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Robust fault detection based on adaptive threshold generation using interval LPV observers
Author(s) -
Montes de Oca Saúl,
Puig Vicenç,
Blesa Joaquim
Publication year - 2012
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1263
Subject(s) - control theory (sociology) , fault detection and isolation , observer (physics) , residual , bounded function , interval (graph theory) , linear matrix inequality , sensitivity (control systems) , fault (geology) , mathematics , computer science , detection threshold , algorithm , engineering , mathematical optimization , artificial intelligence , real time computing , actuator , physics , mathematical analysis , control (management) , quantum mechanics , combinatorics , electronic engineering , seismology , geology
SUMMARY In this paper, robust fault detection based on adaptive threshold generation of a non‐linear system described by means of a linear parameter‐varying (LPV) model is addressed. Adaptive threshold is generated using an interval LPV observer that generates a band of predicted outputs taking into account the parameter uncertainties bounded using intervals. An algorithm that propagates the uncertainty based on zonotopes is proposed. The design procedure of this interval LPV observer is implemented via pole placement using linear matrix inequalities. Finally, the minimum detectable fault is characterized using fault sensitivity analysis and residual uncertainty bounds. Two examples, one based on a quadruple‐tank system and another based on a two‐degree of freedom helicopter, are used to assess the validity of the proposed fault detection approach. Copyright © 2011 John Wiley & Sons, Ltd.