Complex Laplacian based Distributed Control for Multi-Agent Network

TL;DR

This paper introduces a complex Laplacian-based distributed control method for multi-agent networks, enabling hierarchical formation, stability, and robustness against uncertainties, demonstrated through vehicle network simulations.

Contribution

It proposes a novel cascade formulation using complex Laplacian to organize large networks into clusters with controllable convergence and stability properties.

Findings

Hierarchical control structure with meta-cluster formation

Ensures globally stable formations under certain conditions

Robust to communication failures and actuator issues

Abstract

The work done in this paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigen spectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure has also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of thirty vehicles.