Application Of Unscented Kalman Filter Research Articles

АНАЛІЗ ВПЛИВУ ІНДИВІДУАЛЬНИХ ХАРАКТЕРИСТИК КОМІРОК LiFePO4 НА ПАРАМЕТРИ РОБОТИ АКУМУЛЯТОРНИХ ЗБІРОК

The article analyzes the influence of the individual characteristics of lithium-iron-phosphate (LiFePO4) cells on the overall efficiency, stability and duration of operation of battery assemblies. The influence of such parameters as internal resistance, polarization resistance and capacity on the operation of battery assemblies under different load regimes and changes in temperature conditions was studied. It was found that these parameters can vary significantly between cells within the same assembly, which leads to an imbalance during the operation of the battery system. Such an imbalance causes an uneven distribution of charge between the cells, which reduces the overall performance of the system, accelerates the degradation of the cells and shortens their service life. To overcome these problems, the use of active balancing methods based on Unscented Kalman Filter (UKF) algorithms was proposed. The application of UKF allows for dynamic evaluation of the state of charge (SOC) of each cell and adjustment of system parameters in real time, which provides more accurate management of battery assemblies in conditions of variable external factors such as temperature and current loads. Special attention is paid to online identification of battery parameters, such as polarization resistance and capacity, which change over time due to cell aging. The conducted studies confirmed that constant monitoring of temperature regimes is critically important for maintaining stable operation of battery cells, preventing overheating and ensuring uniform load distribution. The performed simulation confirmed the effectiveness of the application of adaptive methods of balancing and temperature control, which allows to increase the reliability and overall performance of battery assemblies.

UKF-Based Optimal Tracking Control for Uncertain Dynamic Systems With Asymmetric Input Constraints.

To enhance system robustness in the face of uncertainty and achieve adaptive optimization of control strategies, a novel algorithm based on the unscented Kalman filter (UKF) is developed. This algorithm addresses the finite-horizon optimal tracking control problem (FHOTCP) for nonlinear discrete-time (DT) systems with uncertainty and asymmetric input constraints. An augmented system is constructed with asymmetric control constraints being considered. The augmented problem is addressed with a DT Hamilton-Jacobi-Bellman equation (DTHJBE). By analyzing convergence with regard to the cost function and control law, the UKF-based iterative adaptive dynamic programming (ADP) algorithm is proposed. This algorithm approximates the solution of the DTHJBE, ensuring that the cost function converges to its optimal value within a bounded range. To execute the UKF-based iterative ADP algorithm, the actor-estimator-critic framework is built, in which the estimator refers to system state estimation through the application of UKF. Ultimately, simulation examples are presented to show the performance of the proposed method.

Kronecker Product Based Modeling of Darlington Amplifier and State Estimation using Unscented Kalman Filter

ABSTRACT This paper presents the application of unscented Kalman filter (UKF) for output voltage estimation of the Darlington amplifier (DA). Implementation of UKF requires the state-space model of DA circuit, which has been obtained using Kirchhoff’s voltage law (KVL), Kirchhoff’s current law (KCL) and the Gummel–Poon model of the bipolar junction transistor (BJT). This work uses Kronecker product for vector multiplication. The mathematical representation of nonlinear system in terms of Kronecker product helps to reduce the computational burden involved in the vector multiplication. The proposed method has the advantage that it can be used for the nonlinear region and large amplitude input signal. As the maximal precision of estimation requires modelling in terms of circuit elements and device parameters, the proposed method is able to provide an accurate estimation. We compared the UKF estimation results with the extended Kalman filter (EKF) and iterated extended Kalman filter (IEKF). The UKF presents smaller mean square error (MSE) as compared to EKF and IEKF as UKF is accurate to the third order for any nonlinearity. In addition, IEKF presents smaller MSE as compared to EKF because the linearisation error is taken into account by IEKF.

Application of unscented Kalman filter for clutch position control of automated manual transmission

Application of unscented Kalman filter for clutch position control of automated manual transmission

A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.

해저 지형정보를 이용하는 수중 로봇 위치추정 방법의 구현 및 성능 비교

This paper proposes an application of unscented Kalman filter(UKF) for localization of an underwater robot. The method compares the bathymetric measurement from the robot with the seabed terrain information. For the measurement of bathymetric range to seabed, it uses a DVL which typically yields four range data together with velocity of the robot. Usual extended Kalman filter is not appropriated for application in case of terrain navigation, since it is not feasible to derive Jacobian for the bathymetric range measurement. Though particle filter(PF) is a nice solution which doesn`t require Jacobian and can deal with non-linear and non-Gaussian system and measurement, it suffers from heavy computational burden. The paper compares the localization performance and the computation time of the UKF approach and PF approach. Though there have been some UKF methods which are used for underwater navigation, application of the UKF for bathymetric localization is rare. Especially, the proposed method uses only four range data whereas many of the bathymetric navigation methods have used multibeam sonar which yields hundreds of scanned range data. The result shows feasibility of the UKF approach for terrain-based navigation using small numbers of range data.

Application of Unscented Kalman Filter for Condition Monitoring of an Organic Rankine Cycle Turbogenerator


 
 
 This work relates to a project focusing on energy optimization on offshore facilities. On oil and gas platforms it is common practice to employ gas turbines for power production. So as to increase the system performance and reduce emissions, a bottoming cycle unit can be designed with particular emphasis on compactness and reliability. In such context, organic Rankine cycle turbogenerators are a promising technology. The implementation of an organic Rankine cycle unit is thus considered for the power system of the Drau- gen offshore platform in the northern sea, which is the case study for this project. Considering the plant dynamics, it is of paramount importance to monitor the peak temperatures within the once-through boiler serving the bottoming unit to prevent the decomposition of the working fluid. This paper accordingly aims at applying the unscented Kalman filter to estimate the temperature distribution inside the primary heat exchanger by engaging a detailed and distributed model of the system and available measurements. Simulation results prove the robustness of the unscented Kalman filter with respect to process noise, measurement disturbances and initial conditions.
 
 

Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation

This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation, are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model, however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to those of conventional EKF.

Application of unscented Kalman filter in satellite orbit simulation

A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to nonlinear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.