Optimal control of probability density functions of stochastic processes
Abstract
A Fokker‐Planck framework for the formulation of an optimal control strategy of stochastic processes is presented. Within this strategy, the control objectives are defined based on the probability density functions of the stochastic processes. The optimal control is obtained as the minimizer of the objective under the constraint given by the Fokker‐Planck model. Representative stochastic processes are considered with different control laws and with the purpose of attaining a final target configuration or tracking a desired trajectory. In this latter case, a receding‐horizon algorithm over a sequence of time windows is implemented.
Supported in part by the Austrian Science Fund FWF project F3205‐N18 “Fast Multigrid Methods for Inverse Problems”.
First published online: 10 Feb 2011
Keywords:
probability density function control, Fokker–Planck equation, optimal control theory, receding–horizon, stochastic processHow to Cite
Share
License
Copyright (c) 2010 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2010 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.