Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2012, Article ID 493715, 18 pages
doi:10.1155/2012/493715
Research Article
Dynamic Feedback Backstepping Control for a
Class of MIMO Nonaffine Block Nonlinear Systems
Hai-Yan Li, Yun-An Hu, Jian-Cun Ren, and Min Zhu
Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai,
Shandong 264001, China
Correspondence should be addressed to Yun-An Hu, hya507@sina.com
Received 23 November 2011; Revised 26 January 2012; Accepted 4 April 2012
Academic Editor: Christos H. Skiadas
Copyright q 2012 Hai-Yan Li et al. 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.
For a class of MIMO nonaffine block nonlinear systems, a neural network- NN- based dynamic
feedback backstepping control design method is proposed to solve the tracking problem. This
problem is difficult to be dealt with in the control literature, mainly because the inverse controls
of block nonaffine systems are not easy to resolve. To overcome this difficulty, dynamic feedback,
backstepping design, sliding mode-like technique, NN, and feedback linearization techniques are
incorporated to deal with this problem, in which the NNs are used to approximate and adaptively
cancel the uncertainties. It is proved that the whole closed-loop system is stable in the sense of
Lyapunov. Finally, simulations verify the effectiveness of the proposed scheme.
1. Introduction
In the last two decades, a number of efforts have been made on developing systematic
design tools for control of uncertain nonlinear systems. Among the obtained results, feedback
linearization techniques 1, adaptive backstepping design 2, and NN control 3, 4 are
the representative theoretical achievements. The common assumptions made in most of the
researches are that the systems to be controlled are affine and the nonlinearities are linearly
parameterized by unknown parameters 2, 5. NN-based adaptive control has relaxed the
assumption on linear parameterized nonlinearities mostly in affine systems 4, 6, 7, which
can deal with nonlinear parameterized nonlinearities. But some systems, such as chemical
reactions 8 and flight control systems 9, cannot be expressed in an affine form.
There are three kinds of methods to deal with the controller design for nonaffine
systems.
The idea of the first method is to transform a nonaffine system into an affine system
with respect to a new control input by introducing an integrator 9–12. In these attempts, an
augmented system affine in u̇ is derived for control design by differentiation 13.
2
Mathematical Problems in Engineering
The second method directly controls a nonaffine system without transformation to
an affine system 14–19. Under the assumption that a control Lyapunov function CLF
was available, Moulay and Perruquetti 16 obtained a sufficient condition to guarantee the
existence of a continuous stabilizing control for nonaffine systems. Lin 14, 15 presented
how nonaffine passive systems theory, together with the techniques of feedback equivalence
and bounded control, could be used to explicitly construct a smooth state feedback control
law that solved the problem of global stabilization for nonaffine nonlinear systems. New
state feedback stabilizing controllers and sufficient conditions of asymptotic stability were
proposed by Shiriaev and Fradkov 18 under assumptions similar to those in 14. But it is
difficult to find a CLF and to deal with controller design for systems with uncertainties.
The third one employs NNs, PI, or fuzzy-neural models to approximate the inverse
system or the uncertainties in controller design for nonaffine nonlinear systems 20–28. For a
class of general nonaffine nonlinear systems, virtual-linearized-system- VLS- based design
methods were proposed, in which the T-S fuzzy-neural model was employed to approximate
a VLS of a real system with modelling errors and external disturbances 20, 26, 27. Teo et
al. 25 constructed a proportional-integral PI controller for the minimum-phase nonaffine
system, which was an equivalent realization of an approximate dynamic inversion controller.
