Int J Neural Syst - A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems.

Tópicos

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Resumo

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.

Resumo Limpo

novel graybox neural network model gbnnm includ multilay percept mlp neural network nn integr propos model identif fault estim mife scheme gbnnm nonlinear dynam class nonlinear dynam system can approxim unlik previous nnbase model identif method gbnnm direct inherit system dynam separ model system nonlinear model correspond well object system easi build gbnnm embed onlin normal model refer obtain quantit residu object system output gbnnm output residu can accur indic fault offset valu suitabl differ fault sever estim fault paramet fps improv extend state observ eso use nns iesonn gbnnm propos avoid requir knowledg eso nonlinear propos mife scheme appli reaction wheel rw satellit attitud control system sac scheme use gbnnm compar nns fault scenario sever partial loss effect loe fault differ sever consid valid effect fp estim superior

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