中止
马尔可夫决策过程
计算机科学
采样(信号处理)
启发式
运筹学
数学优化
控制(管理)
马尔可夫过程
可靠性工程
工程类
数学
统计
人工智能
操作系统
滤波器(信号处理)
计算机视觉
作者
Li Yang,Fanping Wei,Qingan Qiu
摘要
Abstract Information‐driven mission abort is an effective way to control the failure risk of safety‐critical systems during mission executions. We investigate the optimal sampling and mission abort decisions of partially observable safety‐critical systems, where the underlying system health state can only be revealed by sampling. In contrast to previous studies, we employ partial health information to jointly determine: (a) whether to execute sampling, and (b) when to abort the mission in a dynamic manner, so as to minimize the expected total cost incurred by sampling, mission failure, and system malfunction. Dynamic sampling and mission abort policies are devised following the belief state, whose optimization model is cast into the framework of a partially observable Markov decision process. Some structural insights with regard to the value function, control limit selection, and optimality existence are presented. The performance of the proposed sampling and abort policy is tested by numerical experiments, which are proved to outperform other heuristic abort policies in mission loss control.
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