基于自适应性全球快速终端滑动模式神经网络控制方法的混合故障和外部干扰的QUAV的有限时间容错跟踪控制
Xiyu Zhang1, Chun Feng2, Youjun Zhou3
1School of Information Science and Engineering, Xinjiang College of Science & Technology, Korla, 841000, China.
Scientific reports
|July 27, 2025
概括
这项研究开发了面临未知的故障和干扰的四旋翼无人机 (QUAV) 的自适应控制. 该方法确保定位和态度的有限时间跟踪控制,提高QUAV的稳定性和性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 四旋翼无人机 (QUAV) 容易受到未知的混合故障 (输入量化,执行器故障) 和外部干扰.
- 现有的控制策略往往难以应对这些不确定性带来的复杂性,从而影响跟踪性能.
- 准确而强大的控制对于在动态环境中可靠运行QUAV至关重要.
研究的目的:
- 在未知的混合故障和外部干扰下,为QUAVs开发一个有限时间跟踪控制策略.
- 为了提高QUAV位置和态度控制的稳定性和精度.
- 为了应对未知的非线性动态和控制系数所带来的挑战.
主要方法:
- 利用辐射基函数神经网络 (RBFNNs) 来近似未知的QUAV动态.
- 设计适应性控制规则,用于在线重量更新和近似误差和干扰的估计.
- 整合了一个Nussbaum增强函数来处理未知的控制系数,并使用全球快速终端滑动模式 (GFTSM) 函数来控制设计.
主要成果:
- 建议适应GFTSM神经网络控制策略,用于位置和态度跟踪.
- 理论分析证实了QUAV输出与参考轨迹的有限时间趋同.
- 追踪错误被证明在有限的时间内汇聚到一个小的零邻里.
- 通过对实际系统的模拟来验证有效性.
结论:
- 开发的自适应控制策略有效地解决了混合故障和干扰的QUAVs的有限时间跟踪控制.
- 这种方法确保了强大而精确的控制,实现了跟踪错误的快速趋同.
- 该研究展示了一种可行的方法来提高自主四旋翼系统的可靠性和性能.
相关概念视频
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01:26Feedback control systems
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