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arXiv 2008.04407eess.SYcs.AIcs.LGcs.SY
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Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning

Institute of Software Integrated Systems, Vanderbilt University(范德堡大学软件集成系统研究所)

Ibrahim Ahmed, Marcos Quiñones-Grueiro, Gautam Biswas

英文摘要:

We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step. Therefore, \textit{a priori} knowledge of faults that may occur in the system is not required. The adaptive scheme combines online and offline learning of the on-policy control method to improve exploration and sample efficiency, while guaranteeing stable learning. The offline learning phase is performed using a data-driven model of the system, which is frequently updated to track the system's operating conditions. We conduct experiments on an aircraft fuel transfer system to demonstrate the effectiveness of our approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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