Imperial College London
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DrGiordanoScarciotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6268g.scarciotti Website

 
 
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Location

 

1118Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Scarciotti:2019:10.1109/CDC.2018.8619055,
author = {Scarciotti, G and Mylvaganam, T},
doi = {10.1109/CDC.2018.8619055},
publisher = {IEEE},
title = {Approximate infinite-horizon optimal control for stochastic systems},
url = {http://dx.doi.org/10.1109/CDC.2018.8619055},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The  policy  of  an  optimal  control  problem  fornonlinear stochastic systems can be characterized by a second-order  partial  differential  equation  for  which  solutions  are  notreadily available. In this paper we provide a systematic methodfor  obtaining  approximate  solutions  for  the  infinite-horizonoptimal   control   problem   in   the   stochastic   framework.   Themethod  is  demonstrated  on  an  illustrative  numerical  examplein  which  the  control  effort  is  not  weighted,  showing  that  thetechnique is able to deal with one of the most striking featuresof  stochastic  optimal  control.
AU  - Scarciotti,G
AU  - Mylvaganam,T
DO  - 10.1109/CDC.2018.8619055
PB  - IEEE
PY  - 2019///
TI  - Approximate infinite-horizon optimal control for stochastic systems
UR  - http://dx.doi.org/10.1109/CDC.2018.8619055
UR  - https://ieeexplore.ieee.org/document/8619055
UR  - http://hdl.handle.net/10044/1/62583
ER  -
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