Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems

Abstract

This paper is concerned with the finite-horizon estimation problem of randomly occurring faults for a class of nonlinear systems whose parameters are all time-varying. The faults are assumed to occur in a random way governed by two sets of Bernoulli distributed white sequences. The stochastic nonlinearities entering the system are described by statistical means that can cover several classes of well-studied nonlinearities. The aim of the problem is to estimate the random faults, over a finite horizon, such that the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by a H∞-norm in the mean square sense. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions are established for the existence of the desired finite-horizon H∞ fault estimator whose parameters are then obtained by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the effectiveness of the proposed fault estimation method.

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

“…Finally, an example has been given to illustrate the usefulness of the developed state estimation approach. The results in this paper could be further extended to the non-fragile state estimation problems for discrete neural networks with more complicated networkinduced phenomena such as fading measurements [4], [5], [10], [20], [26], missing measurements [8], sensor delays [9], randomly occurring faults [14] and mixed time-delays [31].…”

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

“…As is well known, the nonlinearity is a ubiquitous feature existing in almost all practical systems that contributes significantly to the complexity of system modeling [89], [103], [109]- [112]. The occurrence of the nonlinearity would cause undesirable dynamic behaviors.…”

“…It is well recognized that the existence of the randomly occurring incomplete information would highly degrade the system performance if not handled properly. So far, a series of estimation and filtering schemes has been developed for networked systems with randomly occurring incomplete information in the literature, and great efforts have been made to deal with the randomly occurring nonlinearities in [49], [95]- [99], the randomly occurring uncertainties in [94], [97], the randomly occurring sensor saturations in [40], [72], the randomly occurring sensor delays in [31], [32], [38], [100], [101], the randomly occurring signal quantization in [41], [102], and the randomly occurring faults in [103]. Accordingly, several techniques for analysis and synthesis of the networked systems have been given, including innovation analysis approach [31], [32], linear matrix inequality approach [97], Hamilton-Jacobi-Isaacs inequality method [100], difference linear matrix inequality method [41], Riccati difference equation approach [101], [102], and game theory method [54].…”

A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements

“…In particular, considerable effort has been made towards the sta-bility, synchronization, impulsive control, pinning control and state estimation problems for complex networks [20-22, 26, 28, 29]. In reality, the system states are not always available due probably to physical constraints, technological restrictions or expensive cost for measuring [5,7,12]. Therefore, the state estimation problem has recently received tremendous research interest [2,3,9].…”