Open AccessDNN identification and fault‐tolerant control for non‐linear systems with unknown faults
Author(s) -
Zhang Xiaoli,
Gu Xiang,
Shen Hong,
Yi Yang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1089
Subject(s) - actuator , control theory (sociology) , identifier , computer science , identification (biology) , observer (physics) , fault (geology) , lyapunov function , linear system , mathematics , control (management) , nonlinear system , artificial intelligence , mathematical analysis , botany , physics , quantum mechanics , seismology , biology , programming language , geology
In this study, dynamical neural networks (DNNs) are used as an online identifier for a class of non‐linear systems with unknown actuator faults. By integrating the designed non‐linear FD observer with adaptive regulation algorithm, the parameter coupling problem in DNNs can be successfully solved and the unknown actuator fault can be rejected simultaneously. Based on Lyapunov theory and convex optimisation algorithm, both the observation error and the identification error can be proved to convergent to zero. Finally, simulation examples for the non‐linear systems with actuator fault are given to illustrate the effectiveness of the proposed approach.