基于自适应神经网络的超扭曲滑动模式控制,用于在干扰下跟踪无人机轨迹
Omid Mofid1, Zainab Akhtar2, Saleh Mobayen3
1Computer Science Department, University of Tulsa, OK, USA.
ISA transactions
|October 11, 2025
概括
本研究提出了一种用于四旋翼系统的新型控制策略,增强在扰乱环境中的跟踪控制. 该方法确保了准确的位置估计和强大的干扰排斥,以提高性能.
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:
- 四旋翼系统在保持稳定的飞行和准确的轨迹跟踪方面面临挑战,特别是在外部干扰和不确定状态的环境中.
- 现有的控制方法经常与快速跟踪要求和有效的干扰拒绝同时扎.
研究的目的:
- 开发和验证一种新的控制策略,用于在不确定状态的扰乱环境中快速跟踪四旋翼系统的控制.
- 通过自适应和智能控制技术,提高四旋翼追踪控制的稳定性和准确性.
主要方法:
- 一个双相控制策略,集成自适应超扭曲非单元终端滑动模式控制 (AST-NTSMC) 与多层神经网络 (MLNN).
- 第一个阶段:未知四旋翼位置估计的MLNN.
- 第二阶段:AST-NTSMC用于快速跟踪控制和外部干扰排斥,通过利亚普诺夫理论进行稳定性分析.
主要成果:
- 使用MLNN.准确的四旋翼位置估计.
- AST-NTSMC表面的快速融合,确保快速跟踪控制.
- 有效拒绝外部干扰和无聊的控制性能.
- 对不确定的干扰极限进行自适应估计.
结论:
- 拟议的综合控制策略显著提高了四旋翼在具有挑战性的环境中的追踪性能.
- MLNN和AST-NTSMC的组合为快速跟踪控制和干扰排斥提供了强大而有效的解决方案.
- 广泛的模拟和硬件循环测试验证了拟议的控制技术的优越性和有效性.
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