bibtype J - Journal Article
ARLID 0479178
utime 20240111140947.0
mtime 20171009235959.9
SCOPUS 85030787288
WOS 000447047900004
DOI 10.1109/TCST.2017.2747504
title (primary) (eng) Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon
specification
page_count 12 s.
media_type P
serial
ARLID cav_un_epca*0253228
ISSN 1063-6536
title IEEE Transactions on Control Systems Technology
volume_id 26
volume 6 (2018)
page_num 1970-1981
publisher
name Institute of Electrical and Electronics Engineers
keyword Autonomous vehicles
keyword hybrid systems
keyword nonlinear model predictive control (MPC)
keyword optimization
keyword vehicle control
author (primary)
ARLID cav_un_auth*0281469
name1 Pčolka
name2 M.
country CZ
author
ARLID cav_un_auth*0305702
name1 Žáčeková
name2 E.
country CZ
author
ARLID cav_un_auth*0101074
name1 Čelikovský
name2 Sergej
full_dept (cz) Teorie řízení
full_dept Department of Control Theory
department (cz)
department TR
institution UTIA-B
full_dept Department of Control Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0021057
name1 Šebek
name2 M.
country CZ
source
url http://library.utia.cas.cz/separaty/2018/TR/celikovsky-0479178.pdf
source_size 6,39 MB
cas_special
project
ARLID cav_un_auth*0347203
project_id GA17-04682S
agency GA ČR
country CZ
abstract (eng) This paper focuses on the development of an optimization algorithm for car motion predictive control that addresses both hybrid car dynamics and hybrid minimization criterion. Instead of solving computationally demanding nonlinear mixed-integer programming task or approximating the hybrid dynamics/criterion, the Hamiltonian-switching hybrid nonlinear predictive control algorithm developed in this paper incorporates the information about hybridity directly into the optimization routine. To decrease the time complexity, several adaptive prediction horizon approaches are proposed, and for some of them, it is shown that they preserve maneuverability-related properties of the car. All developed alternatives are verified on an example of a motion control of a racing car and compared with the approximation-based nonlinear predictive control and a commercial product. Moreover, a sensitivity analysis examining robustness of the algorithm is included as well.
result_subspec WOS
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2019
num_of_auth 4
mrcbC52 4 A hod 4ah 20231122142712.7
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0275243
cooperation
ARLID cav_un_auth*0300364
name Department of Control Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, 121 35 Prague, Czech Republic
institution ČVUT v Praze, FEL
country CZ
mrcbC64 1 Department of Control Theory UTIA-B 20205 AUTOMATION & CONTROL SYSTEMS
confidential S
article_num 08059760
mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic
mrcbT16-e AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC
mrcbT16-j 1.538
mrcbT16-s 1.811
mrcbT16-B 84.752
mrcbT16-D Q1
mrcbT16-E Q1
arlyear 2018
mrcbTft \nSoubory v repozitáři: celikovsky-0479178.pdf
mrcbU14 85030787288 SCOPUS
mrcbU24 PUBMED
mrcbU34 000447047900004 WOS
mrcbU56 6,39 MB
mrcbU63 cav_un_epca*0253228 IEEE Transactions on Control Systems Technology 1063-6536 1558-0865 Roč. 26 č. 6 2018 1970 1981 Institute of Electrical and Electronics Engineers