Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system. (March 2016) - Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe
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 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.