Reference : Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critic...
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Security, Reliability and Trust
http://hdl.handle.net/10993/31799
Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critical Systems
English
Sajadi Alamdari, Seyed Amin mailto [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 [Universit ́e de Lorraine, IUT de Longwy, 186 rue de Lorraine, F-54400 Cosnes et Romain, France. > Centre de Recherche en Automatique de Nancy (CRAN) UMR-CNRS 7039]
11-Jul-2017
The 20th World Congress of the International Federation of Automatic Control, IFAC 2017 World Congress, Toulouse, France, 9-14 July 2017
Yes
No
International
Toulouse
France
The 20th World Congress of the International Federation of Automatic Control
09-07-2017 to 14-07-2017
[en] Stochastic Optimisation ; Electric Vehicle ; Predictive Control
[en] Stochastic nonlinear model predictive control has been developed to systematically find an optimal decision with the aim of performance improvement in dynamical systems that involve uncertainties. However, most of the current methods are risk-neutral for safety-critical systems and depend on computationally expensive algorithms. This paper investigates on the risk-averse optimal stochastic nonlinear control subject to real-time safety-critical systems. In order to achieve a computationally tractable design and integrate knowledge about the uncertainties, bounded trajectories generated to quantify the uncertainties. The proposed controller considers these scenarios in a risk-sensitive manner. A certainty equivalent nonlinear model predictive control based on minimum principle is reformulated to optimise nominal cost and expected value of future recourse actions. The capability of proposed method in terms of states regulations, constraints fulfilment, and real-time implementation is demonstrated for a semi-autonomous ecological advanced driver assistance system specified for battery electric vehicles. This system plans for a safe and energy-efficient cruising velocity profile autonomously.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/31799
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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