动态级联尖端神经网络监督控制器用于非平面的十二旋转器无人机
Cheng Peng1, Guanyu Qiao1, Bing Ge1
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究介绍了12旋转机无人机 (UAV) 的智能控制器,以提高轨迹跟踪精度,即使有风力干扰. 新型神经控制系统增强了稳定性和自我学习能力,可提供可靠的飞行性能.
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:
- 风力干扰等环境变量影响多旋转无人机 (UAV) 轨迹的准确性.
- 传统的多转子设计往往表现出有限的曲率控制能力.
研究的目的:
- 开发一个智能监督神经控制系统,用于精确的轨迹跟踪在一个非平面的十二旋转机无人机.
- 提高无人机适应环境不确定性的能力,提高飞行性能.
主要方法:
- 开发一个非平面的十二旋转机无人机,具有控制器设计的特征模型.
- 关于智能复合控制器的建议,该控制器集成了自适应滑动模式反控制和动态级联尖端神经网络 (DCSNN) 监督前控制.
- 实施权重学习和动态级联结构学习算法,以实现网络稳定性和强度.
主要成果:
- 拟议的基于DCSNN的智能复合控制器展示了卓越的轨迹跟踪性能.
- 通过在户外风力干扰条件下进行比较数值模拟和原型实验来验证有效性.
- 非平面的设计改进了传统的多转子曲折运动限制.
结论:
- 智能监督神经控制系统有效地解决了十二旋转无人机的轨迹跟踪挑战,特别是在环境干扰下.
- 适应式滑动模式控制和DCSNN的组合为无人机飞行控制提供了一个强大而可适应的解决方案.
- 该研究证实了开发的非平面无人机及其控制系统的实际可行性和增强性能.
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