The paper discusses various approaches to solving nonlinear optimal control problems. Of all such approaches, we chose the two most characteristic. The first one uses sufficient conditions of optimality in the form of Hamilton–Jacobi–Bellman equations and the corresponding numerical method. The second is based on the reduction of optimal control problem to interval linear programming problem and finding a solution using the Gabasov’s adaptive method. The main goal is to compare the capabilities of these methods within a specific problem of optimal control. As an application, we consider the problem of constructing optimal control in a nonlinear model of macroeconomic growth with nonlinear dynamical constraints. Comparative analysis of these two approaches and corresponding numerical simulation are presented.

Original languageEnglish
Title of host publicationIntelligent Distributed Computing XIII, IDC 2019
EditorsIgor Kotenko, Vasily Desnitsky, Costin Badica, Didier El Baz, Mirjana Ivanovic
PublisherSpringer Nature
Pages183-188
Number of pages6
ISBN (Print)9783030322571
DOIs
StatePublished - 2020
Event13th International Symposium on Intelligent Distributed Computing, IDC 2019 - St. Petersburg, Russian Federation
Duration: 7 Oct 20199 Oct 2019

Publication series

NameStudies in Computational Intelligence
Volume868
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference13th International Symposium on Intelligent Distributed Computing, IDC 2019
Country/TerritoryRussian Federation
CitySt. Petersburg
Period7/10/199/10/19

    Research areas

  • Dynamic programming method, Gabasov’s adaptive method, Optimal control, Gabasov's adaptive method

    Scopus subject areas

  • Artificial Intelligence

ID: 49436775