基于神经网络的观察者与非单元终端滑动模式控制相结合,用于QUAV跟踪:实验验证验证
Haoping Wang1, Omid Mofid2, Saleh Mobayen3
1School of Automation, Nanjing University of Science and Technology, 200 Xiao Ling Wei Street, Nanjing 210094, China.
ISA transactions
|February 4, 2026
概括
这项研究提出了一个强大的四旋翼控制策略,使用自适应神经网络和非单元终端滑动模式控制. 该方法确保了快速的轨迹跟踪,尽管存在不确定性和干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 四旋翼轨迹跟踪受到状态不确定性,动态干扰和未知的系统动态的挑战.
- 现有的控制方法可能会与四旋翼机遇到的复杂,现实世界的条件作斗争.
研究的目的:
- 开发一个先进的控制框架,以快速准确地跟踪四旋翼飞行轨迹.
- 为了增强四旋翼机对不确定性和外部干扰的强度.
- 通过模拟和实验验证拟议的控制策略.
主要方法:
- 模拟四旋翼动力学与固有的不确定性和干扰.
- 设计一个状态和干扰观察器来估计未知的状态并拒绝干扰,通过利亚普诺夫理论证明.
- 实现非单元终端滑动模式控制 (TSMC) 进行错误稳定,具有指数趋同.
- 使用自适应神经网络 (ANN) 来近似未知的系统组件.
主要成果:
- 国家观察员证明了估计错误的指数趋同.
- 在TSMC框架确保了滑动表面的指数趋同到零.
- 模拟和实验证实了拟议的基于自适应神经网络的TSMC战略的有效性.
- 控制策略显示出显著的稳定性和快速轨迹跟踪能力.
结论:
- 集成的自适应神经网络,状态观察员和非单元终端滑动模式控制框架有效地解决了四旋翼飞行轨迹跟踪的挑战.
- 拟议的方法提供了一个强大的解决方案,用于四旋翼控制在不确定的和扰乱的环境.
- 实验验证证证实了开发的控制策略的实际适用性和高性能.
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