Simulation and Comparison of Different Types of First-order Decentralized Sliding
Mode Estimators
Ning Zhou,
College of Computer and
Information Sciences, Fujian
Agriculture and Forestry
University, Fuzhou, China.
Email: zhouning2010@gmail.com;
Guoxing Wen
Department of
Mathematics,
Binzhou University,
Binzhou, China.
Email: gxwen@live.cn;
Jie Huang
†
,
College of Electrical
Engineering and Automation,
Fuzhou University,
Fuzhou, China.
Email: autohuangjie@gmail.com;
Qingkai Yang, Liangming Chen
Faculty of Science and
Engineering, University of
Groningen, Groningen,
9742AG, Netherlands.
Email: qingkyang@gmail.com;
chenliangming@hit.edu.cn
Abstract—This paper focuses on the simulation and com-
parison of different types of first-order decentralized sliding
mode estimators (FDSMEs). From the previous works, three
types of FDSMEs were presented and applied to solve the
cooperative control problems. Utilizing the FDSME, a finite-
time leader-follower tracking control algorithm is proposed for
a networked single-integrator vehicle system. Then based on
the existing structure of FDSME, a new compound FDSME is
developed to improve the estimation performance. Simulation
and comparison of all the presented FDSMEs are given in detail
to evaluate the theoretical results. Finally, regulation rules of
the parameters in the compound FDSME are summarized
according to the simulation results.
Keywords-First-order Decentralized Sliding Mode Estimator,
Finite-time Control, Cooperative Control
I. INTRODUCTION
In recent years, the cooperative control of multi-vehicle
systems has received considerable attention in the control
society, including consensus, formation control, flocking,
swarming, etc. This is not only due to the important theo-
retical significance and broad practical applications in areas
such as industry and medical domain, but also due to its
superiorities in contrast with individual systems, such as
higher efficiency and reusability, better robustness and larger
service areas.
Finite-time control method can offer fast convergence
and high precision which are the practical requirements in
achieving control objectives with special accuracy demand.
As an effective tool, various kinds of FDSMEs were pro-
posed in [1]-[3] and applied to design finite-time cooperative
control algorithms [4]-[7] for multi-vehicle system, multiple
spacecraft system and multiple autonomous underwater ve-
hicles (AUVs), etc. For example, in [1], Cao, Ren and Meng
first proposed and studied both first-order and second-order
decentralized sliding mode estimators, which can guaran-
tee the finite-time convergence of estimation, respectively.
Then they employed the proposed estimators to achieve
decentralized formation tracking of multiple autonomous
vehicles. As verified in the previous literature [1]-[7], the
FDSME can provide an alternative way to develop the
finite-time cooperative control algorithms, but the questions
of what the differences are between these estimators and
how do the parameters affect the estimation performance
remain unresolved. Thus the highlight of this paper is to
compare different types of first-order decentralized sliding
mode estimators based on theoretical analysis and simulation
data, and find the general rules for adjusting parameters.
II. GRAPH THEORY
The network topology among neighboring vehicles is
represented by a directed graph G which consists of a set
of nodes Υ = {v
i
} for i = 1, . . . , n, a set of edges
E ⊆ Υ × Υ, and an adjacency matrix A = [a
ij
] ⊂ R
n×n
.
Let v
i
represent the ith vehicle, there exists information flow
from node v
j
to node v
i
if (v
i
, v
j
) ∈ E, and a
ij
> 0,
otherwise, a
ij
= 0. Self-edges are not allowed, which means
a
ii
= 0. A directed path is a sequence of edges in the
form of (v
1
, v
2
), (v
2
, v
3
), (v
3
, v
4
), . . ., (v
n−1
, v
n
). G has
a directed spanning tree if and only if it has at least one
node with a directed path to all other nodes. The neighbors
of v
i
is denoted by a set N
i
= {j : (v
i
, v
j
) ∈ E}. The
in-degree matrix of the weighted graph G is denoted by
D = diag{d
1
, . . . , d
n
}, and d
i
=
P
n
j=1
a
ij
. The Laplacian
matrix L of G is defined as L = D − A. Note that A, D
and L are all constant and bounded matrices. Define graph
G
0
with Υ = {v
0
, v
i
}. Denote B = diag{b
1
, . . . , b
n
} be the
leader adjacency matrix, and b
i
> 0 if v
i
has access to v
0
,
otherwise, b
i
= 0.
Lemma 2.1: [8] If G
0
has a directed spanning tree, then
[L + B] is invertible.
III. DIFFERENT TYPES OF FIRST-ORDER
DECENTRALIZED SLIDING MODE ESTIMATORS
Considering the requirements of practical applications, we
assume that all vehicles are in a 3-dimensional space for
further presentation. It is worth noting that all the results
are still valid for both low-dimensional space or high-
dimensional space by the introduction of the Kronecker
product.
2018 15th International Conference on
Control, Automation, Robotics and Vision (ICARCV)
Singapore, November 18-21, 2018
978-1-5386-9582-1/18/$31.00 ©2018 IEEE 1087
资源评论
weixin_38526914
- 粉丝: 7
- 资源: 909
最新资源
- Scaled-YOLOv4训练自己的数据集详细教程PDF，关于论文阅读请联系小编获取PDF文档.zip
- Scaled-YOLOv4 的 Tensorflow2.x 实现，如 Scaled-YOLOv4扩展跨阶段部分网络中所述.zip
- python动态化爱心图源代码
- RK3588 版 YOLOv5.zip
- RK3566,68,88 的 YoloV5 NPU.zip
- 计算机行业教育新基建跟踪-华西证券-12页.pdf
- 路面水坑，路面坑洼检测，有YOLOV7标记 5000+标记图片
- QT+NCNN 小米手机运行 YOLOv8s.zip
- adafasfww dddsa
- Pytorch 轻松重新实现“YOLOX2021 年超越 YOLO 系列”.zip
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
