强化学习为基础的分数顺序适应式容错形成控制网络固定翼无人机的网络固定翼无人机与规定的性能
概括
本研究介绍了无人机 (UAV) 的容错形成控制. 它确保追随者无人机与领导者保持一致,即使有故障,使用先进的神经网络和干扰观察器.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 网络无人驾驶飞行器 (UAV) 在故障时面临维护阵列的挑战.
- 分布式跟踪控制对于协调无人机操作至关重要.
研究的目的:
- 调查固定翼无人机的耐故障形成控制 (FTFC).
- 开发一个控制方案,确保追随者无人机跟踪领导者无人机,尽管系统故障.
主要方法:
- 使用有限时间规定的性能函数 (PPF) 来管理跟踪错误.
- 批评者和演员神经网络 (NN) 用于学习绩效指数和非线性术语.
- 非线性干扰观察器 (DO) 的设计是为了弥补学习错误.
主要成果:
- 拟议的FTFC计划确保追随者无人机通过预先设计的偏移来跟踪领导者.
- 使用利亚普诺夫稳定性分析,分布式跟踪错误被证明是有限时间的收.
- 对比模拟验证了开发的控制策略的有效性.
结论:
- 该研究成功解决了网络固定翼无人机的FTFC问题.
- 集成PPF,NN和DO,为无人机形成控制提供了一个强大的解决方案.
- 拟议的方法提高了无人机系统在故障场景中的可靠性和性能.
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