In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

The performance of the selective catalytic reduction (SCR) system has been confirmed to be distinctly affected by hydrothermal aging fault. In this paper, an observer based on Unscented-Kalman-Filter (UKF) algorithm theory is designed identify states so that SCR can more accurately and efficiently diagnosed fault-tolerant controlled. Furthermore, a model reference adaptive controller (MRAC) Lya...

In this paper we investigate the use of an alternative to the extended Kalman filter (EKF), the unscented Kalman filter (UKF). First we give a broad overview of different UKF algorithms, then present an extension to the ensemble of UKF algorithms, and finally address the issue of how to add constraints using the UKF approach. The performance of the constrained approach is compared with EKF and ...

Due to the randomness and uncertainty in atmospheric environment, accompanied by a variety of unknown noise. Accurate prediction PM 2.5 concentration is very important for people prevent injury effectively. In order predict more accurately this hybrid modelling method support vector regression adaptive unscented Kalman filter (SVR-AUKF) proposed case incorrect or Firstly, model was established ...

This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian synchrophasor measurement noise. The tuning of the parameters of our Generalized MaximumLikelihood-type robust UKF (GM-UKF) is presented and discussed in a systematic way. Using simulations c...

This work proposes a novel application of existing powerful nonlinear filters, such as the standard Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented by a large number of parameters when parameterized. We couple these filters with the po...