文章基本信息

标题：Fault Detection for Dynamical Systems using Differential Geometric and Concurrent Learning Approach
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作者：I. Chakraborty ; D. Vrabie
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：1395-1402
DOI：10.1016/j.ifacol.2018.09.552
语种：English
出版社：Elsevier
摘要：AbstractA fault identification and estimation approach that brings together the differential geometric concept of observability codistribution with data-driven concurrent learning, is presented in this paper. In order to identify faults in the presence of unknown disturbances, we use the differential geometric approach to design a coordinate transformation, in order to find a subspace in which the effects of disturbances and system faults can be segregated. We then use concurrent learning to estimate the magnitude of the constant fault. We illustrate the approach to fault isolation for a spherical pendulum dynamics. We use Lyapunov stability analysis to show that the fault estimate by concurrent learning converges to the actual fault value, and then illustrate the design of a recovery controller.
关键词：KeywordsFault detectiondiagnosisDifferential geometric methodConcurrent learningData-driven approach