论文标题
Sinkhorn MPC：模型预测性最佳运输在动态系统上
Sinkhorn MPC: Model predictive optimal transport over dynamical systems
论文作者
论文摘要
我们认为将代理商人群转向无限视野的所需分布的最佳控制问题。这是动态系统上的最佳运输问题，由于其高计算成本，这具有挑战性。在本文中，我们提出了Sinkhorn MPC，这是一种结合模型预测控制和所谓的sndhorn算法的动力传输算法。该方法的显着特征是，它通过同时执行控制和运输计划实时实现具有成本效益的运输。特别是，对于具有能源成本的线性系统，我们揭示了sindhorn MPC的基本特性，例如最终界限和渐近稳定性。
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 with an energy cost, we reveal the fundamental properties of Sinkhorn MPC such as ultimate boundedness and asymptotic stability.
