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Data‐driven fault diagnosis and robust control: Application to PEM fuel cell systems
Author(s) -
OcampoMartinez Carlos,
SánchezPeña Ricardo,
Bianchi Fernando,
Ingimundarson Ari
Publication year - 2017
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3820
Subject(s) - fault (geology) , controller (irrigation) , control theory (sociology) , data driven , proton exchange membrane fuel cell , nonlinear system , control (management) , control engineering , engineering , fault tolerance , control system , robust control , computer science , fuel cells , reliability engineering , artificial intelligence , physics , electrical engineering , quantum mechanics , chemical engineering , seismology , agronomy , biology , geology
Summary A data‐driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault‐tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous online performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests, a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a nonlinear experimentally validated model of the breathing system of a polymer electrolyte membrane fuel cell. The results show the effectiveness of this FDI–fault‐tolerant control data‐driven methodology. Copyright © 2017 John Wiley & Sons, Ltd.