文章基本信息

标题：Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
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作者：Ibrahim Ahmed ; Hamed Khorasgani ; Gautam Biswas 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：24
页码：233-240
DOI：10.1016/j.ifacol.2018.09.583
语种：English
出版社：Elsevier
摘要：AbstractA desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal control policies to be enumerated for possible faults. Instead it can approximate one in real-time. We present two adaptive fault-tolerant control schemes for a discrete time system based on hierarchical reinforcement learning. We compare their performance against a model predictive controller in presence of sensor noise and persistent faults. The controllers are tested on a fuel tank model of a C-130 plane. Our experiments demonstrate that reinforcement learning-based controllers perform more robustly than model predictive controllers under faults, partially observable system models, and varying sensor noise levels.
关键词：KeywordsReinforcement learning controlmodel predictive controlfault tolerancemodel-based controlhierarchical reinforcement learning