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Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol

Fei Han, Wei Gao, Hongyu Gao, Qianqian He (2020)

Kybernetika

This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on [ 0 , 1 ] , is utilized to formulate the occurrence of the missing measurements. Also, an event generator function...

Non-fragile sampled data H ∞ filtering of general continuous Markov jump linear systems

Mouquan Shen, Guangming Zhang, Yuhao Yuan, Lei Mei (2014)

Kybernetika

This paper is concerned with the non-fragile sampled data H ∞ filtering problem for continuous Markov jump linear system with partly known transition probabilities (TPs). The filter gain is assumed to have additive variations and TPs are assumed to be known, uncertain with known bounds and completely unknown. The aim is to design a non-fragile H ∞ filter to ensure both the robust stochastic stability and a prescribed level of H ∞ performance for the filtering error dynamics. Sufficient conditions for...

Nonlinear Bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation

Lenka Pavelková (2011)

Kybernetika

The paper deals with parameter and state estimation and focuses on two problems that frequently occur in many practical applications: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time non-linear state space model whose uncertainties are bounded is proposed. The algorithm also copes with situations when some measurements are missing. It uses Bayesian approach and evaluates maximum a posteriori probability (MAP) estimates of states...

Nonlinear filtering for Markov systems with delayed observations

Antonella Calzolari, Patrick Florchinger, Giovanna Nappo (2009)

International Journal of Applied Mathematics and Computer Science

This paper deals with nonlinear filtering problems with delays, i.e., we consider a system (X,Y), which can be represented by means of a system (X,Ŷ), in the sense that Yt = Ŷa(t), where a(t) is a delayed time transformation. We start with X being a Markov process, and then study Markovian systems, not necessarily diffusive, with correlated noises. The interest is focused on the existence of explicit representations of the corresponding filters as functionals depending on the observed trajectory....

Nonlinear state observers and extended Kalman filters for battery systems

Andreas Rauh, Saif S. Butt, Harald Aschemann (2013)

International Journal of Applied Mathematics and Computer Science

The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations...

Nonlinear state prediction by separation approach for continuous-discrete stochastic systems

Jaroslav Švácha, Miroslav Šimandl (2008)

Kybernetika

The paper deals with a filter design for nonlinear continuous stochastic systems with discrete-time measurements. The general recursive solution is given by the Fokker–Planck equation (FPE) and by the Bayesian rule. The stress is laid on the computation of the predictive conditional probability density function from the FPE. The solution of the FPE and its integration into the estimation algorithm is the cornerstone for the whole recursive computation. A new usable numerical scheme for the FPE is...

Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances

Karim Khémiri, Fayçal Ben Hmida, José Ragot, Moncef Gossa (2011)

International Journal of Applied Mathematics and Computer Science

This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance criteria....

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