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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...

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....

Numerical studies of parameter estimation techniques for nonlinear evolution equations

Azmy S. Ackleh, Robert R. Ferdinand, Simeon Reich (1998)

Kybernetika

We briefly discuss an abstract approximation framework and a convergence theory of parameter estimation for a general class of nonautonomous nonlinear evolution equations. A detailed discussion of the above theory has been given earlier by the authors in another paper. The application of this theory together with numerical results indicating the feasibility of this general least squares approach are presented in the context of quasilinear reaction diffusion equations.

On adaptive control of a partially observed Markov chain

Giovanni Di Masi, Łukasz Stettner (1994)

Applicationes Mathematicae

A control problem for a partially observable Markov chain depending on a parameter with long run average cost is studied. Using uniform ergodicity arguments it is shown that, for values of the parameter varying in a compact set, it is possible to consider only a finite number of nearly optimal controls based on the values of actually computable approximate filters. This leads to an algorithm that guarantees nearly selfoptimizing properties without identifiability conditions. The algorithm is based...

On parameter estimation in the bass model by nonlinear least squares fitting the adoption curve

Darija Marković, Dragan Jukić (2013)

International Journal of Applied Mathematics and Computer Science

The Bass model is one of the most well-known and widely used first-purchase diffusion models in marketing research. Estimation of its parameters has been approached in the literature by various techniques. In this paper, we consider the parameter estimation approach for the Bass model based on nonlinear weighted least squares fitting of its derivative known as the adoption curve. We show that it is possible that the least squares estimate does not exist. As a main result, two theorems on the existence...

On-line parameter and delay estimation of continuous-time dynamic systems

Janusz Kozłowski, Zdzisław Kowalczuk (2015)

International Journal of Applied Mathematics and Computer Science

The problem of on-line identification of non-stationary delay systems is considered. The dynamics of supervised industrial processes are usually modeled by ordinary differential equations. Discrete-time mechanizations of continuous-time process models are implemented with the use of dedicated finite-horizon integrating filters. Least-squares and instrumental variable procedures mechanized in recursive forms are applied for simultaneous identification of input delay and spectral parameters of the...

Currently displaying 81 – 100 of 160