Distributed Optimal Consensus of Nonlinear Multi-Agent Systems

TL;DR

This paper introduces a unified framework for optimal consensus in nonlinear multi-agent systems, proposing new algorithms based on optimal control and MPC with proven convergence.

Contribution

It develops a distributed optimal consensus algorithm for nonlinear multi-agent systems using OCP and MPC, with rigorous convergence analysis and broader applicability.

Findings

Algorithms demonstrate superlinear convergence rate.

Numerical simulations confirm effectiveness.

Applicable to both leaderless and leader-follower systems.

Abstract

In this paper, the optimal consensus problem for general nonlinear multi-agent systems is studied, where both leaderless and leader-follower cases are considered in a unified framework. The key idea is to convert consensus problems into optimal control problems where the objective of each agent with nonlinear dynamics is to design the control input minimizing the global consensus cost function. Compared with the existing distributed consensus control for nonlinear multi-agent systems, we propose a distributed optimal consensus algorithm based on the optimal control principle (OCP) method, and two enhanced algorithms are developed under the model predictive control (MPC) framework,these two algorithms demonstrate broader applicability when handling general nonlinear multi-agent systems. Moreover, the convergence and superlinear convergence rate of the proposed algorithms are rigorously analyzed. Numerical simulations demonstrate the effectiveness of the proposed algorithms.