Ge and Zhang 21 suggested using NNs as emulators of inverse systems for controller design
of general nonlinear systems. Using the implicit function theory and the mean value theorem,
an NN was employed to approximate an ideal control signal which solved the tracking
problem in 22. In 29–32, instead of seeking a direct solution to the inverse problem, a
solution was obtained by introducing an analytically invertible model and then employing
an NN to compensate inversion error. By using implicit function theorem and Taylor series
expansion, an observer-based adaptive fuzzy-neural control scheme was presented for the
nonaffine nonlinear system in the presence of unknown structure of nonlinearities 33. A
neural synthesis method was considered for a class of multivariable nonaffine uncertain
systems 28. The method extended the previous approach developed in a single-input
single-output system to a multi-input multi output system without resorting to a fixed-point
assumption or boundedness assumption on the time derivative of a control effectiveness
term. The difficulty associated with these methods for nonaffine control systems is that an
explicit inverting control design is, in general, not possible even if the inverse exists by the
implicit function theorem 28. Moreover, this kind of method relies on the approximation
ability of NN.
Backstepping method is one of the breakthroughs in design of nonlinear control
systems. Therefore, it has become one of the important and popular approaches for nonlinear
systems 2. This approach is based on a systematic procedure for the design of feedback
control strategies suitable for the design of a large class of nonlinear systems with unmatched
uncertainties, and it guarantees global regulation and tracking for the class of nonlinear
systems transformable into the strict-feedback form. Developing a systematic synthesis
method for general nonaffine systems still remains a challenging problem.
In this paper, we discussed the NN-based backstepping design for a class of uncertain
nonaffine systems in block control form. The main contributions of this paper can be
summarized as follows: 1 the proposed method avoids the difficulties to solve the inverse
control in most literatures; 2 it does not rely on implicit function theorem and Taylor
series expansion which makes the output tracking difficult; 3 it can deal with the systems
with unmatched uncertainties; 4 introducing the sliding mode surface-like variables into
backstepping procedure makes the design and stability analysis clear and simple; 5 a
Mathematical Problems in Engineering
3
systematic procedure is proposed for tracking control design for a class of block nonlinear
systems that are nonaffine in the control inputs.
The rest of the work is organized as follows. The problem formulation is introduced
in Section 2. The controller design and stability analysis are given in Section 3. Simulation
example is given in Section 4 and followed by Section 5 which concludes the work.
2. Problem Formulation
The uncertain block nonaffine system considered in this paper is given by
ẋ1 f1 x1 , x2 ,
ẋ2 f2 x2 , x3 ,
..
.
2.1
ẋn fn xn , u,
y x1 ,
T
where xi xi1 · · · xim T ∈ Rm , xi x1T · · · xiT , i 1, 2, . . . , n, u ∈ Rm , and y ∈ Rm , are state
variables, input and output, respectively.
Remark 2.1. Although we assume xi ∈ Rm , the proposed method is easy to extend to the other
cases by using pseudoinversion.
Assumption 2.2. fi xi1 fi1 xi1 , fi2 xi1 , . . . , fim xi1 T , i 1, 2, . . . , n, is an unknown
smooth function vector.
The control objective is to design an adaptive NN controller for the system 2.1 such
that the output tracks the desired signal yd and all signals in the closed-loop system remain
bounded. Let · denote the 2-norm, and let · F denote the Frobenius norm.
3. Controller Design and Stability Analysis
In the following, introducing sliding mode-like technique, a systematic design method is
proposed for a class of the uncertain block nonaffine systems.
Consider the following NN:
hx W T S V T x ,
3.1
where W w1 w2 · · · wl T ∈ Rp×l and V v1 v2 · · · vi T ∈ RN×l are the first-to-second layer
and the second-to-third layer weights, respectively, hx ∈ Rp , p ≥ 1, x ∈ RN is the input
vector, and the node number is l > 1:
T T
x 1 ,
S V T x sv v1T x sv v2T x · · · sv vl−1
where sv xa 1/1 e−γxa with the constant γ > 0.
3.2
4
Mathematical Problems in Engineering
Before designing the controller, we make the following assumptions.
