A nonlinear model predictive control based virtual driver for high performance driving

Mattia Bruschetta, Enrico Picotti, Enrico Mion, Yutao Chen, Alessandro Beghi, Diego Minen

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

2 Citations (Scopus)

Abstract

Virtual prototyping is currently a widely used tool for the development of new cars. In this paper, the development of an effective virtual driver (VD) is described, that aims at reproducing real-time driver’s behaviour, also at the limit of performance. The proposed VD model, a four-wheel vehicle with longitudinal load transfer and Pacejka’s lateral tires forces model, has been implemented in the nonlinear model predictive control framework. The implementation is developed in MATMPC, a Matlab-based open-source toolbox, and tested in co-simulation with commercial software VI-CarRealTime (VI-CRT), specifically designed to reproduce vehicles behaviour. A challenging Double Lane Change (DLC) maneuver has been used to evaluate performance, showing great abilities of the proposed VD in handling track boundaries during high speed manoeuvring.
Original languageEnglish
Title of host publicationCCTA 2019 - 3rd IEEE Conference on Control Technology and Applications
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages9-14
Number of pages6
ISBN (Electronic)978-1-7281-2767-5
ISBN (Print)978-1-7281-2768-2
DOIs
Publication statusPublished - 5 Dec 2019
Event3rd 2019 IEEE Conference on Control Technology and Applications (CCTA2019) - China, Hong Kong, China
Duration: 19 Aug 201921 Aug 2019
https://ccta2019.ieeecss.org/

Conference

Conference3rd 2019 IEEE Conference on Control Technology and Applications (CCTA2019)
Abbreviated titleCCTA2019
CountryChina
CityHong Kong
Period19/08/1921/08/19
Internet address

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