Feedforward Density-Driven Optimal Control for Tracking Time-Varying Distributions with Guaranteed Stability

TL;DR

This paper introduces a feedforward-augmented Density-Driven Optimal Control framework that explicitly incorporates reference velocity fields to improve tracking of dynamic distributions in multi-agent systems, with proven stability and reduced lag.

Contribution

It extends existing D$^2$OC methods by integrating a predictive control mechanism that accounts for reference flow dynamics, enhancing tracking accuracy in changing environments.

Findings

Reduces tracking lag in dynamic scenarios

Provides analytical bounds on tracking error

Demonstrates improved performance in numerical simulations

Abstract

This paper addresses the spatiotemporal mismatch in multi-agent distribution tracking within time-varying environments. While recent advancements in Density-Driven Optimal Control (D$^2$OC) have enabled finite-time distribution matching using Optimal Transport theory, existing formulations primarily assume a stationary reference density. In dynamic scenarios, such as tracking evolving wildfires or moving plumes, this assumption leads to a structural tracking lag where the agent configuration inevitably falls behind the shifting reference flow. To resolve this, we propose a feedforward-augmented D$^2$OC framework that explicitly incorporates the reference velocity field, modeled via the continuity equation, into the control law. We provide a formal mathematical quantification of the induced tracking lag and analytically prove that the proposed predictive mechanism effectively reduces the cumulative tracking error. Furthermore, an analytical ultimate bound for the local Wasserstein distance is established under discretization errors and transport jitter. Theoretical analysis and numerical results demonstrate that our approach significantly mitigates tracking latency, ensuring robust and high-fidelity tracking performance in rapidly changing environments.