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Sampled-data exponential consensus of multi-agent systems with Lipschitz nonlinearities.
Zhao, Wenqing; Chen, Guoliang; Xie, Xiangpeng; Xia, Jianwei; Park, Ju H.
Afiliación
  • Zhao W; School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, PR China. Electronic address: zhaowq10789@163.com.
  • Chen G; School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, PR China. Electronic address: Chenguoliang3936@126.com.
  • Xie X; Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, PR China. Electronic address: xiexiangpeng1953@163.com.
  • Xia J; School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, PR China. Electronic address: njustxjw@126.com.
  • Park JH; Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea. Electronic address: jessie@ynu.ac.kr.
Neural Netw ; 167: 763-774, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37729790
In this paper, the exponential consensus of leaderless and leader-following multi-agent systems with Lipschitz nonlinear dynamics is illustrated with aperiodic sampled-data control using a two-sided loop-based Lyapunov functional (LBLF). Firstly, applying input delay approach to reformulate the resulting sampled-data system as a continuous system with time-varying delay in the control input. A two-sided LBLF which captures the information on sampled-data pattern is constructed and the symmetry of the Laplacian matrix together with Newton-Leibniz formula have been employed to obtain reduced number of decision variables and decreased LMI dimensions for the exponential sampled-data consensus problem. Subsequently, an aperiodic sampled-data controller was designed to simplify and enhance stability conditions for computation and optimization purposes in the proposed approach. Finally, based on the controller design, simulation examples including the power system are proposed to illustrate the theoretical analysis, moreover, a larger sampled-data interval can be acquired by this method than other literature, thereby conserving bandwidth and reducing communication resources.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Dinámicas no Lineales Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Dinámicas no Lineales Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos