This paper focusses on a discrete-time neural identifier applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and par...

This paper provides methods and experimental results for recursively estimating the sprung mass of a vehicle driving on rough terrain. It presents a base-excitation model of vertical ride dynamics which treats the unsprung vertical accelerations, instead of the terrain profile, as the ride dynamics model input. It employs recently developed methods based on polynomial chaos theory and on the ma...

The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor’s angular velocity is estimated by an Extended Kalman Filter which processesmeasurements of the rotor’s angle. Sensorl...

In this work we combine ideas from multi-index Monte Carlo and ensemble Kalman filtering (EnKF) to produce a highly efficient method called EnKF (MIEnKF). MIEnKF is based on independent samples of four-coupled estimators hierarchy resolution levels, it may be viewed as an extension the multilevel (MLEnKF) developed by same authors in 2020. Multi-index here refers two-index method, consisting th...

Kalman filtering is very efficient for data fusion, in which the definition of the process and measurement noises (i.e. the matrices Q and R, respectively) greatly influences the filter performance. In recent years several studies reported that adjustments of Q and R can be helpful to reduce the errors of the estimations. In this paper, various methods for making adjustments to the matrices Q a...