A linear matrix inequality approach to robust H∞filtering
IEEE Transactions on Signal Processing
An extended Kalman filter frequency tracker for high-noiseenvironments
IEEE Transactions on Signal Processing
The extended Kalman filter as an exponential observer for nonlinearsystems
IEEE Transactions on Signal Processing
A new H∞ filter design for linear time delaysystems
IEEE Transactions on Signal Processing
State/noise estimator for descriptor systems with application to sensor fault diagnosis
IEEE Transactions on Signal Processing
State and input simultaneous estimation for a class of nonlinear systems
Automatica (Journal of IFAC)
Observer design for a class of MIMO nonlinear systems
Automatica (Journal of IFAC)
| Hi-index | 35.68 |
In this paper, we address the problem of state estimation and input recovery for a class of nonlinear systems in the presence of disturbances in both the state and output equations. Indeed one of the main difficulties, that arise when input recovery is considered, is how to cope with the problem of disturbance's derivative for the filter design. Our contribution lies in the use of Sobolev norms to develop a simple and useful observer. We provide first the state filtering and input recovery equations. After, based on the Lyapunov stiibility theory and some robustness criteria, new sufficient synthesis conditions are given in terms of linear matrix inequalities (LMIs). To show performances of the proposed method, we considered the problem of simultaneous synchronization and decryption in chaotic communication systems with some experimental results.