Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation

Motivated by the successful implementation of Pseudo-spectral (PS) methods in optimal control problems (OCP), a new technique is introduced to control the probability density function (PDF) of the state of the 1-D system described by a stochastic differential equation (SDE). In this paper, the Fokke...

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Main Authors: Ali Namadchian, Mehdi Ramezani
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Cogent Engineering
Subjects:
Online Access:http://dx.doi.org/10.1080/23311916.2019.1691804
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spelling doaj-9b26829ace5547098c1cf72767facfcf2021-03-02T14:46:51ZengTaylor & Francis GroupCogent Engineering2331-19162019-01-016110.1080/23311916.2019.16918041691804Pseudo-spectral optimal control of stochastic processes using Fokker Planck equationAli Namadchian0Mehdi Ramezani1Tafresh UniversityTafresh UniversityMotivated by the successful implementation of Pseudo-spectral (PS) methods in optimal control problems (OCP), a new technique is introduced to control the probability density function (PDF) of the state of the 1-D system described by a stochastic differential equation (SDE). In this paper, the Fokker Planck equation (FPE) is used to model the time evolution of the PDF of the stochastic process. Using FPE instead of SDE, changes the problem of stochastic optimal control to a deterministic one. FPE is a parabolic PDE. Solving an OCP with PDE constraint is computationally a difficult task. We use two strategies to efficiently solve this OCP problem: firstly, we use PS methods in order to transform the OCP to a non-linear programming (NLP) with fewer discretization points but higher order of accuracy, and secondly, we utilize Genetic algorithm (GA) to solve this large-scale NLP in a more efficient approach than gradient-based optimization methods. The simulation results based on Monte-Carlo simulations prove the performance of the proposed method.http://dx.doi.org/10.1080/23311916.2019.1691804pseudo-spectral optimal controlfokker planck equationstochastic processgenetic algorithmlegendre pseudo-spectral method
collection DOAJ
language English
format Article
sources DOAJ
author Ali Namadchian
Mehdi Ramezani
spellingShingle Ali Namadchian
Mehdi Ramezani
Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
Cogent Engineering
pseudo-spectral optimal control
fokker planck equation
stochastic process
genetic algorithm
legendre pseudo-spectral method
author_facet Ali Namadchian
Mehdi Ramezani
author_sort Ali Namadchian
title Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
title_short Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
title_full Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
title_fullStr Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
title_full_unstemmed Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation
title_sort pseudo-spectral optimal control of stochastic processes using fokker planck equation
publisher Taylor & Francis Group
series Cogent Engineering
issn 2331-1916
publishDate 2019-01-01
description Motivated by the successful implementation of Pseudo-spectral (PS) methods in optimal control problems (OCP), a new technique is introduced to control the probability density function (PDF) of the state of the 1-D system described by a stochastic differential equation (SDE). In this paper, the Fokker Planck equation (FPE) is used to model the time evolution of the PDF of the stochastic process. Using FPE instead of SDE, changes the problem of stochastic optimal control to a deterministic one. FPE is a parabolic PDE. Solving an OCP with PDE constraint is computationally a difficult task. We use two strategies to efficiently solve this OCP problem: firstly, we use PS methods in order to transform the OCP to a non-linear programming (NLP) with fewer discretization points but higher order of accuracy, and secondly, we utilize Genetic algorithm (GA) to solve this large-scale NLP in a more efficient approach than gradient-based optimization methods. The simulation results based on Monte-Carlo simulations prove the performance of the proposed method.
topic pseudo-spectral optimal control
fokker planck equation
stochastic process
genetic algorithm
legendre pseudo-spectral method
url http://dx.doi.org/10.1080/23311916.2019.1691804
work_keys_str_mv AT alinamadchian pseudospectraloptimalcontrolofstochasticprocessesusingfokkerplanckequation
AT mehdiramezani pseudospectraloptimalcontrolofstochasticprocessesusingfokkerplanckequation
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