Practical consensus of T-S fuzzy positive multi-agent systems using linear programming

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Junfeng Zhang , Chongxiang Yu , Baochen Zhang , Weidong Zhang
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引用次数: 0

Abstract

This paper presents the practical consensus of T-S fuzzy positive multi-agent systems with interval type-1 and type-2 fuzzy sets using observer-based control protocols. A fuzzy positive observer is first constructed for the systems. An observer-based fuzzy control protocol is designed, where an additional constant term is introduced. Some linear programming conditions are established to achieve the positivity of the original system and its observer. Then, the practical consensus of the original system is transformed into the stability of a dynamic system, where a set of new variables is defined by combining the constant term and the states of the agents. In the first step, the positivity of the new variables is guaranteed. In the second step, the stability of the dynamic system consisting new variables is addressed by using a fuzzy copositive Lyapunov function. The gain matrices of observer and control protocols are formulated based on a matrix decomposition approach. All positive and consensus conditions are described via linear programming. Finally, two examples are provided to verify the validity of the obtained results.
利用线性规划实现 T-S 模糊正多代理系统的实用共识
本文介绍了利用基于观测器的控制协议对具有区间类型-1 和类型-2 模糊集的 T-S 模糊正多代理系统达成共识的实用方法。首先为系统构建了一个模糊正向观测器。设计了一个基于观测器的模糊控制协议,其中引入了一个附加常数项。建立了一些线性编程条件,以实现原始系统及其观测器的正向性。然后,将原始系统的实际共识转化为动态系统的稳定性,通过结合常数项和代理的状态来定义一组新变量。第一步,保证新变量的正向性。第二步,利用模糊共正李亚普诺夫函数解决由新变量组成的动态系统的稳定性问题。观测器和控制协议的增益矩阵是基于矩阵分解方法制定的。所有正条件和共识条件都通过线性规划来描述。最后,提供了两个例子来验证所获结果的有效性。
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
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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