Assumption 3.1. One has a function vector hx : Ω → Rp ; for any σ > 0, there always
exist a Gauss function array S : RN → Rl and an optimal weight matrix W ∗ such that
hx − W ∗T SV ∗T x ≤ σ, for all x ∈ Ω, where Ω is a compact subset of RN , W ∗T SV ∗T x
def
is the optimal approximation of hx using NN, and hx − W ∗T SV ∗T x Δhx is called
W
− W ∗ and V
V − V ∗ , where W
and V are the estimated
reconstruction error. Define W
∗
∗
values of W and V .
−1
Assumption 3.2. Jx−1i 0 and Ju0
, i 2, . . . , n, exist. Jxi xi−1 , xi ∂fi−1 xi−1 , xi /∂xi and
Ju xn , u ∂fn xn , u/∂u denote the Jacobians with respect to xi and u, respectively.
Let fi−1 xi−1 , xi fi−10 xi−1 , xi Δfi−10 xi−1 , xi , fn xn , u fn0 xn , u Δfn0 xn , u,
Jxi xi−1 , xi Jxi 0 xi−1 , xi ΔJxi 0 xi−1 , xi , and Ju xn , u Ju0 xn , u ΔJu0 xn , u,
where fi−10 xi−1 , xi , fn0 xn , u, Jxi 0 xi−1 , xi , and Ju0 xn , u are the nominal parts of the
functions fi−1 xi−1 , xi , fn xn , u, Jxi xi−1 , xi , and Ju xn , u, respectively, and Δfi−10 xi−1 , xi ,
Δfn0 xn , u, ΔJxi 0 xi−1 , xi , and ΔJu0 xn , u are the unknown parts.
Remark 3.3. Assumption 3.2 is not a strong condition imposed on the system. In fact, because
Jxi 0 xi−1 , xi and Ju0 xn , u are the nominal parts of the functions Jxi xi−1 , xi and Ju xn , u,
respectively, we can modify the values of the elements of Jxi 0 xi−1 , xi and Ju0 xn , u such that
−1
, i 2, . . . , n, exist.
Jx−1i 0 , and Ju0
Lemma 3.4 see 31, 34. For the NN approximator, the approximation error can be described as
T S − S V T X W
T S V
T X du ,
T S V T X − W ∗T S V ∗T X W
W
3.3
where S SV T X, S diag{sv1 , sv2 , . . . , svl }
svi sv viT X dsv xa /dxa |x vT X , and the
a
i
residual term du satisfies the following inequality:
T S X W ∗ F S V T X W ∗ F l.
du ≤ V ∗ F W
F
F
3.4
Step 1. Consider the first subsystem of 2.1 ẋ1 f1 x1 , x2 . Taking its derivative gives
ẍ1 Jx1 x1 , x2 f1 x1 , x2 Jx2 x1 , x2 ẋ2 ,
3.5
where Jx1 x1 , x2 ∂f1 x1 , x2 /∂x1 denotes the Jacobian with respect to x1 . Equation 3.5
can be rewritten as
ẍ1 Jx1 0 x1 , x2 f10 x1 , x2 Jx2 0 x1 , x2 ẋ2 Δf1 x1 , x2 ,
3.6
where
Δf1 x1 , x2 ΔJx1 0 x1 , x2 f10 x1 , x2 Jx1 0 x1 , x2 Δf10 x1 , x2 ΔJx1 0 x1 , x2 Δf10 x1 , x2 ΔJx2 0 x1 , x2 ẋ2 .
3.7
Mathematical Problems in Engineering
5
Let z1 x1 − x1d and s1 z1 c1 ż1 , where c1 > 0 is a constant, s1 is a sliding mode surface-like
vector, and x1d is the reference signal of x1 . Taking the time derivative of s1 , we can obtain
ṡ1 ż1 c1 z̈1
ż1 c1 Jx1 0 x1 , x2 f10 x1 , x2 c1 Jx2 0 x1 , x2 ẋ2 c1 Δf1 x1 , x2 − c1 ẍ1d
ż1 c1 Jx1 0 x1 , x2 f10 x1 , x2 c1 Jx2 0 x1 , x2 ẋ2d
3.8
− Jx2 0 x1 , x2 z2 c1 Δf1 x1 , x2 − c1 ẍ1d Jx2 0 x1 , x2 s2 .
