This paper tackles the multi-objective optimization of the cost functional of a path-following model predictive control for vehicle longitudinal and lateral control. While the inherent optimal character of the model predictive control and the direct consideration of constraints gives a very powerful tool for many applications, is the determination of an appropriate cost functional a non-trivial task. This results on the one hand from the number of degrees of freedom or the multitude of adjustable parameters and on the other hand from the coupling of these. To overcome this situation a Bayesian optimization procedure is present, which gives the possibility to determine optimal cost functional parameters for a given desire. Moreover, a Pareto-front for a whole set of possible configurations can be computed.
@article{arxiv.2104.03773,
title = {Multi-Objective Optimization of a Path-following MPC for Vehicle Guidance: A Bayesian Optimization Approach},
author = {Ali Gharib and David Stenger and Robert Ritschel and Rick Voßwinkel},
journal= {arXiv preprint arXiv:2104.03773},
year = {2021}
}
Comments
This work has been accepted for publication at 2021 European Control Conference