Reference : Deadzone-Quadratic Penalty Function for Predictive Extended Cruise Control with Exper...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/33259

Title :	Deadzone-Quadratic Penalty Function for Predictive Extended Cruise Control with Experimental Validation
Language :	English
Author, co-author :	Sajadi Alamdari, Seyed Amin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
	Darouach, Mohamed [Centre de Recherche en Automatique de Nancy (CRAN) > Universite de Lorraine]
Publication date :	Nov-2017
Main document title :	ROBOT 2017: Third Iberian Robotics Conference, Sevilla, Spain 22-24 November 2017
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	ROBOT 2017: Third Iberian Robotics Conference
Event date :	from 22-11-2017 to 24-11-2017
Keywords :	[en] Nonlinear Model Predictive Control ; Driver Assistance System ; Intelligent Transportation Systems
Abstract :	[en] Battery Electric Vehicles have high potentials for the modern transportations, however, they are facing limited cruising range. To address this limitation, we present a semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques. The main contribution of this paper is the design of a real-time nonlinear receding horizon optimal controller to plan the online cost-effective cruising velocity. Instead of conventional L2-norms, a deadzone-quadratic penalty function for the nonlinear model predictive controller is proposed. Obtained field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of real-time energy-efficient velocity regulation and constraints satisfaction.
Permalink :	http://hdl.handle.net/10993/33259
FnR project :	FnR ; FNR7041503 > Seyed Amin Sajadi Alamdari > SEDAS > Stochastic Model Predictive Control For Eco-Driving Assistance Systems In Electric Vehicles > 15/06/2014 > 14/06/2018 > 2013

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