学习基于观察者的故障耐受性控制,用于具有概率记忆事件触发协议的半马尔科夫跳跃系统.
Qilong Xie1, Yunliang Wang1, Jun Cheng1
1School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China.
ISA transactions
|May 6, 2025
概括
本研究引入了一种新的故障耐受性控制方法,用于使用概率记忆事件触发协议的半马尔科夫跳跃系统. 它实现了稳定性,并通过快速故障估计减少了通信负载.
科学领域:
- 控制系统工程 控制系统工程
- 随机系统分析 随机系统分析
- 网络控制系统 网络控制系统
背景情况:
- 半马尔科夫跳跃系统 (SMJS) 对于建模具有状态依赖切换行为的系统至关重要.
- 基于观察者的故障耐受性控制对于在组件故障下保持系统稳定性和性能至关重要.
- 事件触发通信协议旨在减少网络负载,但经常面临稳定性保证和网络延迟的挑战.
研究的目的:
- 开发一个基于学习的观察者来控制SMJS的容错性控制.
- 设计一种新的概率记忆事件触发协议 (PMETP),以适应网络延迟并降低通信负担.
- 为了确保系统的稳定性和性能,尽管传感器故障和通信限制.
主要方法:
- 利用一个由平均停留时间信号控制的半马尔科夫过程,以模拟系统动态.
- 实施一种新的PMETP,利用历史内部变量并考虑随机网络延迟.
- 引入基于学习的观察员来准确和快速估计不可区分的传感器故障.
主要成果:
- 拟议的PMETP显著减少数据传输,而不会影响系统稳定性.
- 基于学习的观察者有效地估计了传感器故障错误,即使对于不可区分的故障.
- 综合性耐故障控制策略表明,在考虑的条件下,SMJS的性能强.
结论:
- 开发的方法为SMJS的故障耐受性控制提供了有效的解决方案,通信需求减少.
- 结合PMETP和基于学习的观察员,为未来的网络控制系统设计提供了一个有希望的方向.
- 该方法成功地在复杂的动态系统中平衡了性能,稳定性和通信效率.
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