Robust Neural Network Fault Estimation Approach for Nonlinear Dynamic Systems With Applications to Wind Turbine Systems
- 17 January 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Informatics
- Vol. 15 (12) , 6302-6312
Abstract
In this paper, a robust fault estimation approach is proposed for multi-input and multioutput 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.Keywords
Funding Information
- National Natural Science Foundation of China (61673074)
- American Friends of the Alexander von Humboldt Foundation (GRO/1117303)
- E&E Faculty, University of Northumbria
This publication has 33 references indexed in Scilit:
- A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based ApproachesIEEE Transactions on Industrial Electronics, 2015
- Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy ModelsIEEE Transactions on Industrial Electronics, 2014
- Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated SignalsIEEE Transactions on Industrial Electronics, 2013
- Robust fault estimation approach and its application in vehicle lateral dynamic systemsOptimal Control Applications and Methods, 2006
- Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosisSystems & Control Letters, 2006
- A Stable Neural Network-Based Observer With Application to Flexible-Joint ManipulatorsIEEE Transactions on Neural Networks, 2006
- Nonlinear Model Identification of Wind Turbine With a Neural NetworkIEEE Transactions on Energy Conversion, 2004
- L/sub 2/-gain analysis of nonlinear systems and nonlinear state-feedback H/sub infinity / controlIEEE Transactions on Automatic Control, 1992
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989