Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at same time. However, it poses challenge since not all quantities interest can be directly measured due cost and/or technological reasons. Therefore, combining principle robust filtering and unscented particle algorithm, filter estimation method state proposed es...

The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the ExpectationMaximization (EM) algorithm)where both s...

Vehicle motion trajectory prediction is the basis of vehicle collision early warning or conflict resolution. In order to improve accuracy prediction, a method based on “current” statistical (CS) model and cubature Kalman filter (CKF) proposed. This considers acceleration variation rules in actual process state equation, so that estimated value can be consistent with real range. condition overco...

The routine use of pseudospectral (PS) optimal control techniques in space applications1 has generated new opportunities for performance enhancement. One of these opportunities is related to the interaction of an outer guidance loop with inner control loops. In current flight applications, knowledge of the initial state vector, padded with high conservatism, is considered necessary to maintain ...

Vehicle Dynamics Control (VDC) systems require the information about system variables, which cannot be directly measured, e.g., the wheel slip or the vehicle side-slip angle. This paper presents a new concept for the vehicle state estimation under assumption that the vehicle is equipped with the standard VDC sensors. It is proposed to utilize an Unscented Kalman Filter (UKF) for estimation purp...