Application Specific System Identification for Model-Based Control in Self-Driving Cars
Salt Ducaju, Julian M. LU
; Tang, Chen
; Tomizuka, Masayoshi
and Chan, Ching Yao
(2020)
31st IEEE Intelligent Vehicles Symposium, IV 2020
p.384-390
- 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... (More)
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.
(Less)
- author
- Salt Ducaju, Julian M.
LU
; Tang, Chen
; Tomizuka, Masayoshi
and Chan, Ching Yao
- organization
- publishing date
- 2020
- type
- Contribution to conference
- publication status
- published
- subject
- pages
- 7 pages
- conference name
- 31st IEEE Intelligent Vehicles Symposium, IV 2020
- conference location
- Virtual, Las Vegas, United States
- conference dates
- 2020-10-19 - 2020-11-13
- external identifiers
-
- scopus:85099877342
- DOI
- 10.1109/IV47402.2020.9304586
- language
- English
- LU publication?
- yes
- id
- ba80907c-b10f-4993-aa53-04c96bc928c4
- date added to LUP
- 2021-02-10 11:40:53
- date last changed
- 2023-10-09 15:06:15
@misc{ba80907c-b10f-4993-aa53-04c96bc928c4,
abstract = {{<p>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.</p>}},
author = {{Salt Ducaju, Julian M. and Tang, Chen and Tomizuka, Masayoshi and Chan, Ching Yao}},
language = {{eng}},
pages = {{384--390}},
title = {{Application Specific System Identification for Model-Based Control in Self-Driving Cars}},
url = {{https://lup.lub.lu.se/search/files/160718247/Paper_IEEE_IV20_Final_USformat.pdf}},
doi = {{10.1109/IV47402.2020.9304586}},
year = {{2020}},
}