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dc.contributor.authorPhelps, Chris
dc.contributor.authorRoyset, Johannes O.
dc.contributor.authorGong, Qi
dc.date2016
dc.date.accessioned2016-11-02T01:45:57Z
dc.date.available2016-11-02T01:45:57Z
dc.date.issued2016
dc.identifier.citationSIAM J. CONTROL OPTIM. Vol. 54, No. 1, pp. 1–29en_US
dc.identifier.urihttp://hdl.handle.net/10945/50453
dc.descriptionThe article of record as published may be found at http://dx.doi.org/10.1137/140983161en_US
dc.description.abstractIn this paper, we introduce the uncertain optimal control problem of determining a control that minimizes the expectation of an objective functional for a system with parameter uncertainty in both dynamics and objective. We present a computational framework for the numerical solution of this problem, wherein an independently drawn random sample is taken from the space of uncertain parameters, and the expectation in the objective functional is approximated by a sample average. The result is a sequence of approximating standard optimal control problems that can be solved using existing techniques. To analyze the performance of this computational framework, we develop necessary conditions for both the original and approximate problems and show that the approximation based on sample averages is consistent in the sense of Polak [Optimization: Algorithms and Consistent Approximations, Springer, New York, 1997]. This property guarantees that accumulation points of a sequence of global minimizers (stationary points) of the approximate problem are global minimizers (stationary points) of the original problem. We show that the uncertain optimal control problem can further be approximated in a consistent manner by a sequence of nonlinear programs under mild regularity assumptions. In numerical examples, we demonstrate that the framework enables the solution of optimal search and optimal ensemble control problems.en_US
dc.format.extent29 p.en_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleOptimal Control of Uncertain Systems Using Sample Average Approximationsen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentOperations Research (OR)en_US
dc.subject.authoroptimal controlen_US
dc.subject.authornumerical methodsen_US
dc.subject.authorparameter uncertaintyen_US


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