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

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

doi:10.1109/CCTA.2019.8920504

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 proceeding › Conference contribution › Academic › peer-review

5 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 language	English
Title of host publication	CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	9-14
Number of pages	6
ISBN (Electronic)	978-1-7281-2767-5
ISBN (Print)	978-1-7281-2768-2
DOIs	https://doi.org/10.1109/CCTA.2019.8920504
Publication status	Published - 5 Dec 2019
Event	3rd IEEE Conference on Control Technology and Applications, CCTA 2019 - Hong Kong, China Duration: 19 Aug 2019 → 21 Aug 2019 Conference number: 3 https://ccta2019.ieeecss.org/

Conference

Conference	3rd IEEE Conference on Control Technology and Applications, CCTA 2019
Abbreviated title	CCTA 2019
Country/Territory	China
City	Hong Kong
Period	19/08/19 → 21/08/19
Internet address	https://ccta2019.ieeecss.org/

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10.1109/CCTA.2019.8920504

Cite this

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title = "A nonlinear model predictive control based virtual driver for high performance driving",

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{\textquoteright}s behaviour, also at the limit of performance. The proposed VD model, a four-wheel vehicle with longitudinal load transfer and Pacejka{\textquoteright}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.",

author = "Mattia Bruschetta and Enrico Picotti and Enrico Mion and Yutao Chen and Alessandro Beghi and Diego Minen",

year = "2019",

month = dec,

day = "5",

doi = "10.1109/CCTA.2019.8920504",

language = "English",

isbn = "978-1-7281-2768-2",

pages = "9--14",

booktitle = "CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "3rd IEEE Conference on Control Technology and Applications, CCTA 2019, CCTA 2019 ; Conference date: 19-08-2019 Through 21-08-2019",

url = "https://ccta2019.ieeecss.org/",

}

Bruschetta, M, Picotti, E, Mion, E, Chen, Y, Beghi, A & Minen, D 2019, A nonlinear model predictive control based virtual driver for high performance driving. in CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications., 8920504, Institute of Electrical and Electronics Engineers, Piscataway, pp. 9-14, 3rd IEEE Conference on Control Technology and Applications, CCTA 2019, Hong Kong, China, 19/08/19. https://doi.org/10.1109/CCTA.2019.8920504

A nonlinear model predictive control based virtual driver for high performance driving. / Bruschetta, Mattia; Picotti, Enrico; Mion, Enrico et al.
CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications. Piscataway: Institute of Electrical and Electronics Engineers, 2019. p. 9-14 8920504.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - A nonlinear model predictive control based virtual driver for high performance driving

AU - Bruschetta, Mattia

AU - Picotti, Enrico

AU - Mion, Enrico

AU - Chen, Yutao

AU - Beghi, Alessandro

AU - Minen, Diego

N1 - Conference code: 3

PY - 2019/12/5

Y1 - 2019/12/5

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications

PB - Institute of Electrical and Electronics Engineers

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T2 - 3rd IEEE Conference on Control Technology and Applications, CCTA 2019

Y2 - 19 August 2019 through 21 August 2019

ER -

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

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