A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

Cen, Zhaohui, Wei, Jiaolong, & Jiang, Rui (2013) A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems. International Journal of Neural Systems, 23(6), pp. 1350025-1.

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Abstract

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Impact and interest:

15 citations in Scopus
12 citations in Web of Science®
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ID Code: 74808
Item Type: Journal Article
Refereed: Yes
Keywords: Model identification and fault estimation, nonlinear dynamic systems, gray-box neural-network model, extended state observer, reaction wheel
DOI: 10.1142/S0129065713500251
ISSN: 0129-0657
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Transport Research Centre
Copyright Owner: Copyright 2013 World Scientific Publishing
Deposited On: 07 Aug 2014 22:17
Last Modified: 11 Aug 2014 02:47

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