Data-Driven Modeling and Control for Lane Keeping System of Automated Driving Vehicles: Koopman Operator Approach
- Authors
- Kim, Jin Sung; Quan, Ying Shuai; Chung, Chung Choo
- Issue Date
- Jan-2022
- Publisher
- IEEE
- Keywords
- Data-driven control; Koopman operator; Vehicle control
- Citation
- 2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022), pp.1049 - 1055
- Indexed
- SCOPUS
- Journal Title
- 2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022)
- Start Page
- 1049
- End Page
- 1055
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/184921
- DOI
- 10.23919/ICCAS55662.2022.10003764
- ISSN
- 2093-7121
- Abstract
- This paper proposes the data-driven modeling and control method with the Koopman operator for the lane-keeping system. The vehicle can be modeled as a linear motion model but has underlying complicated nonlinear behavior. Thus, there exists a need to model the full vehicle dynamics effectively. To this end, we use the Koopman operator to express the full vehicle nonlinear dynamics as a linear structure. However, it is not practical to use the Koopman operator directly because it lies in infinite-dimensional space. Hence, we apply the extended dynamic mode decomposition to approximate the Koopman operator as a finite-dimensional linear operator. We conduct a comparative study between the linear model-based optimal control and the Koopman operator-based optimal control. As a result, it is observed that the proposed method reduces the system state by 20% compared to the linear model-based controller.
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