基于神经网络的活性干扰排斥控制及其在四旋翼无人机轨迹跟踪中的应用
Zhongxing Ren1, Rongguang Peng2, Xiaoxu Liu2
1Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China; Faculty of Data Science, City University of Macau, Taipa, Macau, China.
ISA transactions
|March 9, 2026
概括
本研究引入了一种基于神经网络的新型主动干扰排斥控制 (ADRC) 系统,用于四旋翼无人机. 它通过实时适应干扰来精确追踪轨迹.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 在工程领域的人工智能.
背景情况:
- 四旋翼无人机需要强大的控制来精确的轨迹跟踪.
- 外部干扰和模型不确定性挑战了现有的控制方法.
- 适应性干扰排斥对于可靠的无人机操作至关重要.
研究的目的:
- 为四旋翼无人机开发一种基于神经网络的新型主动干扰排斥控制 (ADRC) 方案.
- 在动态干扰下提高轨迹跟踪精度.
- 为了实现在线自适应调整控制参数,而无需手动重新校准.
主要方法:
- 一个级联的ADRC架构,内部和外部循环用于态度控制.
- 在线自适应调整扩展状态观察者 (ESO) 参数,使用辐射基函数神经网络 (RBFNN).
- 整合适应时刻估计 (ADAM) 优化器用于加速RBFNN训练.
- 利亚普诺夫稳定性分析证明了系统的稳定性.
主要成果:
- 拟议的ADRC方案证明了四旋翼无人机的精确轨迹跟踪.
- 实现了对时间变化的干扰的实时排斥.
- 硬件在循环中的实验验验证了在多种场景中卓越的性能.
- 该ADAM优化器显著加速了RBFNN训练.
结论:
- 新型基于神经网络的ADRC方案为四旋翼无人机轨迹跟踪提供了强大的解决方案.
- 在线自适应调整有效地处理动态干扰和不确定性.
- 拟议的方法消除了手动参数调节的需要,提高了实用性.
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