Let z2 x2 − x2d and s2 z2 c1 ż2 , where x2d is the desired signal of x2 and s2 is a sliding
mode surface-like vector.
Choose the virtual control as
1 − Jx2 0 x1 , x2 z2 v1 ,
ẋ2d −c1 Jx2 0 x1 , x2 −1 c1 Jx1 0 x1 , x2 f10 x1 , x2 ż
3.9
where vi ki si − ci ẍid vNNi − vir , i 1, 2, . . . , n, v1r will be defined in 3.20, ki is a diagonal
i is the output of a tracking differentiator 35 with zi
matrix with its elements positive, and ż
as its input. The error between żi and żi can be approximated by a neural network. vNNi is the
NN compensator, which is used to overcome the influence of the uncertainties in the system.
According the approximation ability, we can assume that
ci Δfi xi , xi1 Δi Wi∗T Si Vi∗T Xi εi ,
3.10
where Wi∗T Si Vi∗T Xi εi is the optimal approximation of Δfi xi , xi1 Δi , Δi is the
i and
uncertainty induced by the error between the output of the tracking differentiator ż
T
i , and Xi xTi , xT , ẋTi , 1 is the input of NN. εi ≤ εiu is the
żi , namely, Δi żi − ż
i1
approximation error with constant εiu > 0.
Let
T Si V T Xi ,
vNNi W
i
i
i 1, 2, . . . , n.
3.11
Substituting 3.9 into 3.8 leads to
ṡ1 c1 Δf1 x1 , x2 Δ1 − c1 ẍ1d Jx2 0 x1 , x2 s2 − v1
−c1 ẍ1d Jx2 0 x1 , x2 s2 − v1 W1∗T S1 V1∗T X1 ε1 .
3.12
Substituting the expressions of v1 and vNN1 into 3.12 gives
T S1 V T X1 W ∗T S1 V ∗T X1 ε1 Jx2 0 x1 , x2 s2 v1r .
ṡ1 −k1 s1 − W
1
1
1
1
3.13
6
Mathematical Problems in Engineering
According to Lemma 3.4, 3.13 can be transformed into
ṡ1 −k1 s1 Jx2 0 x1 , x2 s2
T S V
T X1 − du1 ε1 v1r .
T S1 − S V T X1 − W
−W
1 1
1
1 1 1
3.14
We choose Lyapunov function as
V1 1 T
1 T −1 1 T −1 s1 s1 tr W
tr V1 ΓV 1 V1 ,
1 ΓW1 W1 2
2
2
3.15
where ΓW1 ΓTW1 > 0 and ΓV 1 ΓTV 1 > 0 are constant design parameters. Taking the derivative
of V1 , we have
˙
T Γ−1 V˙ 1
T Γ−1 W
tr
V̇1 sT1 ṡ1 tr W
V
1
1 W1
1 V1
T S V
T X1 − du1 ε1 v1r
T S1 − S V T X1 − W
sT1 −k1 s1 Jx2 0 x1 , x2 s2 − W
1 1
1
1 1 1
˙
T Γ−1 W
T Γ−1 V˙ 1
tr W
tr
V
1
1 W1
1 V1
3.16
˙
T
T −1 ˙
T Γ−1 W
−k1 s1 2 tr W
1 tr V1 ΓV 1 V 1 s1 Jx2 0 x1 , x2 s2
1 W1
T S V
T X1 sT −du1 ε1 v1r .
