Distributed Algorithm for the Global Optimal Controller of Nonlinear Multi-Agent Systems

TL;DR

This paper proposes a distributed algorithm to find the global optimal controller for nonlinear multi-agent systems with private state and dynamic information, addressing confidentiality constraints.

Contribution

It introduces a novel distributed approximation method for the Hamilton-Jacobi-Bellman equation suitable for privacy-preserving multi-agent control.

Findings

Numerical simulations confirm the algorithm's effectiveness.

The method achieves global optimal control under privacy constraints.

Abstract

In this paper, we investigate the distributed optimal control problem for a kind of nonlinear multi-agent systems. In particular,both the state and the system dynamic structures of each agent are private and can only be shared among communicating agents.This type of information structure is inevitable in fields such as collaborative control for industrial confidentiality, and renders traditional distributed control methods using all systems' dynamic structures ineffective. The primary contribution is the proposal of a distributed algorithm for the global optimal controller under such practical information structure via distributed approximation of the Hamilton-Jacobi-Bellman equation. Practical numerical simulation demonstrates the effectiveness of the proposed algorithm.