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Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers
Author(s) -
Rotondo Damiano,
Cristofaro Andrea,
Johansen Tor Arne,
Nejjari Fatiha,
Puig Vicenç
Publication year - 2018
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.4381
Subject(s) - control theory (sociology) , icing , interval (graph theory) , actuator , interval estimation , observer (physics) , fault (geology) , computer science , robust control , control engineering , engineering , mathematics , control system , control (management) , artificial intelligence , combinatorics , seismology , geology , confidence interval , oceanography , statistics , physics , quantum mechanics , electrical engineering
Summary This paper proposes a linear parameter varying (LPV) interval unknown input observer for the robust fault diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs) described by an uncertain model. The proposed interval observer evaluates the set of values for the state, which are compatible with the nominal fault‐free and icing‐free operation and can be designed in such a way that some information about the nature of the unknown inputs affecting the system can be obtained, thus allowing the diagnosis to be performed. The proposed strategy has several advantages. First, the LPV paradigm allows taking into account operating point variations. Second, the noise rejection properties are enhanced by the presence of the integral term. Third, the interval estimation property guarantees the absence of false alarms. Linear matrix inequality–based conditions for the analysis/design of these observers are provided in order to guarantee the interval estimation of the state and the boundedness of the estimation. The developed theory is supported by simulation results, obtained with the uncertain model of a Zagi Flying Wing UAV, which illustrate the strong appeal of the methodology for identifying correctly unexpected changes in the system dynamics due to actuator faults or icing.