Application Specific System Identification for Model-Based Control in Self-Driving Cars

Research output: Contribution to conferencePaper, not in proceeding


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.


External organisations
  • University of California, Berkeley
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Control Engineering
  • Vehicle Engineering
Original languageEnglish
Number of pages7
Publication statusPublished - 2020
Publication categoryResearch
Event31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, United States
Duration: 2020 Oct 192020 Nov 13


Conference31st IEEE Intelligent Vehicles Symposium, IV 2020
CountryUnited States
CityVirtual, Las Vegas