Dissipativity-Based Distributed Stability Analysis for Networks with Heterogeneous Nonlinear Agents

TL;DR

This paper develops distributed stability analysis algorithms for heterogeneous nonlinear networks using dissipativity theory and ADMM, improving security and computational efficiency, with demonstrated effectiveness on UAV swarms and complex networks.

Contribution

It introduces novel distributed algorithms combining dissipativity theory with ADMM, enabling secure and efficient stability analysis of heterogeneous nonlinear networks.

Findings

Algorithms converge reliably in numerical tests.

Security is enhanced by minimal information sharing.

Applicable to both linearized UAV swarms and complex nonlinear networks.

Abstract

Stabilizing large networks of nonlinear agents is challenging; decomposition and distributed analysis of these networks are crucial for computational tractability and information security. Vidyasagar's Network Dissipativity Theorem enables both properties concurrently in distributed network analysis. This paper explored combining it with the alternating direction methods of multipliers to develop distributed stability analysis for networks of inhomogeneous, nonlinear agents. One algorithm enhances information security by requiring agents to share only a dissipativity characterization, not a dynamical model, for stability analysis. A second algorithm further restricts this information sharing to their clique, thereby enhancing security, and can also reduce the computational burden of stability analysis if the network allows chordal decomposition. The convergence of the proposed algorithms is demonstrated, and criteria are identified for decomposable networks facilitating chordal decomposition. The effectiveness of the proposed methods is demonstrated through numerical examples involving a swarm of linearized unmanned aerial vehicles and networks beyond linear time-invariant agents.