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• W4313854485 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." @default.
• W4313854485 created "2023-01-10" @default.
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• W4313854485 date "2022-11-27" @default.
• W4313854485 modified "2023-09-24" @default.
• W4313854485 title "Data-Driven Modeling and Control for Lane Keeping System of Automated Driving Vehicles: Koopman Operator Approach" @default.
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• W4313854485 doi "https://doi.org/10.23919/iccas55662.2022.10003764" @default.
• W4313854485 hasPublicationYear "2022" @default.
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