A GRAY-BOX NEURAL NETWORK-BASED MODEL IDENTIFICATION AND FAULT ESTIMATION SCHEME FOR NONLINEAR DYNAMIC SYSTEMS

作者：Cen Zhaohui; Wei Jiaolong^*; Jiang Rui

来源：International Journal of Neural Systems, 2013, 23(6): 1350025.

DOI：10.1142/S0129065713500251

• Model identification and fault estimation
• nonlinear dynamic systems
• gray-box neural-network model
• extended state observer
• reaction wheel

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.

• 出版日期2013-12
• 单位华中科技大学