T S1 − S V T X1 W
− sT1 W
1 1
1
1 1 1
1
Choose the following adaptive tuning laws as
˙ Γ
W
1
W1
1 ,
S1 − S1 V1T X1 sT1 − σW1 W
T S − σV 1 V1 ,
V˙ 1 ΓV 1 X1 sT1 W
1 1
3.17
where σW1 > 0 and σV 1 > 0 are small design parameters. Substituting 3.17 into 3.16 results
in
1
T S1 − S V T X1 sT − σW1 W
V̇1 −k1 s1 2 tr W
1 1
1
1
T S − σV 1 V1 − sT du1 − ε1 − v1r tr V
1T X1 sT1 W
1 1
1
3.18
T S V
T X1 sT Jx2 0 x1 , x2 s2 .
T S1 − S V T X1 W
− sT1 W
1 1
1
1 1 1
1
With the property tr{yxT } xT y, 3.18 can be simplified as
1 − σV 1 tr V
T V1 − sT du1 − ε1 − v1r sT Jx2 0 x1 , x2 s2 .
T W
V̇1 −k1 s1 2 − σW1 tr W
1
1
1
1
3.19
Mathematical Problems in Engineering
7
Design the robust term vir as
vir −
T 2
T 2
2
si Wi Si Xi Si Vi Xi 2
F
F
ηi
,
i 1, 2, . . . , n,
3.20
where ηi > 0 is a small constant. After applying Lemma 3.4 and substituting 3.20 into 3.19,
V̇1 is upper bounded by
1 − σV 1 tr V
T V1
T W
V̇1 ≤ −k1 s1 2 − σW1 tr W
1
1
T 2
T 2
2
S
X
2
S
V
s1 2 W
X
1
1
1 1
1 1
F
F
sT1 Jx2 0 x1 , x2 s2
η1
∗ T W ∗ ε1u .
W V T X1 S
sT1 V1∗ F W
l
S
X
1
1 F
1 1
1 F
1 1
−
F
3.21
F
With the following properties 31:
2
1 1 ≤ σW1 W ∗ 2 − σW1 T W
−σW1 tr W
W
,
1 F
1
F
2
2
σ σV 1 2
V 1 ∗ 2
−σV 1 tr V
1T V1 ≤
V1 F −
V1 ,
F
2
2
T 2
s η1 2
T 2
T ∗ T S1 X1 2 V1∗ F ,
s1 V1 F W1 S1 X1 ≤ 1 W
1
F
F
η1
4
T 2
2 η s 1
2
T ∗ T s1 W1 F S1 V1 X1 ≤ 1 S1 V1T X1 W1∗ F ,
F
F
η1
4
2sT 2 lη1 T
1
∗
W ∗ 2 η1 ε2 ,
l W1 F ε1u ≤
s1 1 F
η1
4
4 1u
3.22
Equation 3.21 can be simplified as
V̇1 ≤ −k1 s1 2 −
σW1 2 σV 1 2
W1 −
V1 b1 sT1 Jx2 0 x1 , x2 s2 ,
F
F
2
2
3.23
2
where b1 η1 1/4W1∗ 2F 1/4V1∗ 2F l/4W1∗ 2F ε1u
σW1 /2W1∗ 2F σV 1 /2V1∗ 2F is a bounded constant.
Step 2. Let us consider the subsystem ẋ2 f2 x2 , x3 . Taking its derivative leads to
ẍ2 Jx2 x2 , x3 f2 x2 , x3 Jx3 x2 , x3 ẋ3 ,
3.24
8
Mathematical Problems in Engineering
where Jx2 x2 , x3 ∂f2 x2 , x3 /∂x2 denotes the Jacobian with respect to x2 . Equation 3.24
can be rewritten as
ẍ2 Jx2 0 x2 , x3 f20 x2 , x3 Jx3 0 x2 , x3 ẋ3 Δf2 x2 , x3 ,
3.25
where
Δf2 x2 , x3 ΔJx2 0 x2 , x3 f20 x2 , x3 Jx2 0 x2 , x3 Δf20 x2 , x3 ΔJx2 0 x2 , x3 Δf20 x2 , x3 ΔJx2 0 x2 , x3 ẋ3 .
