Search

This paper is concerned with the sampled-position states based consensus of networked multi-agent systems with second-order dynamics, where agents are connected through communication channels subject to time-varying communication delays. Note that consensus in such systems cannot be reached if using only the current sampled-position states. This paper investigates the positive effects of time-varying communication delays on the consensus. For two cases, where a directed graph has a fixed or switching topology, several sufficient conditions on consensus are derived by using a discretized Lyapunov-Krasovskii functional method. Based on the obtained conditions, suitable control protocols can be devised through tuning two parameters. Finally, simulation shows that consensus can be achieved using sampled-position states if the time-varying communication delays are within a certain time interval with its lower bound strictly greater than zero. [ABSTRACT FROM AUTHOR]

Abstract Neuronal state estimation of neural networks is a fundamental issue, aiming at estimating neuronal states from contaminated neural measurement outputs. Inspired by extensive applications of neural networks, neuronal state estimation has become a hot research topic in the last decade. This paper presents an overview of neuronal state estimation of neural networks with time-varying delays. First, two kinds of state estimators are analyzed, and their differences are uncovered. Second, recent developments of a Lyapunov–Krasovskii functional method for performance analysis of estimation error systems are surveyed deeply, and some existing methods to design suitable state estimators are summarized with insightful understanding. Third, brief analysis of event-triggered neuronal state estimations of delayed neural networks is made. Finally, some challenging issues that need to be addressed in the near future are listed and briefly discussed. [ABSTRACT FROM AUTHOR]