TY - GEN
T1 - Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements
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
Y1 - 2013/6
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 -