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

Julian M. Salt Ducaju, Chen Tang, Masayoshi Tomizuka, Ching Yao Chan

Forskningsoutput: KonferensbidragKonferenspaper, ej i proceeding/ej förlagsutgivetPeer review


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.

Antal sidor7
StatusPublished - 2020
Evenemang31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, USA
Varaktighet: 2020 okt. 192020 nov. 13


Konferens31st IEEE Intelligent Vehicles Symposium, IV 2020
OrtVirtual, Las Vegas

Ämnesklassifikation (UKÄ)

  • Reglerteknik
  • Farkostteknik


Utforska forskningsämnen för ”Application Specific System Identification for Model-Based Control in Self-Driving Cars”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här