General Discrete-Time Fokker-Planck Control by Power Moments

TL;DR

This paper introduces a novel approach for controlling discrete-time linear systems by manipulating their probability distributions through power moments, avoiding Gaussian assumptions and employing convex optimization for distribution control.

Contribution

It proposes a moment-based representation and a new feedback control method combining feedback and Markovian kernels for non-Gaussian distribution control.

Findings

The method effectively controls non-Gaussian distributions.

Convex optimization ensures unique solutions for transition kernels.

Simulation validates the approach's effectiveness.

Abstract

In this paper, we address the so-called general Fokker-Planck control problem for discrete-time first-order linear systems. Unlike conventional treatments, we don't assume the distributions of the system states to be Gaussian. Instead, we only assume the existence and finiteness of the first several order power moments of the distributions. It is proved in the literature that there doesn't exist a solution, which has a form of conventional feedback control, to this problem. We propose a moment representation of the system to turn the original problem into a finite-dimensional one. Then a novel feedback control term, which is a mixture of a feedback term and a Markovian transition kernel term is proposed to serve as the control input of the moment system. The states of the moment system are obtained by maximizing the smoothness of the state transition. The power moments of the transition kernels are obtained by a convex optimization problem, of which the solution is proved to exist and be unique. Then they are mapped back to the probability distributions. The control inputs to the original system are then obtained by sampling from the realized distributions. Simulation results are provided to validate our algorithm in treating the general discrete-time Fokker-Planck control problem.