一个分布式的时间变化的神经动力学算法用于多无人机协作目标跟踪问题在海上搜索和救援
Lingxi Zhang1, Xing He1, Junzhi Yu2
1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
ISA transactions
|September 10, 2025
概括
这项研究引入了一种新的神经动力学算法,用于多个无人机 (UAV) 在海上搜救中跟踪目标,即使风速变化. 该系统有效地跟踪目标,无论其轨迹变化如何.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 优化理论 优化理论
- 海上运营 海上运营
背景情况:
- 海上搜索和救援行动通常依赖无人机 (UAV) 来追踪目标.
- 现有的方法通常假设固定的环境条件,例如恒定的风速,限制了它们在现实世界中的应用.
- 多个无人机的协作跟踪提供了更好的覆盖范围和稳定性,但在动态环境中面临着挑战.
研究的目的:
- 开发和分析一个分布式算法,用于多个无人机在时间变化 (TV) 条件下进行协作目标跟踪,特别是针对不同的风速.
- 在具有动态目标轨迹的场景中调查拟议的算法的性能.
- 为了确保跟踪算法的固定时间收,独立于初始条件.
主要方法:
- 制定一个类型的TV凸优化问题与不平等约束.
- 分布式电视神经动力学算法的设计,集成预测校正和滑动模式控制.
- 使用利亚普诺夫函数的理论分析来证明固定时间的收性质.
- 实验验证使用已建立的3D目标轨迹方程与侧面变异进行实验验证.
主要成果:
- 拟议的分布式电视神经动力学算法实现了协作目标跟踪的固定时间收.
- 尽管有电视目标轨迹,算法的追踪效率表现仍然很强大.
- 实验结果证实了该系统在不同风速条件下的有效性.
结论:
- 开发的算法为动态海上环境中的多无人机协作目标跟踪提供了强大的解决方案.
- 固定时间收确保可靠和可预测的系统行为,对于搜索和救援任务至关重要.
- 该系统对电视目标轨迹和风状况的弹性提高了其实际效用.
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