A geometric approach for stability analysis of delay systems: Applications to network dynamics

TL;DR

This paper introduces a geometric method for analyzing the stability of delay systems with complex coefficients, applicable to various networked dynamics, including asymmetric and distributed delays, with broad real-world applications.

Contribution

The paper presents a novel geometric approach to stability analysis of delay systems with complex-valued characteristic equations, extending analysis to asymmetric, distributed, and random networks.

Findings

Effective stability determination for complex-valued TCEs.

Applicable to diverse delay types and network structures.

Demonstrated in control of car-following, mechanical, and neural systems.

Abstract

Investigating the network stability or synchronization dynamics of multi-agent systems with time delays is of significant importance in numerous real-world applications. Such investigations often rely on solving the transcendental characteristic equations (TCEs) obtained from linearization of the considered systems around specific solutions. While stability results based on the TCEs with real-valued coefficients induced by symmetric networks in time-delayed models have been extensively explored in the literature, there remains a notable gap in stability analysis for the TCEs with complexvalued coefficients arising from asymmetric networked dynamics with time delays. To address this challenge comprehensively, we propose a rigorously geometric approach. By identifying and studying the stability crossing curves in the complex plane, we are able to determine the stability region of these systems. This approach is not only suitable for analyzing the stability of models with discrete time delays but also for models with various types of delays, including distributed time delays. Additionally, it can also handle random networks. We demonstrate the efficacy of this approach in designing delayed control strategies for car-following systems, mechanical systems, and deep brain stimulation modeling, where involved are complex-valued TCEs or/and different types of delays. All these therefore highlight the broad applicability of our approach across diverse domains.