[4K1-GS-1] Fundamental AI, theory: algorithm

[4K1-GS-1-01] Optimal transport meets MPC

〇Kaito Ito¹, Kenji Kashima¹ (1. Kyoto University)

Keywords:Optimal transport, Dynamical system, Model predictive control

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 a dynamical system, which is challenging due to its high computational cost. In this paper, we propose Sinkhorn MPC, which is a dynamical transport algorithm combining model predictive control 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. In particular, for linear systems, we reveal the fundamental properties of Sinkhorn MPC such as ultimate boundedness and asymptotic stability.