Research Article
Event-Triggered Consensus Control for Leader-Following
Multiagent Systems Using Output Feedback
Yang Liu
1,2
and Xiaohui Hou
1
1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
The Seventh Research Division, Beihang University, Beijing 100191, China
Correspondence should be addressed to Yang Liu; ylbuaa@163.com
Received 8 May 2018; Accepted 10 July 2018; Published 15 August 2018
Academic
Editor:
Yimin
Zhou
Copyright © 2018 Yang Liu and Xiaohui Hou. 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.
The event-triggered consensus control for leader-following multiagent systems subjected to external disturbances is investigated, by
using the output feedback. In particular, a novel distributed event-triggered protocol is proposed by adopting dynamic observers to
estimate the internal state information based on the measurable output signal. It is shown that under the developed observer-based
event-triggered protocol, multiple agents will reach consensus with the desired disturbance attenuation ability and meanwhile
exhibit no Zeno behaviors. Finally, a simulation is presented to verify the obtained results.
1. Introduction
Consensus is a basic problem in the cooperative control
of multiagent systems [1–5], which is generally realized
through the behavior-based method or the leader-following
approach. To be specific, the leader-following consensus
problem has been studied in [6–12] from different perspec-
tives, where all the following agents can reach consensus by
tracking a real or virtual leader based on local interactions.
However, all the work mentioned above requires that the
control system is implemented in a continuous or time-
sampled triggered way, which would consume some unnec-
essary energy and computing resources in applications.
To reduce the resource consumption, the event-triggered
scheme has been applied to the consensus control problem.
In particular, the event-triggered consensus of leader-
following multiagent systems was studied in [13–16], where
the dynamics of agents was restricted to single- or double-
integrator. Furthermore, the general linear multiagent system
was considered in [17–21]. In [18], a distributed event-
triggered strategy was proposed with state-dependent thresh-
old so that the following agents could asymptotically track
the leader without continuous communication. But it was
assumed that all the following agents were aware of state
information of the leader. In [19], the distributed, central-
ized, and clustered event-triggered schemes were proposed
for different network topologies, which could reduce the fre-
quency of controller updates. In [21], an existing continuous
control law was extended with a novel event-triggered condi-
tion, and it was proved that the system remained the desired
performance with much lower controller updating fre-
quency. In the literatures mentioned above, the consensus
protocols and event-triggered conditions are both designed
using the internal state information that is very hard or even
impossible to be accurately obtained in real systems [22]. In
addition, the external disturbance always exists in realistic
situations, whose influence also has to be taken into account.
In this paper, the output-feedback event-triggered con-
sensus is investigated for leader-following multiagent sys-
tems with high-order linear dynamics, subject to external
disturbances. A novel distributed control protocol is pro-
posed with an observer form, by using the local output infor-
mation. Then, sufficient conditions are derived to guarantee
that the system can reach consensus asymptotically with
the desired disturbance attenuation ability but without Zeno
behavior. The contributions of our research are as follows.
The difficulties in directly obtaining full states is overcom e
by designing a local state observer, whose output is used to
Hindawi
Complexity
Volume 2018, Article ID 6342683, 9 pages
https://doi.org/10.1155/2018/6342683