Extension technology and extrema selections in a stochastic multistart algorithm for optimal control problems

The paper proposes a method for finding the minimum value of a functional in nonlinear nonconvex optimal control problems. The method takes advantage of the hidden convexity property of the controlled differential equations systems. Application of the multistart idea with extrema selection procedure...

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Published in:Journal of global optimization 2020-03, Vol.76 (3), p.533-543
Main Authors: Gornov, Alexander Yu, Zarodnyuk, Tatiana S., Anikin, Anton S., Finkelstein, Evgeniya A.
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Convexity
Differential equations
Mathematics
Mathematics and Statistics
Nonlinear control
Operations Research/Decision Theory
Optimal control
Optimization
Real Functions
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cites cdi_FETCH-LOGICAL-c358t-8ead14520128390388db65cbae9c469b05168dc8c4f05d23eb1d1656694011973
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container_title Journal of global optimization
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creator Gornov, Alexander Yu
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description The paper proposes a method for finding the minimum value of a functional in nonlinear nonconvex optimal control problems. The method takes advantage of the hidden convexity property of the controlled differential equations systems. Application of the multistart idea with extrema selection procedures makes it possible to create software that does not strongly depend on the problem size and supplies additional information about the object under investigation. Three test problems are considered to show specific properties of using the stochastic multistart algorithm and extension numerical technology.
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subjects Algorithms
Computer Science
Convexity
Differential equations
Mathematics
Mathematics and Statistics
Nonlinear control
Operations Research/Decision Theory
Optimal control
Optimization
Real Functions
title Extension technology and extrema selections in a stochastic multistart algorithm for optimal control problems
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