Autonomous Vehicle Control: A Nonconvex Approach for Obstacle Avoidance

Ugo Rosolia, Stijn De Bruyne, Andrew G. Alleyne

Research output: Contribution to journalArticle

Abstract

This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions. The goal of the controller is to track the centerline of the roadway while avoiding obstacles. An outer-loop nonlinear model predictive control is adopted for generating the collision-free trajectory with the resultant input based on a simplified vehicle model. The optimization is solved through the generalized minimal residual method augmented with a continuation method. A sufficient condition to overcome limitations associated with continuation methods is introduced. The inner loop is a simple linear feedback controller based on an optimal preview distance. Simulation results illustrate the effectiveness of the approach. These are bolstered by scaled-vehicle experimental results.

Original languageEnglish (US)
Article number7489011
Pages (from-to)469-484
Number of pages16
JournalIEEE Transactions on Control Systems Technology
Volume25
Issue number2
DOIs
StatePublished - Mar 2017

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Collision avoidance
Controllers
Model predictive control
Trajectories
Feedback

Keywords

  • Autonomous vehicle
  • collision avoidance
  • nonconvex optimization
  • nonlinear model predictive control (NLMPC)
  • path following
  • real-time optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Autonomous Vehicle Control : A Nonconvex Approach for Obstacle Avoidance. / Rosolia, Ugo; De Bruyne, Stijn; Alleyne, Andrew G.

In: IEEE Transactions on Control Systems Technology, Vol. 25, No. 2, 7489011, 03.2017, p. 469-484.

Research output: Contribution to journalArticle

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