Distributed Nonlinear MPC of Multi-Agent Systems with Data Compression and Random Delays - Extended Version

TL;DR

This paper introduces a distributed nonlinear model predictive control framework for multi-agent systems that effectively manages communication delays and bandwidth constraints through neural network data compression, ensuring formation stability and collision avoidance.

Contribution

It presents a novel ISpS formulation for distributed NMPC that incorporates neural network-based data compression and robust delay handling, with analytical stability proofs for various network connectivities.

Findings

Neural network compression significantly reduces data transmission size.

The proposed control scheme maintains stability despite delays and bandwidth limitations.

Simulations demonstrate effective formation control and collision avoidance.

Abstract

This is an extended version of a technical note accepted for publication in IEEE Transactions on Automatic Control. The note proposes an Input to State practically Stable (ISpS) formulation of distributed nonlinear model predictive controller (NMPC) for formation control of constrained autonomous vehicles in presence of communication bandwidth limitation and transmission delays. Planned trajectories are compressed using neural networks resulting in considerable reduction of data packet size, while being robust to propagation delays and uncertainty in neighbors' trajectories. Collision avoidance is achieved by means of spatially filtered potential field. Analytical results proving ISpS and generalized small gain conditions are presented for both strongly- and weakly-connected networks, and illustrated by simulations.