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Brief Papers
Observer-based
fuzzy control for nonlinear stochastic systems
with multiplicative noise and successive packet dropouts
Ming Gao
a
, Li Sheng
a,
n
, Yurong Liu
b,c
, Zhengmao Zhu
d
a
College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
b
Department of Mathematics, Yangzhou University, Yangzhou 225002, China
c
Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
d
Institute of Science and Technology, North China Electric Power University, Beijing 102206, China
article info
Article history:
Received 1 July 2015
Received in revised form
18 August 2015
Accepted 21 August 2015
Communicated by Z. Wang
Available online 29 August 2015
Keywords:
Stochastic systems
control
T–S fuzzy model
Multiplicative noise
Successive packet dropouts
abstract
In this paper, the observer-based
control problem is investigated for a class of T–S fuzzy nonlinear
stochastic systems with multiplicative noise and successive packet dropouts. The stochastic system
under study is subject to state- and disturbance-dependent noises, and the successive packet dropouts
are assumed to obey the Bernoulli random binary distribution. By employing the stochastic analysis
approach, a sufficient condition is derived such that the closed-loop system is stochastically stable, and
the prescribed
disturbance-rejection-attenuation performance is also achieved. Moreover, the
observer-based
fuzzy controller design method is proposed in terms of linear matrix inequalities for
the systems with successive packet dropouts. Finally, a numerical example is exploited to demonstrate
the effectiveness of the obtained results.
& 2015 Elsevier B.V. All rights reserved.
1. Introduction
The past few decades have witnessed an ever increasing
research interest in the
H
control problem, which is one of the
most important robust controller design approaches. The study of
H
control first starts from the deterministic linear systems, and
the derivation of the state-space formulation of the standard
H
control [9] makes it possible to develop the
H
control theory for
nonlinear systems [16] or stochastic systems [15]. Due to the wide
applications of stochastic systems with multiplicative noise in the
fields of synthetic biology [1], mathematical finance [32], portfolio
selection [33], etc., the linear and nonlinear stochastic
H
pro-
blems of such systems have received a great deal of attention in
recent years [19,24,30,28,34]. From the dissipation point of view,
the state feedback
H
control problem has been studied for a
general class of nonlinear stochastic systems in [34].In[30], the
problem of dynamic output feedback
H
control has been dis-
cussed for nonlinear stochastic time-delay systems with missing
measurements and logarithmic quantization effects. Generally, the
nonlinear stochastic
H
controller design can be achieved by sol-
ving second-order coupled Hamilton–Jacobi equations (HJEs) [34]
or Hamilton–Jacobi inequalities (HJIs) [30]. However, it is usually
difficult to fi nd efficient numerical algorithms to solve such cou-
pled HJEs or HJIs.
For several decades, the Takagi–Sugeno (T–S) fuzzy model has
been widely applied for the control design of nonlinear systems
[5,10,21,27,37], since it can approximate any smooth nonlinear
system to any specifi ed accuracy by blending a family of local
linear models through fuzzy membership functions. Recently, the
T–S fuzzy model has been employed to deal with the control and
filter problems of nonlinear stochastic systems with multiplicative
noise [1,25,26].In[26], based on the T–S fuzzy model, the
H
filter
problem of nonlinear stochastic systems has been transformed
into the solvability of a set of linear matrix inequalities (LMIs)
instead of coupled HJIs, which provides a possible approach to
solve the HJIs. Along the same line of [26], a robust optimal
reference-tracking design method has been proposed in [1] for
synthetic gene networks which described by nonlinear stochastic
systems. By using the fuzzy approach, the state feedback
H
control problem has been discussed in [25] for nonlinear sto-
chastic systems with Markov jump parameters. However, up to
now, the problem of observer-based
H
fuzzy control for nonlinear
stochastic systems with multiplicative noise has not received
adequate research attention yet.
On the other hand, the study of networked control systems
(NCSs) has gradually become an active area of research owing to
their advantages such as low cost, reduced weight, and ease of
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/neucom
Neurocomputing
http://dx.doi.org/10.1016/j.neucom.2015.08.042
0925-2312/& 2015 Elsevier B.V. All rights reserved.
n
Corresponding author.
E-mail address: victory8209@163.com (L. Sheng).
Neurocomputing 173 (2016) 2001–2008