| Vehicle’s Lateral Motion Control Using Dynamic Mode Decomposition Model Predictive Control for Unknown Model
|
| Guntae Kim 1, Chaehun Park 2, Cheolmin Jeong 1, Chang Mook Kang 1, Jaeil Cho 3, Hyungchae Lee 3, Jaeho Lee 4, Donghyun Kang 4 |
1Department of Electrical Engineering , Incheon National University
2Intelligent Transportation Systems R&D Group , KATECH
3Ground Current Capability Technology Team , DRATR
4Advanced Technology Research Team , Hyundai-Rotem |
|
|
|
|
|
|
|
| ABSTRACT |
|
In this paper, we present a data-driven modeling method for lateral motion control of unknown vehicle models. Vehicle’s motion can be modeled linearly but this model has complex and nonlinear characteristic. Therefore, it is necessary to know the exact information of the car chassis and requires a knowledge and understanding of dynamics. To solve these drawbacks, we linearly represent full vehicle's lateral dynamics which include nonlinear behavior using dynamic mode decomposition (DMD), one of the data driven modeling methods. To determine the validity of the model obtained using the DMD method, we conducted a simulation of the comparison of the output states between the existing model and the model obtained through DMD modeling, using the scenario of a dynamic maneuver called a double line change during lateral motion of a vehicle. After determination of validation is completed, we designed a lane keeping system by applying a model predictive control to specifically evaluate the model of the proposed method. Performance was derived by comparing the error caused by the vehicle driving on the course with the controller of the simulation. The performance of the proposed approach has been evaluated through simulations and is useful when the model is inaccurate.
|
| Key Words:
Data-driven modeling, Dynamic mode decomposition, Model predictive control, Lane-keeping control
|
|
Corresponding Author.
Chang Mook Kang , Email. 
Preview 
Full text via DOI 
Download Citation
Print
