cn
收稿:2025-01-16,
网络首发:2025-12-25,
纸质出版:2026-02-28
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郭可晴, 王辉, 唐道光, 等. 基于延时补偿策略与拉盖尔函数的松弛分布式轨迹跟踪算法[J]. 兵工学报, 2026,47(2):250059.
GUO Keqing, WANG Hui, TANG Daoguang, et al. Relaxed Distributed Trajectory Tracking Algorithm Based on Delay Compensation Strategy and Laguerre Function[J]. Acta Armamentarii, 2026, 47(2): 250059.
郭可晴, 王辉, 唐道光, 等. 基于延时补偿策略与拉盖尔函数的松弛分布式轨迹跟踪算法[J]. 兵工学报, 2026,47(2):250059. DOI: 10.12382/bgxb.2025.0059.
GUO Keqing, WANG Hui, TANG Daoguang, et al. Relaxed Distributed Trajectory Tracking Algorithm Based on Delay Compensation Strategy and Laguerre Function[J]. Acta Armamentarii, 2026, 47(2): 250059. DOI: 10.12382/bgxb.2025.0059.
针对无人机编队轨迹跟踪中的时延效应显著与计算资源需求高的问题,提出一种基于延时补偿策略与拉盖尔函数的松弛分布式模型预测控制算法。以分布式模型预测控制框架为基础,建立无人机编队离散运动模型,并引入拉盖尔函数将控制输入参数化,显著提高计算效率。通过离散控制屏障函数与松弛变量机制,实现对约束条件的动态调整,避免障碍冲突和机间碰撞。针对采样延时、计算延时和通信延时引起的误差累积问题,提出多延时耦合补偿同步机制,有效保障编队协同稳定控制。仿真实验结果表明,在随机扰动条件下,该算法可显著降低轨迹跟踪误差,提升动态响应速度,同时满足高实时性与高可靠性的需求,为无人机在复杂环境中的编队轨迹跟踪问题提供了有效解决方案。
The problems of prominent delay effect and high computational resource requirements exist in UAV formation trajectory tracking. A relaxed distributed model predictive control algorithm based on delay compensation strategy and Laguerre function is proposed. Based on the distributed model predictive control framework
a discrete motion model of UAV formation is established
and the Laguerre function is introduced to parameterize the control inputs
which significantly improves the computational efficiency. The constraints are dynamically adjusted to avoid obstacles and inter-agent collisions through a discrete-time control barrier function and the slack variable mechanism. For the error accumulation problem caused by sampling delay
computation delay and communication delay
a multi-delay coupling compensation synchronization mechanism is proposed to effectively ensure the stable control of formation coordination. The simulated results show that the proposed algorithm can significantly reduce the trajectory tracking errors and enhance the dynamic responsiveness under random perturbation conditions. Furthermore
it meets the requirements for highly real-time and high reliability
providing an effective solution to the trajectory tracking challenges faced by UAV formation in complex environments.
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