Fuzzy adaptive control for consensus tracking in multiagent systems with incommensurate fractional-order dynamics: Application to power systems

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Amin Sharafian , Ahmad Ali , Inam Ullah , Tarek R. Khalifa , Xiaoshan Bai , Li Qiu
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引用次数: 0

Abstract

This paper presents a novel approach to address the consensus tracking problem within a class of incommensurate fractional-order nonlinear non-affine systems. Our method employs an adaptive fuzzy technique that integrates newly developed fractional adaptive algorithms based on the Lyapunov method into the controller's design process. This method develops stability based on the global representation of the follower and leader systems, reducing assumptions on the system dynamics to address non-affinity. Additionally, it introduces a simplified approach to designing controllers for incommensurate fractional-order multiagent systems. The proposed controller effectively discerns uncertainties and external disturbances, compelling follower agents to seamlessly follow the desired trajectories set by the leader. Notably, compared to the existing literature, our method exhibits key advantages, including reduced assumptions regarding the system's non-affinity and a simpler design for controlling incommensurate systems. We demonstrate the efficacy of the proposed incommensurate fractional controller through simulations conducted using MATLAB on a fractional-order multiagent power system.

在具有不相称分数阶动态的多代理系统中进行共识跟踪的模糊自适应控制:在电力系统中的应用
本文提出了一种新方法来解决一类不相容分数阶非线性非仿真系统中的共识跟踪问题。我们的方法采用自适应模糊技术,将基于 Lyapunov 方法新开发的分数自适应算法集成到控制器的设计过程中。该方法基于跟随者和领导者系统的全局表示来开发稳定性,减少了对系统动力学的假设,以解决非亲和性问题。此外,它还引入了一种简化方法,用于设计不相容分数阶多代理系统的控制器。所提出的控制器能有效识别不确定性和外部干扰,迫使跟随者无缝跟随领导者设定的理想轨迹。值得注意的是,与现有文献相比,我们的方法具有一些关键优势,包括减少了对系统非亲和性的假设,以及控制不相容系统的设计更简单。我们通过使用 MATLAB 对分数阶多代理电力系统进行仿真,证明了所提出的不相称分数控制器的功效。
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
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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