Entropic model predictive optimal transport over dynamical systems?

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 socalled 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. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper addresses the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. To tackle the high computational cost, the authors propose Sinkhorn MPC, an algorithm that integrates model predictive control (MPC) with the Sinkhorn algorithm using entropy regularization. The method achieves cost-effective transport in real time by simultaneously performing control and transport planning, as demonstrated in numerical examples. Furthermore, the authors reveal the global convergence property and boundedness of Sinkhorn MPC under certain assumptions on the iterations of the Sinkhorn algorithm integrated in MPC.