Paper Info
Title
Embedded Optimization Algorithms For Steering In Autonomous Vehicles Based On Nonlinear Model Predictive Control
Abstract
Steering control for autonomous vehicles on slippery road conditions, such as on snow or ice, results in a highly nonlinear and therefore challenging online control problem, for which nonlinear model predictive control (NMPC) schemes have shown to be a promising approach. NMPC allows to deal with the nonlinear vehicle dynamics as well as the system limitations and geometric constraints in a rather natural way, given a desired trajectory that can be provided by a supervisory algorithm for path planning. Our aim is to study the real-time feasibility of NMPC-based steering control on an embedded computer and the importance of the appropriate vehicle model selection, the optimization solver choice and control horizon length. The presented computation times have been obtained on a Raspberry Pi 2 model, as a proof of concept for a future real-world implementation on an embedded microprocessor.
Year
Venue
Field
2018
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC)
Motion planning,Optimal control,Control theory,Computer science,Model predictive control,Model selection,Control engineering,Vehicle dynamics,Proof of concept,Solver,Trajectory
DocType
ISSN
Citations 
Conference
0743-1619
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Rien Quirynen14711.88
Karl Berntorp22616.30
Stefano Di Cairano330944.69