Open AccessDesign of a PD‐type learning observer for reconstruction of actuator faults in descriptor systems
Author(s) -
Jia Qingxian,
Chen Wen,
Wang Peng,
Zhang Yingchun
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.1143
Subject(s) - observer (physics) , control theory (sociology) , actuator , computer science , linear matrix inequality , type (biology) , fault detection and isolation , stability (learning theory) , artificial intelligence , control engineering , mathematics , engineering , control (management) , mathematical optimization , machine learning , physics , quantum mechanics , ecology , biology
This study investigates the problem of reconstructing actuator faults for descriptor systems via a PD‐type learning observer. By synthesising the derivatives of the output estimation error into the P‐type learning law, a novel PD‐type learning observer is established to simultaneously reconstruct original system states and actuator faults. Stability analysis of the PD‐type learning observer is explicitly provided. A systematic design method is also suggested based on a linear matrix inequality technique. Further, a robust PD‐type learning observer is designed against process disturbances and measurement noises. At last, a simulation example is used to demonstrate the effectiveness of the proposed fault‐reconstructing method.