Nonlinear Consensus Strategies for Multi-Agent Networks in Presence of Communication Delays and Switching Topologies: Real-Time Receding Horizon Approach

TL;DR

This paper introduces a real-time, non-iterative nonlinear receding horizon control framework for multi-agent networks, enabling consensus despite communication delays and switching topologies, with proven stability and convergence.

Contribution

It develops a novel real-time nonlinear receding horizon control approach that directly solves distributed optimization for multi-agent consensus in complex, delayed, and switching networks.

Findings

Validated on nonlinear chaotic systems

Achieved stable consensus under delays and topology switches

Demonstrated real-time, non-iterative solution effectiveness

Abstract

This paper presents a novel framework which combines a non-iterative solution of Real-Time Nonlinear Receding Horizon Control (NRHC) methodology to achieve consensus within complex network topologies with existing time-delays and in presence of switching topologies. In this formulation, we solve the distributed nonlinear optimization problem for multi-agent network systems directly, \emph{in real-time}, without any dependency on iterative processes, where the stability and convergence guarantees are provided for the solution. Three benchmark examples on non-linear chaotic systems provide validated results which demonstrate the significant outcomes of such methodology.