Graph-based Stochastic Control with Constraints: A Unified Approach with Perfect and Imperfect Measurements
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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.) © 2013 AACC American Automatic Control Council.
author list (cited authors)
Agha-mohammadi, A., Chakravorty, S., Amato, N. M., & IEEE, ..