Model predictive longitudinal motion control for low velocities on known road profiles

Research on longitudinal motion control has so far mainly focused on high level planning and control techniques in high speed situations, for example highway driving, and the worst case scenarios there. Low level acceleration control is found only rarely in literature. With the development of automated vehicles, the controllers need to be able to handle the worst case scenarios at low velocities, such as traversing obstacles carefully. In this paper the authors present a novel approach for a low level acceleration controller that uses its knowledge of the road profile ahead to control engine and brakes proactively. The main contribution is the introduction of a suitable model predictive controller and its implementation in a real vehicle. In addition, the proposed control loop can be easily enhanced by employing existing control approaches. The authors present simulated data as well as experimental results for a state-of-the-art literature approach and their model predictive controller. Their data clearly shows that the control performance is significantly improved by their predictive solution.


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  • Accession Number: 01746804
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 8 2020 3:00PM