Numerical optimization software for solving stochastic optimal control

Alridha, Ahmed Hasan; Salman, Abbas Musleh; Mousa, Ekhlas Annon

doi:10.47974/JIM-1525

Open Access Research Article

Numerical optimization software for solving stochastic optimal control

^*Ahmed Hasan AlridhaCorresponding authoramqa92@yahoo.comGeneral Directorate of Education of Babylon Ministry of EducationIraqView full profile → , Abbas Musleh Salmanabbas.mmm2019@gmail.comGeneral Directorate of Education of Babylon Ministry of EducationIraqView full profile → , Ekhlas Annon Mousaekhlasanoon@yahoo.comGeneral Directorate of Education of Babylon Ministry of EducationIraqView full profile →

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pp. 889–895Vol. 26Issue 5August 2023DOI: 10.47974/JIM-1525XML

Received:: 06 May 2022
Accepted:: 08 Jun 2022
Published Online:: 08 Sep 2023
Article type:: Research Article
Language:: EN
Article no.:: JIM-1525
Pages:: 889–895

Abstract

Stochastic optimal control is a branch of control theory that deals with uncertain system parameters. One of the requirements for an accurate description of systems is a complete knowledge of the structure or values of the basic parameters of the system, which if achieved, the optimization process will also have accurate results. In this paper, we highlight a method for optimizing control under the control of a stochastic uncertainty approach that involves the use and dependence on uncertain parameters for systems whether they are distributed by a distribution function or within other sets of potential values by using the python language. Finally, the numerical results were implemented in python using the GEKKO package.

Keywords

Stochastic optimal control Optimization approach Python language

Subject Classifications

16Y60

Acknowledgements

Obaid (53_icmas_h19)

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