Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

This paper deals with convergence analysis of the extended Kalman filters (EKFs) for sensorless motion control systems with induction motor (IM). An EKF is tuned according to a six-order discrete-time model of the IM, affected by system and measurement noises, obtained by applying a first-order Euler discretization to a six-order continuous-time model. Some properties of the discrete-time model have been explored. Among these properties, the observability property is relevant, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has b…

Estimation of turbulence and state based on EKF for a tandem Canard UAV

This paper deals with the state and turbulence estimation of a model describing the longitudinal dynamics of an Unmanned Aerial Vehicle (UAV). Due to both the high nonlinearities of the model and the stochastic nature of disturbances, an Extended Kalman Filter (EKF) is proposed. To allow the estimator to be employed on low cost UAV systems, it is assumed that the aircraft is equipped with a low performance GPS, characterized by a relatively low refresh rate. The designed EKF is able to work efficiently in both turbulent and calm atmosphere. In order to obtain information about the performances of the proposed estimator for control purposes, a control system, consisting of the EKF, a PID-typ…

Turbolence and State Estimation Via Extended Kalman Filter for a Non Conventional UAV

Decentralized Kalman Filter Based Robot Control

State Estimation of a Nonlinear Unmanned Aerial Vehicle Model using an Extended Kalman Filter