Paper Info
Title
Application Specific System Identification for Model-Based Control in Self-Driving Cars
Abstract
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.
Year
DOI
Venue
2020
10.1109/IV47402.2020.9304586
2020 IEEE Intelligent Vehicles Symposium (IV)
Keywords
DocType
ISSN
application specific system identification,self-driving cars,Linear Parameter Varying models,vehicular lateral dynamic behavior,model-based control schemes,model predictive control,real-time trajectory tracking control,proper trade-off,reduced computation cost,nonlinear models,MPC control schemes,long prediction horizon,prediction errors,long time horizon,system identification procedure,lateral dynamics,LPV model,dynamic systems,vehicular lateral dynamic models
Conference
1931-0587
ISBN
Citations 
PageRank 
978-1-7281-6674-2
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Julian M. Salt Ducaju100.68
Chen Tang200.34
Masayoshi Tomizuka300.68
Ching-Yao Chan47923.48