Recursive approach to networked fault estimation with packet dropouts and randomly occurring uncertainties

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

“…For example, only the uncertain noise variances, [5][6][7] only the multiplicative noises, 11,12,[15][16][17][18][19][20][21][22][23][24][25][26] and only the packet dropouts [28][29][30][31] were considered in previous works, respectively, and the packet dropouts 11,12,[15][16][17][18][19][20][21][22][23][24][25][26] and the uncertain noise variances [33][34][35][36][37] were not considered in other previous works, respectively. Further work will be to solve the robust fusion Kalman filtering problem for multisensor networked systems with mixed uncertainties.…”

“…The filtering problem for systems with multiplicative noises and different packet dropouts rates was addressed in the work of Qu and Zhou. 34 In the work of Song et al, 35 the fault estimation problem was considered for a class of time-varying networked systems in the simultaneous presence of randomly occurring uncertainties, stochastic nonlinearities, and packet dropouts. In the work of Chen et al, 36 the robust Kalman filtering problem was investigated for systems with random observation delays and missing measurements.…”

“…In particular, in the work of Ma and Sun, 37 by using the state augmentation approach, the filtering was considered for networked systems with multiplicative noises occur in both the state and measurement matrices, simultaneously with packet dropouts. The common limitation in other existing references [33][34][35][36][37] is that the uncertain noise variances have not been considered. Recently, in the work of Liu et al, 38 the robust measurement fusion prediction problem has been considered for systems with both the state-dependent and noise-dependent multiplicative noises and with missing measurements and uncertain additive noise variances.…”

Filter design, fault estimation and reliable control for networked time-varying systems: a survey

“…In Hou, Dong, Bu, & Yang (2016) and Huo, Chen, & Shen (2017), the estimator has been designed for discrete neural networks with randomly occurring uncertainties and missing measurements, and the eventbased robust state estimation problem has been solved in Wang, Fang, & Tian (2017) for discrete time-varying system with uncertain observations and randomly occurring uncertainties. Moreover, to deal with the fault estimation issue with packet dropouts and randomly occurring uncertainties, the recursive approach has been proposed in Song, Hu, Chen, Ji, & Liu (2016).…”

Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

“…Fault estimation [7][8][9][10][11] is supplementary to give the exact information of faults, thereby helping to reconstruct fault signals. Hence, it is further needed for the purpose of active fault-tolerant control to maintain the normal performance of systems.…”