Information space receding horizon control. Academic Article

abstract

• In this paper, we present a receding horizon solution to the optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a partially observed Markov decision problem 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 sensor scheduling problem, in which a sensor must choose among the measurements of N dynamical systems in a manner that maximizes information regarding the aggregate system over an infinite horizon. While simple, such problems nonetheless lead to very high dimensional DP problems to which the receding horizon approach is well suited.

authors

• Chakravorty, Suman

published proceedings

• IEEE Trans Cybern

author list (cited authors)

• Sunberg, Z., Chakravorty, S., & Scott Erwin, R.

citation count

• 12

complete list of authors

• Sunberg, Z||Chakravorty, S||Scott Erwin, R

publication date

• December 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Cybernetics  Journal

keywords

• Algorithms
• Artificial Intelligence
• Computer Simulation
• Decision Support Techniques
• Feedback
• Markov Chains
• Models, Statistical
• Pattern Recognition, Automated
• Stochastic Processes

PubMed Central ID

• 23757584

Digital Object Identifier (DOI)

• 10.1109/TSMCB.2012.2236313

start page

• 2255

end page

• 2260

volume

• 43

issue

• 6

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ftsmcb.2012.2236313