Entropic Model Predictive Optimal Transport over Dynamical Systems

TL;DR

This paper introduces Sinkhorn MPC, a real-time, cost-effective optimal transport control method for dynamical systems that combines entropy regularization with model predictive control, ensuring convergence and stability.

Contribution

It proposes Sinkhorn MPC, integrating the Sinkhorn algorithm with MPC for efficient, real-time optimal transport over dynamical systems, with proven convergence and stability properties.

Findings

Achieves real-time cost-effective transport planning.

Proves global convergence under certain assumptions.

Shows stability for quadratic control costs.

Abstract

We consider the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. This is an optimal transport problem over dynamical systems, which is challenging due to its high computational cost. In this paper, by using entropy regularization, we propose Sinkhorn MPC, which is a dynamical transport algorithm integrating model predictive control (MPC) and the so-called Sinkhorn algorithm. The notable feature of the proposed method is that it achieves cost-effective transport in real time by performing control and transport planning simultaneously, which is illustrated in numerical examples. Moreover, under some assumption on iterations of the Sinkhorn algorithm integrated in MPC, we reveal the global convergence property for Sinkhorn MPC thanks to the entropy regularization. Furthermore, focusing on a quadratic control cost, without the aforementioned assumption we show the ultimate boundedness and the local asymptotic stability for Sinkhorn MPC.