Optimal control of probability density functions of stochastic processes*

Supported in part by the Austrian Science Fund FWF project F3205‐N18 “Fast Multigrid Methods for Inverse Problems”.

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.

Keywords:

• probability density function control
• Fokker–Planck equation
• optimal control theory
• receding–horizon
• stochastic process

Notes

Supported in part by the Austrian Science Fund FWF project F3205‐N18 “Fast Multigrid Methods for Inverse Problems”.