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- W2904324180 abstract "For building a control system for automated driving vehicles one should consider on one side the ability for the vehicle to drive on a desired trajectory, and on the other side to ensure passenger comfort and safety. As the known predictive control strategy is able to implement optimal control over a future horizon, it is the best suited for automated vehicles in steering and accelerating or braking maneuvers. In this paper is presented a comparison between generalized predictive control (GPC) approach and nonlinear model predictive control (NMPC) for implementing a control system for the lateral dynamics of the vehicle in autonomous driving. For both control strategies the single-track model of the vehicle is employed. In the NMPC, it is considered a model based on the nonlinearities of the tire lateral forces and an online optimization algorithm. The proposed GPC considers a linear model for the lateral dynamics of the vehicle. The control signal for both approaches will be considered the front wheel steering angle, assuming constant vehicle speed. The results of the simulations show that both proposed control strategies can achieve good performance in automated driving." @default.
- W2904324180 created "2018-12-22" @default.
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- W2904324180 date "2018-10-01" @default.
- W2904324180 modified "2023-09-27" @default.
- W2904324180 title "Comparison Between Generalized Predictive Control and Nonlinear Predictive Control for Automated Ground Vehicles" @default.
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- W2904324180 doi "https://doi.org/10.1109/icepe.2018.8559674" @default.
- W2904324180 hasPublicationYear "2018" @default.
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