固定翼无人机的自适应神经网络控制与干扰观察器在切换干扰下
IEEE transactions on neural networks and learning systems
|November 6, 2024
概括
本研究介绍了适应性神经网络控制固定翼无人机 (FUAV) 面临无模型动态和时间变化的切换干扰. 拟议的方法提高了FUAV在复杂飞行环境中的稳定性和控制精度.
科学领域:
- 控制工程 控制工程 控制工程
- 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:
- 固定翼无人机 (FUAV) 面临着未建模动力学和时间变化的切换干扰 (TVSD) 的挑战.
- 准确的建模和控制对于FUAV的稳定性和性能至关重要,尤其是在飞行区域变化期间.
研究的目的:
- 为FUAV开发一种适应性神经网络控制策略,该策略对未建模动态和TVSD具有稳定性.
- 改进TVSD和未建模动态的估计,以提高控制性能.
主要方法:
- 一种切换增强模型 (SAM) 和参数适应 (PA) 技术来描述和估计TVSD.
- 一个干扰观察器 (DO) 来估计未建模的时间变异干扰.
- 辐射基函数神经网络 (RBFNN) 用于近似未知的动态.
- 一个DO形式的辅助系统,以提高估计性能.
主要成果:
- 拟议的控制策略有效地估计和补偿模拟和非模拟的干扰.
- 为闭环开关系统 (CLSS) 获得了足够的稳定性条件.
- 一个说明性的例子展示了自适应控制策略的可行性和优势.
结论:
- 开发的自适应神经网络控制为在复杂和不确定的条件下运行的FUAV提供了强大的解决方案.
- 该策略增强了FUAV控制的准确性和稳定性,通过对态度模型的模拟进行验证.
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