Name
Title | ||
|---|---|---|
A Neural-Network-Based Robust Observer for Simultaneous Unknown Input Decoupling and Fault Estimation. |
Abstract | ||
|---|---|---|
The paper deals with the problem of neural-network based on robust unknown input observer design for the fault diagnosis. Authors review the recent development in the area of robust observers for nonlinear discrete-time systems and propose less restrictive procedure for design of the H-infinity observer. The approach guaranties simultaneously the unknown input decoupling and the fault estimation. The paper presents an unknown input observer design that reduces to a set of linear matrix inequalities. The final part of the paper presents an illustrative example devoted to fault diagnosis of the wind turbine. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19258-1_44 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I (IWANN 2015) |
Keywords | Field | DocType |
Takagi-Sugeno systems,System identification,Artificial neural networks,Sector non-linearities,Observer,Robustness,Fault diagnosis | Computer science,Matrix (mathematics),Control theory,Decoupling (cosmology),Robustness (computer science),Turbine,Observer (quantum physics),System identification,Artificial neural network | Conference |
Volume | ISSN | Citations |
9094 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Witczak | 1 | 10 | 2.56 |
| Marcin Mrugalski | 2 | 63 | 9.65 |
| Krzysztof Patan | 3 | 151 | 18.13 |
| Marcin Witczak | 4 | 147 | 23.87 |