3.26
Taking its time derivative of s2 , we can obtain
ṡ2 ż2 c1 Jx2 0 x2 , x3 f20 x2 , x3 c1 Jx3 0 x2 , x3 ẋ3d
− Jx3 0 x2 , x3 z3 c1 Δf2 x2 , x3 − c1 ẍ2d Jx3 0 x2 , x3 s3 .
3.27
Let z3 x3 − x3d and s3 z3 c1 ż3 , where x3d is the desired signal of x3 and s3 is a sliding
mode surface-like vector.
Design a virtual control signal as
ẋ3d −c1 Jx3 0 x2 , x3 −1
2 − Jx3 0 x2 , x3 z3 JxT 0 x1 , x2 s1 v2 .
· c1 Jx2 0 x2 , x3 f20 x2 , x3 ż
2
3.28
Choose Lyapunov function as
1
1 T −1 1 T −1 V2 V1 sT2 s2 tr W
tr V2 ΓV 2 V2 ,
2 ΓW2 W2 2
2
2
3.29
where ΓW2 ΓTW2 > 0 and ΓV 2 ΓTV 2 > 0 are constant design parameters.
Choose the following adaptive tuning laws:
˙ Γ
W
2
W2
2 ,
S2 − S2 V2T X2 sT2 − σW2 W
T S − σV 2 V2 ,
V˙ 2 ΓV 2 X2 sT2 W
2 2
where σW2 > 0 and σV 2 > 0 are small constants.
3.30
Mathematical Problems in Engineering
9
7
6
5
x11
4
3
2
1
0
0
5
10
15
20
Time (s)
Figure 1: Curve of x11 in case of the system without uncertainties.
Taking 3.25–3.30 into account, we have
V̇2 ≤ −k1 s1 2 − k2 s2 2 −
− σV 2 tr V
2T V2
σW1 2 σV 1 2
2
T W
W1 −
V1 − σW2 tr W
2
F
F
2
2
T 2
T 2
2
S
X
2
S
V
s2 2 W
X
2
2
2 2
2 2
F
3.31
F
sT2 Jx3 0 x2 , x3 s3
η2
∗ T W ∗ ε2u b1 .
W V T X2 S
s2 V2∗ F W
l
S
X
2
2 F
2 2
2 F
2 2
−
F
F
Similar to Step 1, 3.31 can be simplified as
σW1 2 σV 1 2
W1 −
V1 F
F
2
2
2
2
σW2 σV 1 −
W2 −
V2 b1 b2 sT2 Jx3 0 x2 , x3 s3 ,
F
F
2
2
V̇2 ≤ −k1 s1 2 − k2 s2 2 −
3.32
2
where b2 η2 1/4W2∗ 2F 1/4V2∗ 2F l/4W2∗ 2F ε2u
σW2 /2W2∗ 2F σV 2 /2V2∗ 2F is a bounded constant.
Steps 3 to n − 1 are similar to Step 2, which are omitted here.
Step n. Let us consider the system ẋn fn xn , u. Taking its derivative leads to
ẍn Jxn xn , ufn xn , u Ju xn , uu̇,
3.33
10
Mathematical Problems in Engineering
0.35
0.3
0.25
x12
0.2
0.15
0.1
0.05
0
−0.05
0
5
10
15
20
Time (s)
Figure 2: Curve of x12 in case of the system without uncertainties.
0.1
0
−0.1
x21
−0.2
−0.3
−0.4
−0.5
−0.6
0
5
10
15
20
Time (s)
Figure 3: Curve of x21 in case of the system without uncertainties.
where Jxn xn , u ∂fn xn , u/∂xn denotes the Jacobian with respect to xn . Equation 3.33
can be rewritten as
ẍn Jxn 0 xn , ufn0 xn , u Ju0 xn , uu̇ Δfn xn , u,
3.34
where
Δfn xn , u ΔJxn 0 xn , ufn0 xn , u Jxn 0 xn , uΔfn0 xn , u
ΔJxn 0 xn , uΔfn0 xn , u ΔJxn 0 xn , uu̇.
