无人驾驶飞行器的自适应控制基于复杂环境中的通信信息优化.
Zirong Wang1, Zhengyu Han1, Shahzadi Tayyaba2
1Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi'an, Shaanxi, China.
PeerJ. Computer science
|April 25, 2024
概括
本研究引入了一种ATT-Bi-LSTM框架,通过整合通信信号以进行自适应性参数调整来增强无人机控制. 这种新的方法显著提高了立场估计的准确性,并优化了无人机定位.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 无人机 (UAV) 越来越多地用于航空,军事和物流,以提高效率和安全.
- 实时参数调整对于UAV在复杂环境中的飞行安全和有效性至关重要.
- 准确的态度估计对于有效的无人机操作和控制至关重要.
研究的目的:
- 通过自适应参数控制来开发优化无人机的先进框架.
- 通过整合通信信号数据来提高态度估计的准确性.
- 改进无人机的实时控制和定位.
主要方法:
- 介绍ATT-Bi-LSTM框架用于无人机优化.
- 使用双层双向长短期内存 (BI-LSTM) 进行增强的特征提取.
- 采用注意力机制来放大LSTM网络输出,以实现最佳定位控制.
- 整合来自通信信号的状态信息,以进行自适应性参数调整.
主要成果:
- 该ATT-Bi-LSTM框架在使用光学系统数据的实证验证中表现出了令人称赞的表现.
- 该模型在估计关键态度指标方面取得了卓越的准确性:斜率,斜率和滚动.
- 与现有方法相比,拟议的模型表现出最低的错误率 (RMSR和MAE).
结论:
- 该ATT-Bi-LSTM框架提供了一个强大的解决方案,以提高无人机的态度估计和控制.
- 该研究提供了重要的算法支持,并为未来的无人机优化研究提供了宝贵的参考.
- 与通信信号数据集成的自适应参数控制对于提高无人机性能是有效的.
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