Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements

Ali-akbar Agha-mohammadi; Suman Chakravorty; Nancy M. Amato

doi:10.1109/ACC.2013.6580545

Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements

Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).

Original language	English (US)
Title of host publication	2013 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	9781479901784
DOIs	https://doi.org/10.1109/ACC.2013.6580545
State	Published - Jun 2013
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: The work of Agha-mohammadi and Chakravorty is supported in part by NSFaward RI-1217991 and AFOSR Grant FA9550-08-1-0038 and the work of Aghamohammadiand Amato is supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, byNSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, byDOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, byTHECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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title = "Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements",

abstract = "This paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).",

author = "Ali-akbar Agha-mohammadi and Suman Chakravorty and Amato, {Nancy M.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: The work of Agha-mohammadi and Chakravorty is supported in part by NSFaward RI-1217991 and AFOSR Grant FA9550-08-1-0038 and the work of Aghamohammadiand Amato is supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, byNSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, byDOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, byTHECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST). This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2013",

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doi = "10.1109/ACC.2013.6580545",

language = "English (US)",

isbn = "9781479901784",

booktitle = "2013 American Control Conference",

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AU - Agha-mohammadi, Ali-akbar

AU - Chakravorty, Suman

AU - Amato, Nancy M.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: The work of Agha-mohammadi and Chakravorty is supported in part by NSFaward RI-1217991 and AFOSR Grant FA9550-08-1-0038 and the work of Aghamohammadiand Amato is supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, byNSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, byDOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, byTHECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST). This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2013/6

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N2 - This paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).

AB - This paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).

UR - http://hdl.handle.net/10754/598428

UR - http://ieeexplore.ieee.org/document/6580545/

U2 - 10.1109/ACC.2013.6580545

DO - 10.1109/ACC.2013.6580545

M3 - Conference contribution

SN - 9781479901784

BT - 2013 American Control Conference

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements

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