3.35
Mathematical Problems in Engineering
11
20
15
x22
10
5
0
−5
0
5
10
15
20
Time (s)
Figure 4: Curve of x22 in case of the system without uncertainties.
Let zn xn − xnd and sn zn c1 żn , where xnd is the desired signal of xn and sn is a sliding
mode surface-like vector. Taking its time derivative, we can obtain
ṡn żn c1 z̈n
żn c1 Jxn 0 xn , uf20 xn , u
3.36
c1 Ju xn , uu̇ c1 Δfn xn , u − c1 ẍnd .
We choose the control law as
n JxT 0 xn−1 , xn sn−1 vn .
u̇ −c1 Ju0 xn , u−1 c1 Jxn 0 xn , ufn0 xn , u ż
n−1
3.37
Let
nT Sn VnT Xn .
vNNn W
3.38
Let us consider the following Lyapunov function:
Vn n−1
1
1 T −1 1 T −1 tr Vn ΓV n Vn ,
Vi sTn sn tr W
n ΓWn Wn 2
2
2
i1
where ΓWn ΓTWn > 0 and ΓV n ΓTV n > 0 are constant design parameters.
3.39
12
Mathematical Problems in Engineering
7
6
5
x11
4
3
2
1
0
0
5
10
15
20
Time (s)
Figure 5: Curve of x11 in case of the system with uncertainties.
0.35
0.3
0.25
x12
0.2
0.15
0.1
0.05
0
−0.05
0
5
10
15
20
Time (s)
Figure 6: Curve of x12 in case of the system with uncertainties.
Choose the adaptive tuning law as
˙ Γ
W
n
Wn
n ,
Sn − Sn VnT Xn sTn − σWn W
nT Sn − σV n Vn ,
V˙ n ΓV n Xn sTn W
where σWn > 0 and σV n > 0 are small parameters.
3.40
Mathematical Problems in Engineering
13
Similar to the derivation process in Step 1, we have
V̇n ≤ −
n σWi 2 σV i 2
ki si 2 Wi Vi − bi
F
F
2
2
i1
3.41
≤ −kVn b,
2
where bn ηn 1/4Wn∗ 2F 1/4Vn∗ 2F l/4Wn∗ 2F εnu
σWn /2Wn∗ 2F σV n /2Vn∗ 2F n
−1
is a bounded constant, k minj1,...,n {2ki , σWj /λmax ΓWj , σV j /λmax Γ−1
j1 bj .
V j }, and b Integrating 3.41 over 0, t, it can be shown that
Vn t ≤ Vn 0e−kt 1
b
b ≤ Vn 0 ,
k
k
∀ t ≥ 0.
3.42
−1
−1
−1
Defining λmin Γ−1
W minj1,2,···n {λmin ΓWj }, and λmin ΓV minj1,2,···n {λmin ΓV j },
and from 3.39, it can be shown that
n 2
Wj ≤
F
j1
2Vn t
,
λmin Γ−1
W
n 2
Vj ≤
j1
F
2Vn t
.
λmin Γ−1
V
3.43
It is clear that
Vn t ≥
n 1
sj 2 .
2 j1
3.44
It can be seen from 3.41–3.44 that all the closed-loop signals are uniformly ultimately
bounded. Inequality 3.41 implies that V̇n t ≤ −k/2 nj1 sj 2 b holds. Integrating it
yields
t
sj τ2 dτ ≤ 2Vn 0 tb ,
k
0
j 1, . . . , n.
3.45
Summarizing the previous discussion, we have the following results.
Theorem 3.5. Considering the system 2.1, if Assumptions 2.2–3.2 hold, the NN weights are
updated according to 3.17, 3.30, 3.40, and the control u̇ is given in 3.37, and then the following
results hold.
