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An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

100%

Mrugalski M.

International Journal of Applied Mathematics and Computer Science

2013

tom 23

nr 1

157-169

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.

Design and Implementation of a UKF-based SOC Estimator for LiMnO2 Batteries Used on Electric Vehicles

63%

Dai H. , Wei X. , Sun Z.

2012

tom R. 88, nr 1b

57-63

To ensure safe and reliable battery operations, an accurate battery state of charge (SOC) estimation is critical for the battery systems used in electric vehicles and hybrid electric vehicles because of the arduous operation conditions. This paper presents a SOC estimator designed based on the unscented Kalman filter (UKF), which is very popular in the state estimation in non-linear systems. The dynamic characteristics of the battery are modeled with an equivalent circuit, which is composed of two capacitors, three resistors and a voltage source to simulate the equilibrium open circuit voltage (OCV). To relieve the computation requirement of the original UKF, an efficient implementation using a Cholesky factorization is investigated, and thereby a SR-UKF based SOC estimator is proposed. Experiment results shows that the model proposed can track the dynamic behavior of the battery very well and the UKF-based SOC estimator has a good performance in the state estimation, and a comparison with EKFbased estimator also shows that a better accuracy can be got by the proposed UKF- based estimator.

Artykuł prezentuje system kontroli SOC (state of charge – stan naładowania) baterii używanych w pojazdach elektrycznych. System bazuje na filtrze Kalmana typu UKF. Własności dynamiczne baterii modelowane są przy pomocy odpowiedniego obwodu elektrycznego zastępczego. System może śledzić właściwości dynamiczne baterii i badać jej stan naładowania.

Estymacja położenia przy użyciu bezśladowego filtru Kalmana

63%

Konatowski S.

2006

tom Vol. 52, z. 2

229-243

Proces estymacji położenia w zintegrowanych systemach nawigacyjnych jest często realizowany na nieliniowych modelach systemów. Nieliniowość dynamiki obiektu, którego pozycję należy estymować, wymaga stosowania odpowiednich filtrów. Powszechnie przyjętym rozwiązaniem jest rozszerzony filtr Kalmana oparty na linearyzacji funkcji nieliniowych. W artykule przedstawiono ideę bezśladowego filtru Kalmana wykorzystującego przekształcenie bezśladowe. Zaprezentowano wyniki badań symulacyjnych, które wykazują lepszą dokładność estymacji położenia przy użyciu bezśladowego niż rozszerzonego filtru Kalmana.

In integrated navigation systems different kinds of Kalman Filter working as error estimators or navigation algorithms are widely used. These filters work in time-discrete mode. Kalman Filters utilize information about dynamics of the object (system). Knowledge about dynamics and its correct modelling is the main issue in implementation of the Kalman Filters. In systems with linear dynamics, it is adequately to use basic Kalman Filter. Systems with nonlinear dynamics require linearization of the system model and in such case Extended Kalman Filter (EKF) is generally accepted. Unscented Kalman Filter (UKF) is an alternative for EKF. UKF is a recursive-estimating filter, which properties meet well requirements of strongly nonlinear systems. UKF does not linearize the model but manipulate on statistical parameters of nonlinear transformed state and measurement vector. UKF bases on Unscented Transform (UT). UT converts the state vector into a set of weighted Sigma Points. These points are than used in algorithms of UKF. The UKF algorithm is a set of equations, which are necessary to do prediction, innovation and correction steps. Simulation results of position estimation using EKF and UKF show that UKF used as data processing algorithm gives better accuracy of estimation in system with nonlinear dynamics than EKF. Nonlinearity in system used in simulation causes by transformation of co-ordination systems. Such situation takes place very often in navigation. This shows that UKF is more suitable to systems with strong nonlinearities than EKF. Better accuracy of position estimation using UKF calls for large number of computations (especially evaluation of matrix square root), what makes it more demanding for computation units of integrated navigation systems. UKF may also be used to estimate errors in integrated navigation system based on the compensation mode.