Decentralized finite-time adaptive consensus of multiagent systems
with fixed and switching network topologies
Zhizhong Tu
a,b
, Hui Yu
a,
n
, Xiaohua Xia
b
a
College of Science, China Three Gorges University, Yichang 443002, China
b
Centre of New Energy Systems, Department of Electrical, Electronics and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa
article info
Article history:
Received 16 December 2015
Received in revised form
11 July 2016
Accepted 7 September 2016
Communicated by Long Cheng
Available online 14 September 2016
Keywords:
Multiagent system
Unknown nonlinear dynamics
Finite-time consensus
Finite-time parameter convergence
abstract
In this paper, finite-time adaptive consensus problem is investigated for first-order multiagent systems
with unknown nonlinear dynamics. Linearly parameterized method is introduced to model unknown
nonlinear dynamics of the systems. By only utilizing the local relative position state information between
each agent and its neighbors, decentralized finite-time adaptive consensus algorithms are presented
with directed fixed and switching network topologies which satisfy detailed balance condition. Based on
classical Lyapunov analysis techniques, both finite-time stability and finite-time parameter convergence
are guaranteed by making use of the proposed control algorithms. Finally, the results in Simulations part
are presented to validate our main results.
& 2016 Elsevier B.V. All rights reserved.
1. Introduction
The topic of distributed coordinated control of multiple dynamical
agents has received extensi ve attention by many researchers over the
past few decades [1–7]. This is not only due to an increasing interest
in understanding thought-provoking animal group behaviors, such as
floc king and swarming, but also due to its broad applications in di-
verse places, such as multi-vehicles rend ezvou s, attitude alignment,
formation control of autonomous robots, unmanned aerial vehicles
and so forth. Consensus problem is the fundamental problem in
multiagent systems . The essence of it is to construct proper control
laws so that all agents can attain a consensus decision value by using
the information of each agent and its neighbors. In order to achieve
improved cooperative performances for multiagent systems, v arious
works have been done in [8–14],tociteonlyafew.
In recent years, finite-time consensus problem becomes a re-
search hotspot in multiagent systems. The purpose of it is to con-
struct proper control protocols such that finite-time consensus can
be attained. Although asymptotic consensus is enough to satisfy
practical demand in engineering in general, finite-time consensus is
sometimes more desirable for some engineering applications, such
as in some situations where rigid convergence time and high pre-
cision must be met. Compared with conventional asymptotic
consensus, finite-time consensus reveals numerous advantages, for
instance, faster response, higher accuracy, and better robustness and
anti-disturbance performance against uncertainties and so forth. On
account of these superiorities, several kinds of finite-time consensus
protocols have been proposed for first-order [15,16], second-order
[17,18] or high-order [19] multiagent systems.
However, most of the existing works focus attention on finite-
time consensus algorithms design for multiagent systems without
unknown nonlinear dynamics. In [20–23], by employing state
feedback or adaptive design methods, finite-time consensus pro-
blems are investigated for multiagent systems under the absence
of unknown nonlinear dynamics. On the current situation, it is a
big challenge to construct decentralized finite-time adaptive con-
trol laws for multiagent systems with embedded unknown non-
linear dynamics such that finite-time consensus is attained. In
[24], a class of distributed controllers is developed for solving fi-
nite-time leaderless consensus problem of nonlinear multiagent
systems with parametric uncertainties under an undirected graph.
In [25], fi nite-time consensus problem is solved for a group of
high-order agents taking into account unknown nonlinear dy-
namics under undirected fixed network topology. The finite-time
stability is derived by employing the homogeneous Lyapunov
function, which is too complicated to find a specific form of it. In
addition, the finite-time consensus algorithms designed in this
paper, which base on local consensus errors and relative position
measurements between each agent and its neighbors, are not
purely decentralized.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/neucom
Neurocomputing
http://dx.doi.org/10.1016/j.neucom.2016.09.013
0925-2312/& 2016 Elsevier B.V. All rights reserved.
n
Corresponding author.
E-mail addresses: tuzhizhongsina@163.com (Z. Tu), yuhui@ctgu.edu.cn (H. Yu),
xxia@up.ac.za (X. Xia).
Neurocomputing 219 (2017) 59 – 67