Attitude Estimation and Grip Margin Evaluation of a Sport Motorcycle

This thesis work is divided into 2 macro-projects. The first deals with the estimation of the attitude of the motorcycle, with particular focus on the sideslip angle; the sideslip angle is estimated in 2 different ways: through an Extended Kalman Filter (EKF) and through a Black Box model. The second part deals with the understanding and quantification of the Grip Margin available on the tires. Both projects have the goal of being implemented on-board a Ducati sport motorcycle. The company supported the theses with knowledge and experimental data coming from real track sessions.

Estimating the attitude and the exploited grip of the tire is of paramount importance to enhance safety and maximize the performance of a motorcycle through the use of control systems. This thesis work consists of two different, and consecutive projects. The first project aimed to provide a consistent and accurate estimate of the attitude of the motorcycle, with particular focus on the sideslip angle. The second project instead, dealt with the evaluation of the grip margin available to the motorcycle. To deal with the first project, two different and completely independent methods have been developed: a black box approach, exploiting a model built on ground truth data acquired for different track sessions; then another approach using an Extended Kalman Filter algorithm, through which the whole attitude of the motorcycle is estimated, and not only sideslip. For the second project instead, different performance indicators have been defined. An indicator was dedicated to identify grip losses; then other indicators have been designed to evaluate the margin of grip available to the tire, using concepts like the friction ellipse and saturation of the contact forces. With regard to the first project, both approaches to the problem were successful. The two completely independent approaches led to a very similar sideslip estimate. The Root Mean Square Error, averaged on laps, of the estimates has been evaluated with the knowledge of ground truth data, available thanks to special optical sensors installed on the motorcycle. The two estimates have approximately halved the average error, on more than 100 laps of data, compared to the sideslip estimate of a third party control unit, currently installed on-board the vehicle. For the second project, very efficient and representative KPIs have been defined. The first KPI dedicated to identifying grip losses has been proven to be very effective comparing it with ground truth data of sideslip. Then, highly representative KPIs have been defined to evaluate the grip margin, validated through the correlation with the KPI for grip losses. In particular, a KPI that combines longitudinal and lateral forces saturation has been chosen as most representative. The achievements of these two projects are of particular importance for the development of control systems on the production motorcycle.