Adaptive backstepping-based fuzzy tracking control scheme for
output-constrained nonlinear switched lower triangular systems with
time-delays
$
Ben Niu
a,
n
, Lina Liu
a
, Yanyan Liu
b
a
College of Mathematics and Physics and Automation Research Institute, Bohai University, Jinzhou, Liaoning Province 121013, People's Republic of China
b
AVIC Shenyang Engine Design and Research Institute, Shenyang, Liaoning Province 110015, People's Republic of China
article info
Article history:
Received 24 July 2015
Received in revised form
20 October 2015
Accepted 2 November 2015
Communicated by Xiaojie Su
Available online 12 November 2015
Keywords:
Switched nonlinear systems
Adaptive fuzzy control
Output constraints
Time-delays
abstract
In this paper, an adaptive backstepping-based fuzzy tracking control scheme is proposed for a class of
output-constrained nonlinear switched lower triangular systems with time-delays. In the design process,
a Barrier Lyapunov Function is employed to deal with the output constraint, the common Lyapunov
function method combined with the recursive backstepping technique and Lyapunov–Krasovskii func-
tionals is used to construct a state feedback controller, and fuzzy logic systems are applied to approx-
imate the unknown nonlinear functions. The constructed controller assures that all signals in the closed-
loop system are bounded without transgression of the constraint, and the system output eventually
converges to a small neighborhood of the desired reference signal. The simulation example is provided to
show the effectiveness of the developed approach.
& 2015 Elsevier B.V. All rights reserved.
1. Introduction
In the last few decades, the stability analysis and control design
problems of switched systems have attracted considerable atten-
tion, due to their numerous engineering applications [1–9]. The
design of switching laws plays a key role in the study of switched
systems. It is pointed out that the switching laws can be classified
into arbitrary switchings and constrained switchings [10–17]. For
the case of arbitrary switchings, the stability of a switched system
was proved to be equivalent to the existence of a common Lya-
punov function (CLF) for all subsystems. Therefore, how to find a
CLF to study stability is of great significance. For switched non-
linear systems, however, finding a CLF is often diffi cult and chal-
lenging, due to the complexity of the system structure involving a
family of subsystems and nonlinear assumption, especially the
switched system is in lower triangular form.
Although various control design methods have been proposed
for switched nonlinear systems, the constrained control problems
have not been fully considered until now. It needs to be
emphasized that during operation violation of the physical con-
straints leads to performance degradation, hazards or system
damage. Hence, the study on output constraints in control design
is of great theoretical and engineering significance. Many control
design approaches have been proposed for non-switched systems
to handle the output constraints, such as model predictive control
[18], reference governors [19], and the use of set invariance
notions [20] . Further, the control design based on Barrier Lyapunov
function (BLF) is first studied in [21], which is proven to be a very
effective technique to deal with output constraints. By using the
BLF, [22] developed a control design for the single input and single
output (SISO) nonlinear systems with an output constraint, [23,24]
presented the state-feedback control designs for a class of output-
constrained nonlinear switched systems, [25] investigated the
adaptive fuzzy output feedback control for the SISO nonlinear
system with output constraints and input saturation. Nevertheless,
the considered systems in [21–25] are restricted to a class of
nonlinear systems without time delays. In addition, they cannot
deal with the nonlinear systems with external disturbances.
On the other hand, the fuzzy or neural network control tech-
niques have been widely used in many adaptive control problems
of uncertain nonlinear systems [26–34]. More specifically, fuzzy
logic system (FLS) is constructed from a collection of fuzzy IF–
THEN rules. In practical applications, FLSs are often used as
approximation models for the unknown parts of nonlinear sys-
tems, and many important results on adaptive backstepping-based
fuzzy control have been proposed in the past years. For example,
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journal homepage: www.elsevier.com/locate/neucom
Neurocomputing
http://dx.doi.org/10.1016/j.neucom.2015.11.006
0925-2312/& 2015 Elsevier B.V. All rights reserved.
☆
This work was partially supported by the National Natural Science Foundation
of China [Grant nos. 61304054, 61403041 and 61403354], the Program for Liaoning
Provincial Excellent Talents in University, China [Grant no. LJQ2014122], and the
China Postdoctoral Science Foundation [Grant no. 2015M581333].
n
Corresponding author.
E-mail addresses: niubenbhu@gmail.com, niubenbhu@163.com (B. Niu),
liulinabhu@163.com (L. Liu), yanyanliu11@yahoo.cn (Y. Liu).
Neurocomputing 175 (2016) 759–767