Abstract
AbstractIn this paper, a model predictive control (MPC) approach for the lateral and longitudinal control of a highly automated electric vehicle with all-wheel drive and dual-axis steering is presented. For the prediction of state trajectories a two-track vehicle model is used. The MPC problem for trajectory tracking is formulated by controlling the front and rear steering angle as well as the individual drive torques with respect to actuator and design constraints. Beside the steering angles, the MPC controller computes the individual drive torques to not only match the reference velocity but also to support the lateral dynamics of the vehicle using torque vectoring. The MPC problem is solved using ACADOS, a software package for efficiently solving optimal control problems. The effectiveness of the proposed MPC scheme is demonstrated via simulation.
Highlights
1.1 MotivationAutonomous driving is among the most active research areas in the current automotive industry with the aim to improve various aspects of mobility and transportation
The authors use a learning-based model predictive control (MPC) scheme for autonomous racing where a single track model is used as the nominal vehicle model which is improved based on measurement data and machine learning tools
A model predictive control approach for the lateral and longitudinal control of highly automated vehicles was presented with a focus on high positioning accuracy and maneuverability, required for driving in urban environments
Summary
Autonomous driving is among the most active research areas in the current automotive industry with the aim to improve various aspects of mobility and transportation. Ongoing trends towards Urbanization, Digitalization and Sustainability result in new vehicle concepts, e.g. FlexCar and U-Shift. Ongoing trends towards Urbanization, Digitalization and Sustainability result in new vehicle concepts, e.g. FlexCar and U-Shift2 Those platforms have in common that they are highly automated and explicitly developed for operating in urban environments, for example as people mover, equipped with electric all-wheel drive and dual-axis steering focusing on high maneuverability. Urban environments are of great interest to researchers due to the high density of vehicles and specific traffic rules that have to be followed [1]. These specified conditions in city traffic lead to new challenges regarding the motion control of vehicles demanding high position accuracy, e.g. the tracking of trajectories with large curvatures.
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