71,673 research outputs found
Self-triggered Consensus of Multi-agent Systems with Quantized Relative State Measurements
This paper addresses the consensus problem of first-order continuous-time
multi-agent systems over undirected graphs. Each agent samples relative state
measurements in a self-triggered fashion and transmits the sum of the
measurements to its neighbors. Moreover, we use finite-level dynamic quantizers
and apply the zooming-in technique. The proposed joint design method for
quantization and self-triggered sampling achieves asymptotic consensus, and
inter-event times are strictly positive. Sampling times are determined
explicitly with iterative procedures including the computation of the Lambert
-function. A simulation example is provided to illustrate the effectiveness
of the proposed method.Comment: 29 pages, 3 figures. To appear in IET Control Theory & Application
Self-triggered Consensus Control of Multi-agent Systems from Data
This paper considers self-triggered consensus control of unknown linear
multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used
in MASs, thanks to their advantages in avoiding continuous monitoring and
saving computing and communication resources. However, existing results require
the knowledge of system matrices, which are difficult to obtain in real-world
settings. To address this challenge, we present a data-driven approach to
designing STMs for unknown MASs building upon the model-based solutions. Our
approach leverages a system lifting method, which allows us to derive a
data-driven representation for the MAS. Subsequently, a data-driven
self-triggered consensus control (STC) scheme is designed, which combines a
data-driven STM with a state feedback control law. We establish a data-based
stability criterion for asymptotic consensus of the closed-loop MAS in terms of
linear matrix inequalities, whose solution provides a matrix for the STM as
well as a stabilizing controller gain. In the presence of external
disturbances, a model-based STC scheme is put forth for
-consensus of MASs, serving as a baseline for the
data-driven STC. Numerical tests are conducted to validate the correctness of
the data- and model-based STC approaches. Our data-driven approach demonstrates
a superior trade-off between control performance and communication efficiency
from finite, noisy data relative to the system identification-based one
Event-triggered Consensus for Multi-agent Systems with Asymmetric and Reducible Topologies
This paper studies the consensus problem of multi-agent systems with
asymmetric and reducible topologies. Centralized event-triggered rules are
provided so as to reduce the frequency of system's updating. The diffusion
coupling feedbacks of each agent are based on the latest observations from its
in-neighbors and the system's next observation time is triggered by a criterion
based on all agents' information. The scenario of continuous monitoring is
first considered, namely all agents' instantaneous states can be observed. It
is proved that if the network topology has a spanning tree, then the
centralized event-triggered coupling strategy can realize consensus for the
multi-agent system. Then the results are extended to discontinuous monitoring,
where the system computes its next triggering time in advance without having to
observe all agents' states continuously. Examples with numerical simulation are
provided to show the effectiveness of the theoretical results
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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