Linear Parameter Varying (LPV) models can be used to describe the vehicular lateral dynamic behavior of self-driving cars. They are particularly suitable for model-based control schemes such as model predictive control (MPC) applied to real-time trajectory tracking control, since they provide a proper trade-off between accuracy in different scenarios and reduced computation cost compared to nonlinear models. The MPC control schemes use the model for a long prediction horizon of the states, therefore prediction errors for a long time horizon should be minimized in order to increase the accuracy of the tracking. For this task, this work presents a system identification procedure for the lateral dynamics of a vehicle that combines a LPV model with a learning algorithm that has been successfully applied to other dynamic systems in the past. Simulation results show the benefits of the identified model in comparison to other well-known vehicular lateral dynamic models.
|Status||Published - 2020|
|Evenemang||31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, USA|
Varaktighet: 2020 okt. 19 → 2020 nov. 13
|Konferens||31st IEEE Intelligent Vehicles Symposium, IV 2020|
|Ort||Virtual, Las Vegas|
|Period||2020/10/19 → 2020/11/13|