Mission risk control via joint optimization of sampling and abort decisions

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.