A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analy...

_ Speed control of permanent magnet synchronous motor (PMSM) using Direct Torque Control has rapid response and good static and dynamic performance but less accurate since current, torque and flux linkage wave forms contain big ripples. To overcome these disadvantages stator flux linkage and rotor speed are estimated using Extended Kalman Filter. Therefore, sensorless speed control of surface p...

The unscented Kalman filter (UKF) is formulated for the continuous-discrete state space model. The exact moment equations are solved approximately by using the unscented transform (UT) and the measurement update is obtained by computing the normal correlation, again using the UT. In contrast to the usual treatment, the system and measurement noise sequences are included from the start and are n...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

This paper investigates the possibility estimating the direction of arrival (DOA) in a system identification perspective. The system is modeled as an autoregressive (AR) process and extended Kalman filter (EKF) is used to estimate the DOA, which forms a state of the augmented state vector of the EKF. The states generate the signals at a linearly phased array. Simulation results demonstrate the ...

Abstract: This paper presents an application of nonlinear filtering techniques for the tracking control design of tracked mobile robot under slip condition. The slip is represented only by the longitudinal wheels slip that is described by just an unknown parameter. The extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF) are used to estimate the states of...

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited sma...

This thesis investigates methods for estimating relative 3D position and pose from monocular image sequences. The intended future application is of one satellite observing another, when flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration and Kalman filter-based structure from motion (SfM). Each of the algorithms relies on visible...

Thispaper presents a method of simultaneous localization and mapping (SLAM) in indoor environment using extended Kalman filter (EKF) with the straight line segments as the adopted geometrical feature. By using conventional two dimensional laser range finder (LRF) as the main sensor, robot finds a number of points scanned from the surrounding environment.Split-and-Merge is one of the mostpopular...

Kalman filter is a well known adaptive filtering Algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like extended Kalman filter (EKF) and Unscented Kalman filters (UKF) are used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning an UKF is the p...