Optimal transport over nonlinear systems via infinitesimal generators on graphs

TL;DR

This paper introduces a graph-based computational framework for optimal transport in nonlinear dynamical systems, enabling provably optimal control laws and insights into complex system behaviors.

Contribution

It develops a convex fluid dynamics formulation on graphs using infinitesimal generators, connecting dynamical systems with optimal transport theory for nonlinear control.

Findings

Optimal control laws for nonlinear systems are derived.

The framework applies to chaotic and non-holonomic systems.

Insights into invariant manifolds and lobe dynamics are provided.

Abstract

We present a set-oriented graph-based computational framework for continuous-time optimal transport over nonlinear dynamical systems. We recover provably optimal control laws for steering a given initial distribution in phase space to a final distribution in prescribed finite time for the case of non-autonomous nonlinear control-affine systems, while minimizing a quadratic control cost. The resulting control law can be used to obtain approximate feedback laws for individual agents in a swarm control application. Using infinitesimal generators, the optimal control problem is reduced to a modified Monge-Kantorovich optimal transport problem, resulting in a convex Benamou-Brenier type fluid dynamics formulation on a graph. The well-posedness of this problem is shown to be a consequence of the graph being strongly-connected, which in turn is shown to result from controllability of the underlying dynamical system. Using our computational framework, we study optimal transport of distributions where the underlying dynamical systems are chaotic, and non-holonomic. The solutions to the optimal transport problem elucidate the role played by invariant manifolds, lobe-dynamics and almost-invariant sets in efficient transport of distributions in finite time. Our work connects set-oriented operator-theoretic methods in dynamical systems with optimal mass transportation theory, and opens up new directions in design of efficient feedback control strategies for nonlinear multi-agent and swarm systems operating in nonlinear ambient flow fields.