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Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank System [Technical]

This post is an excerpt from the journal ISA Transactions. All ISA Transactions articles are free to ISA members, or can be purchased from Elsevier Press.

 

 

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 H1 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.

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