一类非线性互连系统的事件触发式基于学习的故障适应
IEEE transactions on neural networks and learning systems
|October 17, 2023
概括
本研究介绍了用于使用事件触发通信的非线性系统的分布式学习故障适应方案. 该方法通过使用自适应神经网络进行控制重新配置来确保系统稳定性,尽管存在故障.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 网络化系统 网络化系统
背景情况:
- 非线性互连系统容易发生工艺故障.
- 事件触发通信提供了效率,但使故障管理复杂化.
- 现有的故障适应方法可能无法有效处理分布式和相互连接的动态.
研究的目的:
- 为非线性互联系统提出基于分布式学习的故障适应方案.
- 解决局部动态和从邻近子系统传播的故障.
- 在事件触发通信和异步重新配置下确保闭环稳定性.
主要方法:
- 在故障检测之前使用事件触发的名义控制定律.
- 实施事件触发的故障适应法后故障检测.
- 采用基于神经网络的自适应学习方案来在线估计耐故障控制函数.
- 分析闭环稳定性与异步控制器重新配置.
主要成果:
- 拟议的方案有效地适应非线性互连系统中的工艺故障.
- 稳定性分析证实了基于事件触发参数的衍生条件的系统稳定性.
- 成功排除了Zeno的行为,确保了实际实施.
- 该方案在一个相互连接的反向摆形系统上进行了演示.
结论:
- 开发的基于分布式学习的故障适应方案提高了非线性互联系统的弹性.
- 事件触发通信和自适应神经网络为故障耐受性提供了有效的框架.
- 严格的稳定性分析验证了实际应用的拟议方法.
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