Hierarchical Nonlinear Model Predictive Control for an Autonomous Racecar

Benedikt Wohner, Francisco Sevilla, Benedikt Grueter, Johannes Diepolder, Rubens Afonso, Florian Holzapfel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper presents an optimization-based hierarchical motion planning and control architecture for autonomous racing that enables fast feedback times without significantly compromising on model accuracy. The online control scheme is divided into a high level NMPC for time-optimal trajectory planning and a low level NMPC for trajectory tracking using the same vehicle model. We numerically validate our approach in terms of computational efficiency and closed-loop performance in a detailed vehicle dynamics simulation environment. Despite considerable model-plant mismatches, the proposed hierarchical NMPC controller achieves a driven lap time in simulation which is only marginally slower than the theoretically optimal trajectory calculated offline via Optimal Control.

Original languageEnglish
Title of host publication2021 20th International Conference on Advanced Robotics, ICAR 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages113-120
Number of pages8
ISBN (Electronic)9781665436847
DOIs
StatePublished - 2021
Event20th International Conference on Advanced Robotics, ICAR 2021 - Ljubljana, Slovenia
Duration: 6 Dec 202110 Dec 2021

Publication series

Name2021 20th International Conference on Advanced Robotics, ICAR 2021

Conference

Conference20th International Conference on Advanced Robotics, ICAR 2021
Country/TerritorySlovenia
CityLjubljana
Period6/12/2110/12/21

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