This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, ...

The Extended Kalman Filter (EKF) has long been the de-facto standard for nonlinear state space estimation [11], primarily due to its simplicity, robustness and suitability for realtime implementations. However, an alternative approach has emerged over the last few years, namely the unscented Kalman filter (UKF). This filter claims both higher accuracy and robustness for nonlinear models. Severa...

Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and flexible generation of wheel individual propulsion braking torques. Exact robust estimation state motion in the presence unknown disturbances, changes road conditions, is crucial for realization systems. This article shows design, tuning, implementation, test a estimato...

Ellipticine is an antineoplastic agent, whose mode of action is based mainly on DNA intercalation, inhibition of topoisomerase II and formation of covalent DNA adducts mediated by cytochromes P450 and peroxidases. Here, the molecular mechanism of DNA-mediated ellipticine action in human neuroblastoma IMR-32, UKF-NB-3 and UKF-NB-4 cancer cell lines was investigated. Treatment of neuroblastoma ce...

Neuroblastoma is the most common cancer in infants and the fourth most common cancer in children. Aggressive cell growth and chemoresistance are notorious obstacles in neuroblastoma therapy. Exposure to the anticancer drug ellipticine inhibits efficiently growth of neuroblastoma cells and induces apoptosis in these cells. However, ellipticine induced resistance in these cells. The upregulation ...

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

Active estimation is becoming a more important issue in control theory and its application, especially in the nonlinear control of uncertain systems, such as robots and unmanned vehicles where time-varying parameters and uncertainties exist extensively in the dynamics and working environment. Among the available techniques for active modeling, Neural Networks (NN) and NN-based self learning hav...

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 Kalman Filter (UKF) algo...

The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated by the UKF are used for covariance estimation by MLE and CM. Then we apply the two covariance estimation me...