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通过共同优化采样和中止决策来控制任务风险
Li Yang1, Fanping Wei1, Qingan Qiu2
1School of Reliability and Systems Engineering, Beihang University, Beijing, China.
本研究介绍了安全关键系统的最佳采样和任务中断策略. 新方法动态决定何时采样和中止任务,以最大限度地降低故障风险和成本.
科学领域:
- 决策理论 决策理论
- 风险管理 风险管理
- 控制系统工程 控制系统工程
背景情况:
- 安全关键系统需要在运行过程中进行强有力的风险管理.
- 系统健康的部分可观测性需要适应性控制策略.
- 现有的任务中止政策往往缺乏基于实时信息的动态调整.
研究的目的:
- 为部分可观测的安全关键系统制定最佳的,动态的采样和任务中止政策.
- 尽量减少预期的总成本,包括采样,任务失败和系统故障.
- 将部分健康信息纳入采样和中止任务的联合决策.
主要方法:
- 将问题构成一个部分可观测的马尔科夫决策过程 (POMDP).
- 设计基于系统的信念状态的动态采样和任务中止政策.
- 分析价值函数和控制极限选择以获得最佳性.
主要成果:
- 拟议的政策共同优化抽样和动态任务中止决策.
- 获得了对价值函数和最佳性条件的结构性见解.
- 数字实验表明,在控制任务损失方面,在启发式中止政策上表现优越.
结论:
- 开发的信息驱动方法有效地管理安全关键系统的故障风险.
- 基于信念状态的动态政策比静态或启发式方法具有显著的优势.
- 这项研究提供了一个框架,通过优化决策来提高复杂系统的可靠性和安全性.
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