TY - JOUR
T1 - Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems
AU - Rahimilarki, Reihane
AU - Gao, Zhiwei
AU - Zhang, Aihua
AU - Binns, Richard
N1 - Research funded by National Natural Science Foundation of China (61673074), American Friends of the Alexander von Humboldt Foundation (GRO/1117303), E&E Faculty, University of Northumbria
PY - 2019/12/5
Y1 - 2019/12/5
N2 - In this paper, a robust fault estimation approach is proposed for multi-input and multi-output nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.
AB - In this paper, a robust fault estimation approach is proposed for multi-input and multi-output nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.
KW - Artificial neural network (ANN)
KW - fault estimation
KW - input-to-state stability
KW - linear matrix inequality
KW - robustness
KW - wind turbine systems
U2 - 10.1109/TII.2019.2893845
DO - 10.1109/TII.2019.2893845
M3 - Article
AN - SCOPUS:85067456293
SN - 1551-3203
VL - 15
SP - 6302
EP - 6312
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 12
M1 - 8616801
ER -
