Proceedings ArticleDOI
Sample average approximations in optimal control of uncertain systems
Chris Phelps,Johannes O. Royset,Qi Gong +2 more
- pp 1958-1965
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TLDR
An optimality function is provided for both the uncertain optimal control problem and its approximation, and it is shown that the approximation based on the sample average approach is consistent in the sense of Polak.Abstract:
This paper focuses on an optimal control problem in which the objective is to minimize the expectation of a cost functional with stochastic parameters. The inclusion of the stochastic parameters in the objective raises new theoretical and computational challenges not present in a standard nonlinear optimal control problem. In this paper, we provide a numerical framework for the solution of this uncertain optimal control problem by taking a sample average approximation approach. An independent random sample is taken from the parameter space, and the expectation is approximated by the sample average. The result is a family of standard nonlinear optimal control problems which can be solved using existing techniques. We provide an optimality function for both the uncertain optimal control problem and its approximation, and show that the approximation based on the sample average approach is consistent in the sense of Polak. We illustrate the approach with a numerical example arising in optimal search for a moving target.read more
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Optimal Control of Uncertain Systems Using Sample Average Approximations
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Consistent approximation of a nonlinear optimal control problem with uncertain parameters
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References
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I and i
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
TL;DR: An SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems is discussed.
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Lectures on Stochastic Programming: Modeling and Theory
TL;DR: The authors dedicate this book to Julia, Benjamin, Daniel, Natan and Yael; to Tsonka, Konstatin and Marek; and to the Memory of Feliks, Maria, and Dentcho.
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Infinite Dimensional Analysis: A Hitchhiker's Guide
TL;DR: In this paper, Riesz spaces are used to represent the topology of the space of sequences of sequences and correspondences of correspondences in Markov transitions, where the correspondences correspond to Markov transition.
Journal ArticleDOI
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
TL;DR: An SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems is discussed and a reduced-Hessian semidefinite QP solver (SQOPT) is discussed.