Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
Adaptive Reliable H∞ Filtering Against Sensor Failures
IEEE Transactions on Signal Processing - Part I
Reliable guaranteed variance filtering against sensor failures
IEEE Transactions on Signal Processing
Robust integral sliding mode control for uncertain stochastic systems with time-varying delay
Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
H∞ filtering with stochastic sampling
Signal Processing
H∞filter design for discrete-time system with lossy measurement: an LMI approach
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Parameter estimation with scarce measurements
Automatica (Journal of IFAC)
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Sliding mode control for stochastic systems subject to packet losses
Information Sciences: an International Journal
Networked control for a class of T--S fuzzy systems with stochastic sensor faults
Fuzzy Sets and Systems
Neural Processing Letters
Automatica (Journal of IFAC)
International Journal of Sensor Networks
Hi-index | 22.16 |
This paper is concerned with the filtering problem for a class of discrete-time uncertain stochastic nonlinear time-delay systems with both the probabilistic missing measurements and external stochastic disturbances. The measurement missing phenomenon is assumed to occur in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [01]. Such a probabilistic distribution could be any commonly used discrete distribution over the interval [01]. The multiplicative stochastic disturbances are in the form of a scalar Gaussian white noise with unit variance. The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing, stochastic disturbances, norm-bounded uncertainties as well as stochastic nonlinearities, the error dynamics of the filtering process is exponentially mean-square stable. By using the linear matrix inequality (LMI) method, sufficient conditions are established that ensure the exponential mean-square stability of the filtering error, and then the filter parameters are characterized by the solution to a set of LMIs. Illustrative examples are exploited to show the effectiveness of the proposed design procedures.