Nonlinear H∞ -control and estimation of optimal H∞ -gain
Systems & Control Letters
Passivity and Passification for Networked Control Systems
SIAM Journal on Control and Optimization
Automatica (Journal of IFAC)
Robust filtering with randomly varying sensor delay: the finite-horizon case
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust H∞ filtering for nonlinear stochastic systems
IEEE Transactions on Signal Processing
A New Fault Detection Scheme for Networked Control Systems Subject to Uncertain Time-Varying Delay
IEEE Transactions on Signal Processing - Part II
H∞ nonlinear filtering of discrete-time processes
IEEE Transactions on Signal Processing
Brief Nonlinear H∞ filtering of sampled-data systems
Automatica (Journal of IFAC)
H∞ filtering with stochastic sampling
Signal Processing
Synchronization in complex dynamical networks with random sensor delay
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Signal Processing
Networked H∞ filtering for linear discrete-time systems
Information Sciences: an International Journal
Automatica (Journal of IFAC)
Event-based H∞ filtering for networked system with communication delay
Signal Processing
Gaussian filter for nonlinear systems with one-step randomly delayed measurements
Automatica (Journal of IFAC)
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Automatica (Journal of IFAC)
Hi-index | 22.16 |
This paper is concerned with the H"~ filtering problem for a general class of nonlinear discrete-time stochastic systems with randomly varying sensor delays, where the delayed sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law. In terms of the Hamilton-Jacobi-Isaacs inequalities, preliminary results are first obtained that ensure the addressed system to possess an l"2-gain less than a given positive scalar @c. Next, a sufficient condition is established under which the filtering process is asymptotically stable in the mean square and the filtering error satisfies the H"~ performance constraint for all nonzero exogenous disturbances under the zero-initial condition. Such a sufficient condition is then decoupled into four inequalities for the purpose of easy implementation. Furthermore, it is shown that our main results can be readily specialized to the case of linear stochastic systems. Finally, a numerical simulation example is used to demonstrate the effectiveness of the results derived.