基于观察员的六旋转无人机的最佳神经共识控制:一个新的演员关键强化学习策略
概括
这项研究引入了六旋转无人机的新型控制方案,以优化资源分配. 该方法确保无人机在设定的时间范围内实现所需状态和准确的领导追踪.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 六旋转机无人机 (UAV) 在动态环境中面临资源分配方面的挑战.
- 通过统一的规定的性能实现最佳的共识控制仍然是一个重大障碍.
研究的目的:
- 为解决六旋转无人机的基于定时观察者的最佳共识控制问题.
- 制定一个高效的资源配置策略,统一规定的性能.
主要方法:
- 一个规定的时间最佳控制方案,将规定的时间控制与简化强化学习框架相结合.
- 小说更新法律的演员和批评神经网络使用规定的时间调整功能.
- 一个改进的分布式规定的时间观察员领导信息估计.
- 针对统一性能要求的非线性转换和映射.
- 一个自适应的规定时间过器来管理复杂性.
主要成果:
- 六旋转机无人机在规定的时间内达到所需的稳定状态.
- 追随者无人机在规定的时间内准确估计领导者的速度和位置.
- 统一的控制框架简化了性能要求的满足和实施.
- 全球性能要求简化了约束验证.
- 过器错误在规定的时间内趋同.
结论:
- 拟议的方法有效地解决了在统一规定的性能下为六旋转机无人机的最佳共识控制问题.
- 这种方法提高了资源分配效率和控制系统的用户友好性.
- 模拟结果验证了设计的控制方案的有效性和稳定性.
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