Abstract
The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the fault diagnosis of nonlinear systems. In particular, the methodology of designing a neural network-based fault estimator is developed. The main novelty of the approach is associated with possibly simultaneous sensor and actuator faults under imprecise measurements. For this purpose, a linear parameter-varying description of a recurrent neural network is exploited. The proposed approach guaranties a predefined disturbance attenuation level and convergence of the estimator. In particular, it uses the quadratic boundedness approach to provide uncertainty intervals of the achieved estimates. The final part of the paper presents an illustrative example concerning the application of the proposed approach to the multitank system fault diagnosis.
Highlights
In the contemporary world a continuous technological development and improvement of innovative systems and processes with the simultaneous minimization of the operation cost can be observed
The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the fault diagnosis of nonlinear systems
One of the leading research trends is devoted to developing novel fault-tolerant control (FTC) schemes, which have numerous applications in the
Summary
In the contemporary world a continuous technological development and improvement of innovative systems and processes with the simultaneous minimization of the operation cost can be observed. The observer-based FTC is difficult to apply for the systems when the analytical model of the diagnosed system is too complex or unavailable Such difficulty can be overcome by the application of the artificial neural networks (ANNs) [15, 19]. To combine the benefits of the ANNs and observerbased approaches for their applications in the FTC purposes, a new methodology of designing neural scheme of the faults estimation with its uncertainty description is proposed. The description of the system by the set of a bilinear matrix inequalities (BMIs) due to application of the linear matrix inequalities (LMIs) approach allows to obtain the fault estimate with their uncertainty description Such knowledge allows to obtain adaptive thresholds which can be applied to design an advanced FTC system.
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