Distributed MPC For Coordinated Path-Following
Lusine Poghosyan, Anna Manucharyan, Mikayel Aramyan, Naira Hovakimyan, Tigran Bakaryan

TL;DR
This paper introduces a distributed MPC algorithm for coordinated path-following that ensures exponential stability and scalability, enabling real-time implementation with low communication overhead and adaptability to complex scenarios.
Contribution
It presents a novel distributed MPC scheme that decouples dynamics using normalized Laplacian properties, providing convergence guarantees and flexibility over traditional preplanning methods.
Findings
Proves exponential stability for prediction horizon K=1
Demonstrates scalability and low communication overhead
Shows robustness to communication failures in simulations
Abstract
In this paper, we consider a distributed model predictive control (MPC) algorithm for coordinated path-following. Relying on the time-critical cooperative path-following framework, which decouples space and time and reduces the coordination problem to a one-dimensional setting, we formulate a distributed MPC scheme for time coordination. Leveraging properties of the normalized Laplacian, we decouple the MPC dynamics into independent modes and derive a recursive relation linking current and predicted states. Using this structure, we prove that, for prediction horizon and a fixed connected communication network, the system is exponentially stable even in the presence of path-following errors. This provides a first result on the convergence analysis of discrete-time distributed MPC schemes within this framework. The proposed approach enables scalable and efficient real-time…
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Taxonomy
TopicsTraffic control and management · Advanced Control Systems Optimization · Distributed Control Multi-Agent Systems
