Dynamic Event-Triggered Consensus for Switched Nonlinear Systems in Intelligent Manufacturing

IF 2.5 Q2 ENGINEERING, INDUSTRIAL
Shanyan Hu, Mengling Wang, Yixiong Feng, Yan Jiang, Lie Chen
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引用次数: 0

Abstract

Multiagent cooperative control enhances system efficiency through the facilitation of distributed collaboration, demonstrating significant applications in intelligent manufacturing. As a fundamental issue of cooperative control, multiagent consensus has been implemented extensively in numerous domains. Therefore, this paper studies the asymptotic consensus issue of a nonlinear system under switching topologies. The changeable topological structures hinder the system's ability to stabilise or require a substantial amount of time for stabilisation. To address this issue, we have incorporated topological information into the traditional Riccati equation. Subsequently, a topology-based dynamic event-triggered mechanism is presented by introducing an internal dynamic variable based on the solution of the Riccati equation. Furthermore, this research proposes a novel control protocol that utilises the full information of the switching topologies. This protocol contains a changeable control gain, which allows for the adjustment of the control law in response to the communication topology. Then, the Lyapunov stability theory guarantees that the nonlinear system reaches an asymptotic consensus under the proposed control law. This study also proves that the system does not exhibit Zeno behaviour. Ultimately, the simulation results confirm the viability of the control protocol.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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