| 引用本文: | 胡景赫,鲜斌,姜鹏志.利用分布式模型预测控制的无人机编队轨迹规划[J].控制理论与应用,2026,43(4):738~746.[点击复制] |
| HU Jing-he,XIAN Bin,JIANG Peng-zhi.Trajectory planning for UAV formation using distributed model predictive control[J].Control Theory & Applications,2026,43(4):738~746.[点击复制] |
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| 利用分布式模型预测控制的无人机编队轨迹规划 |
| Trajectory planning for UAV formation using distributed model predictive control |
| 摘要点击 244 全文点击 26 投稿时间:2024-01-16 修订日期:2025-06-12 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2024.40037 |
| 2026,43(4):738-746 |
| 中文关键词 无人机编队 轨迹规划 分布式控制 模型预测控制 |
| 英文关键词 UAV formation trajectory planning distributed control model predictive control |
| 基金项目 国家重点研发计划资助项目(2018YFB1403900), 国家自然科学基金项目(91748121, 90916004)资助. |
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| 中文摘要 |
| 本文基于分布式模型预测控制方法, 设计了一种考虑多约束的无人机编队轨迹规划策略. 该策略使无人机
编队在满足约束的前提下, 规划出从起始点到目标点的期望轨迹. 首先, 建立线性时不变的无人机编队运动模型.
然后, 在考虑状态约束、机间避碰及避障等约束的情况下, 以期望轨迹生成和编队保持为规划目标, 基于一致性的
编队策略设计一种轨迹规划算法. 采用数值仿真的形式与现有的避碰算法进行比较, 体现了所设计的按需避碰算
法的良好性能. 最后, 使用自主搭建的无人机系统在复杂环境中进行飞行实验, 验证本文设计的编队轨迹规划算法
的有效性. |
| 英文摘要 |
| This paper proposes a multi-constraint trajectory planning strategy for unmanned aerial vehicle (UAV) forma-tions based on distributed model predictive control methods. This strategy enables UAV formations to generate an expected
trajectory from starting points to target points while satisfying constraints. Firstly, a linear time-invariant motion model for
UAV formations is established. Then, considering constraints such as state constraints, inter-vehicle collision avoidance,
and obstacle avoidance, a trajectory planning algorithm is designed based on a consistency-driven formation strategy, with
the goal of generating desired trajectories and maintaining formation. Numerical simulations are used to compare with existing collision avoidance algorithms, demonstrating the good performance of the designed on-demand collision avoidance
algorithm. Finally, flight experiments are conducted in complex environments using an autonomously constructed UAV
system to verify the effectiveness of the formation trajectory planning algorithm designed in this paper. |
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