2021/04/08 by Ali Gharib, David Stenger, David A. Stenger +6 · 3 citations
Computer Science · Engineering · Mathematics · #Advanced Control Systems Optimization #Artificial intelligence #Bayesian optimization #Bayesian probability #Computer science #Control (management) #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Machine learning #Mathematical optimization #Mathematics #Model predictive control #Multi-objective optimization #Path (computing) #Real-time simulation and control systems #Robotics (cs.RO) #Set (abstract data type) #Systems and Control (eess.SY) #Task (project management) #Vehicle Dynamics and Control Systems #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2104.03773
published in arXiv (Cornell University) (Cornell University) · This work has been accepted for publication at 2021 European Control Conference
arxiv created 2021/04/08 · openalex publication_date 2021/04/08 · arxiv updated 2021/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
This paper tackles the multi-objective optimization of the cost functional of\na path-following model predictive control for vehicle longitudinal and lateral\ncontrol. While the inherent optimal character of the model predictive control\nand the direct consideration of constraints gives a very powerful tool for many\napplications, is the determination of an appropriate cost functional a\nnon-trivial task. This results on the one hand from the number of degrees of\nfreedom or the multitude of adjustable parameters and on the other hand from\nthe coupling of these. To overcome this situation a Bayesian optimization\nprocedure is present, which gives the possibility to determine optimal cost\nfunctional parameters for a given desire. Moreover, a Pareto-front for a whole\nset of possible configurations can be computed.\n