Information Space Receding Horizon Control Conference Paper

abstract

• In this paper, we present a receding horizon solution to the problem of optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a Partially Observed Markov Decision Process (POMDP) whose solution is given by an Information Space (I-space) Dynamic Programming (DP) problem. We present a simulation based stochastic optimization technique that, combined with a receding horizon approach, obviates the need to solve the computationally intractable I-space DP problem. The technique is tested on a simple sensor scheduling problem where a sensor has to choose among the measurements of N dynamical systems such that the information regarding the aggregate system is maximized over an infinite horizon. 2011 IEEE.

name of conference

• 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

authors

• Chakravorty, Suman

published proceedings

• 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

author list (cited authors)

• Chakravorty, S., & Erwin, R. S.

citation count

• 18

complete list of authors

• Chakravorty, Suman||Erwin, R Scott

publication date

• January 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

Digital Object Identifier (DOI)

• 10.1109/adprl.2011.5967362

International Standard Book Number (ISBN) 13

• 9781424498888

start page

• 302

end page

• 309

volume

• 1

URL

• http://dx.doi.org/10.1109/adprl.2011.5967362