|Tipo di tesi||Tesi di laurea magistrale|
|Titolo||Stima dell’angolo di rollio di motoveicoli tramite filtro di Kalman esteso.|
|Titolo in inglese||Estimation of the roll angle in motorcycles by means of the extended Kalman filter.|
|Struttura||Dipartimento di Ingegneria "Enzo Ferrari"|
|Corso di studi||Ingegneria Del Veicolo (D.M.270/04)|
|Data inizio appello||2015-12-10|
|Disponibilità||Accesso limitato: si può decidere quali file della tesi rendere accessibili. Disponibilità mixed (scegli questa opzione se vuoi rendere inaccessibili tutti i file della tesi o parte di essi)|
|Data di rilascio||2055-12-10|
L’angolo di rollio è una delle grandezze più rappresentative della dinamica laterale dei motoveicoli, la sua stima costituisce un problema di notevole interesse industriale.
The roll angle is one of the most representative magnitudes in the lateral dynamics of motorcycles and its estimate is a problem of considerable industrial interest. The required sensors are inertial platforms, whose fundamental aspects are accuracy and cost. Thanks to the knowledge of the roll angle it is possible to know the status of a vehicle in operating conditions and consequently ensure the proper operation of driver assistance systems (traction control, ABS, etc). In this thesis in the first place the problem of estimating the attitude angle of vehicles in aviation industry was analysed. Subsequently both its application to land vehicles and the advantages the roll angle estimation brings to the calculation of slip, vehicle speed and lateral acceleration, that are the control variables of traction control and ABS, were studied. Then the structure of both the Kalman Filter (KF) and extended Kalman filter (EKF), the required mathematical tools such as linearization and discretization, the filtering of the measured quantities, the estimate of variables not measured by means of sensors and the estimation of parameters were investigated. Matlab procedures were also implemented on simple examples of dynamic systems. In the following step the focus was on competition vehicles and the operation of the code used by Ducati Corse was analysed. Thanks to the experimental data obtained by laser sensors it was possible to compare the estimates made by the control unit with direct measurements in order to evaluate the performance of the current EKF. The next phase was the tuning of the parameters governing the EKF algorithm currently in the CPU in order to obtain improvements in the estimation of the roll angle. The study of the development of a new EKF algorithm to improve the performance of the estimate has been advanced in the final part of the present study.