Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10399236 | Automatica | 2005 | 12 Pages |
Abstract
Practical exploitation of optimal dual control (ODC) theory continues to be hindered by the difficulties involved in numerically solving the associated stochastic dynamic programming (SDPs) problems. In particular, high-dimensional hyper-states coupled with the nesting of optimizations and integrations within these SDP problems render their exact numerical solution computationally prohibitive. This paper presents a new stochastic dynamic programming algorithm that uses a Monte Carlo approach to circumvent the need for numerical integration, thereby dramatically reducing computational requirements. Also, being a generalization of iterative dynamic programming (IDP) to the stochastic domain, the new algorithm exhibits reduced sensitivity to the hyper-state dimension and, consequently, is particularly well suited to solution of ODC problems. A convergence analysis of the new algorithm is provided, and its benefits are illustrated on the problem of ODC of an integrator with unknown gain, originally presented by Ã
ström and Helmersson (Computers and Mathematics with Applications 12A (1986) 653-662).
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Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Adrian M. Thompson, William R. Cluett,