Localized data‐driven consensus control for continuous‐time multi‐agent systems

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-09-09 DOI:10.1002/rnc.7625

Zeze Chang, Zhongkui Li

引用次数: 0

Abstract

This article proposes a localized data‐driven consensus framework for leader‐follower multi‐agent systems with unknown continuous‐time agent dynamics for both noiseless and noisy data scenarios. In this setting, each follower calculates its feedback control gain based on its locally sampled data, including the states, state derivatives, and inputs. We propose novel distributed control protocols that synchronize the distinct dynamic feedback gains and achieve leader‐follower consensus. Design methods are provided for the devised data‐based consensus control algorithms, which rely on low‐dimensional linear matrix inequalities. The validity of the developed algorithms is demonstrated via simulation examples.

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连续时间多代理系统的局部数据驱动共识控制

本文针对具有未知连续时间代理动态的领导者-跟随者多代理系统，提出了一种局部数据驱动的共识框架，适用于无噪声和噪声数据场景。在这种情况下，每个跟随者根据其本地采样数据（包括状态、状态导数和输入）计算其反馈控制增益。我们提出了新颖的分布式控制协议，可同步不同的动态反馈增益，实现领导者与追随者的共识。我们为所设计的基于数据的共识控制算法提供了设计方法，这些算法依赖于低维线性矩阵不等式。通过仿真实例证明了所开发算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.