Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system. (March 2016)
- Record Type:
- Journal Article
- Title:
- Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system. (March 2016)
- Main Title:
- Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system
- Authors:
- Mrugalski, Marcin
Luzar, Marcel
Pazera, Marcin
Witczak, Marcin
Aubrun, Christophe - Abstract:
- Abstract: The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H ∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Abstract : Highlights: In this paper, a robust neural-network-based actuator fault estimation approach for a class of non-linear systems is proposed, which can be efficiently applied to realize the fault diagnosis three-step procedure within a unified framework. The robustness issue is attacked by minimizing the influence of exogenous external disturbances. The proposed description of neural network-based linear parameter-varying framework constitutes an alternative to the approaches presented in the literature. The main contribution of the paper is the design procedure of an observer-based fault identification scheme for which a prescribed disturbanceAbstract: The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H ∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Abstract : Highlights: In this paper, a robust neural-network-based actuator fault estimation approach for a class of non-linear systems is proposed, which can be efficiently applied to realize the fault diagnosis three-step procedure within a unified framework. The robustness issue is attacked by minimizing the influence of exogenous external disturbances. The proposed description of neural network-based linear parameter-varying framework constitutes an alternative to the approaches presented in the literature. The main contribution of the paper is the design procedure of an observer-based fault identification scheme for which a prescribed disturbance attenuation level is achieved with respect fault estimation error. The performance of the proposed approach is evaluated and compared with a different approach using the laboratory multi-tank system. … (more)
- Is Part Of:
- ISA transactions. Volume 61(2016:Mar.)
- Journal:
- ISA transactions
- Issue:
- Volume 61(2016:Mar.)
- Issue Display:
- Volume 61 (2016)
- Year:
- 2016
- Volume:
- 61
- Issue Sort Value:
- 2016-0061-0000-0000
- Page Start:
- 318
- Page End:
- 328
- Publication Date:
- 2016-03
- Subjects:
- Robust fault diagnosis -- Fault estimation -- Non-linear systems identification -- Observers -- Neural network -- LPV systems
Engineering instruments -- Periodicals
Engineering instruments
Periodicals
Electronic journals
629.805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00190578 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.isatra.2016.01.002 ↗
- Languages:
- English
- ISSNs:
- 0019-0578
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4582.700000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9029.xml