Monte Carlo Methods for Controller Approximation and Stabilization in Nonlinear Stochastic Optimal Control

Bertoli, F; Bishop, AN

Monte Carlo Methods for Controller Approximation and Stabilization in Nonlinear Stochastic Optimal Control

Bertoli, F Bishop, AN

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Publication Type:: Journal Article
Citation:: IFAC-PapersOnLine, 2015, 48 (28), pp. 811 - 816
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Bertoli, F	en_US
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	IFAC-PapersOnLine, 2015, 48 (28), pp. 811 - 816	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117013
dc.description.abstract	© 2015 An approximation method for receding horizon optimal control, in nonlinear stochastic systems, is considered in this work. This approximation method is based on Monte Carlo simulation and derived via the Feynman-Kac formula, which gives a stochastic interpretation for the solution of a Hamilton-Jacobi-Bellman equation associated with the true optimal controller. It is shown that this controller approximation method practically stabilises the system over an infinite horizon and thus the controller approximation errors do not accumulate or lead to instability over time.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120102873
dc.relation.ispartof	IFAC-PapersOnLine	en_US
dc.relation.isbasedon	10.1016/j.ifacol.2015.12.229	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Monte Carlo Methods for Controller Approximation and Stabilization in Nonlinear Stochastic Optimal Control	en_US
dc.type	Journal Article
utslib.citation.volume	28	en_US
utslib.citation.volume	48	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.issue	28	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2015 An approximation method for receding horizon optimal control, in nonlinear stochastic systems, is considered in this work. This approximation method is based on Monte Carlo simulation and derived via the Feynman-Kac formula, which gives a stochastic interpretation for the solution of a Hamilton-Jacobi-Bellman equation associated with the true optimal controller. It is shown that this controller approximation method practically stabilises the system over an infinite horizon and thus the controller approximation errors do not accumulate or lead to instability over time.

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http://hdl.handle.net/10453/117013