量子化代学习控制用于带有数据丢失的冲动差异性纳入系统
Wanzheng Qiu1, JinRong Wang1, Dong Shen2
1Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, China; Supercomputing Algorithm and Application Laboratory of Guizhou University and Gui'an Scientific Innovation Company, Guizhou University, Guiyang, Guizhou 550025, China.
这项研究介绍了一种量子化的代学习控制,用于带有数据丢失的冲动系统. 这种新的方法确保了零错误的跟踪性能,尽管存在通信不确定性.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 网络化系统 网络化系统
背景情况:
- 冲动差异性纳入系统是复杂的,容易发生数据丢失.
- 现有的控制方法与异步数据和量子化效应作斗争.
研究的目的:
- 为具有随机数据丢失的冲动系统开发量化代学习控制策略.
- 为了实现零错误的跟踪性能,尽管通信限制.
主要方法:
- 使用施泰纳类型的选择器将设置值映射转换为单值映射.
- 设计了一种间歇更新学习算法,以处理来自双面停机的数据异步.
- 引入了用于零错误跟踪的缩放序列,并确定了量子化水平的上限.
主要成果:
- 拟议的量子化方法通过增加计算来降低网络通信负载.
- 学习算法有效地管理数据丢失,而不需要特定的概率分布.
- 实现了边界定量化误差和零误差跟踪性能.
结论:
- 开发的量化代学习控制对于具有数据丢失的冲动系统是有效的.
- 该方法为面对通信不确定性的联网控制系统提供了实用解决方案.
- 通过数值模拟在一个开关的不情愿电机系统上进行验证.
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