14
Mathematical Problems in Engineering
0.1
0
−0.1
x21
−0.2
−0.3
−0.4
−0.5
−0.6
0
5
10
15
20
Time (s)
Figure 7: Curve of x21 in case of the system with uncertainties.
16
14
12
x22
10
8
6
4
2
0
−2
0
5
10
15
20
Time (s)
Figure 8: Curve of x22 in case of the system with uncertainties.
1 The sliding surfaces sj and the estimated parameter errors of NN are bounded and converge
to the neighbourhoods of the origins exponentially:
⎧
⎨
n 2 2V 0 b/k
n n
j j , V
j sj 2 ≤ 2Vn 0 b/k , ,
Ωj sj , W
W
≤
−1 ⎩
λ
j1
min ΓW
j1
⎫
n 2
⎬
2Vn 0 b/k
, j 1, . . . , n .
Vj ≤
−1 ⎭
λmin Γ
j1
V
3.46
Mathematical Problems in Engineering
15
4
2
0
−2
u1
−4
−6
−8
−10
−12
−14
−16
0
5
10
15
20
Time (s)
Figure 9: Control signal u1 in case of the system with uncertainties.
300
250
200
u2
150
100
50
0
−50
0
5
10
15
20
Time (s)
Figure 10: Control signal u2 in case of the system with uncertainties.
2 The following inequality holds:
1
lim
t→∞ t
t
sj τ2 dτ ≤ 2b .
k
0
3.47
Remark 3.6. It is obvious that the bounded sj , j 1, . . . , n, implies the bounded zj and xj .
Furthermore, if sj → 0 as t → ∞, we also can conclude that zj → 0 and xj → xjd as t → ∞.
Remark 3.7. The result 1 of Theorem 3.5 indicates that adjusting the values of ki , ΓWi , ΓV i ,
σWi , and σV i can control the convergence rate and the size of the convergence region. It
is shown from the expression 3.46 that larger gains ki , ΓWi , ΓV i , σWi , and σV i may result
16
Mathematical Problems in Engineering
in smaller convergence region. However, in practice, we do not suggest the use of high
adaptation gains because such a choice may cause large oscillations in the control outputs
36.
4. Simulation Study
In order to check the effectiveness of the algorithm, consider the following system:
ẋ1 f1 x1 , x2 ,
ẋ2 f2 x2 , u,
4.1
y x1 ,
where x1 x11 x12 T , x2 x21 x22 T ,
T
2
f1 x1 , x2 x12 x22 x11 x12
− x11 x12 2 0.3 sin x12 x21 cos0.1x21 ,
T
−u2
2
2 1 − e
2
.
f2 x2 , u x22 0.8 cosu1 −x21 x22 u2 x21
−
x
x22
x
22
21
1 e−u2 4.2
Let the desired output of the system be yd x11d x12d T 5 0T , and let the initial
conditions be x11 0 x12 0 x21 0 x22 0 0.5 0.25 0.01 0, W1 0 W2 0 011 × 2 , and V1 0 V2 0 05 × 11 .
According to Remark 3.7, we choose the parameters of the controller as follows: k111 6.6, k122 6.6, k211 12.4, k222 12.4, ΓW1 0.01, ΓW2 0.28, σW1 σW2 0.05, ΓV1 0.11,
ΓV2 0.07, σV1 σV2 0.052, η1 η2 0.001, and c1 0.69.
The following two cases will be considered.
Case 1. All the parameters in 4.1 are known.
Case 2. One has the system with uncertainties f10 x1 , x2 0.8f1 x1 , x2 , Δf10 x1 , x2 0.2f1 x1 , x2 , f20 x2 , u 0.8f2 x2 , u, and Δf20 x2 , u 0.2f2 x2 , u.
The simulation results are shown in Figures 1–8. Figures 1–4 show the state responses
in case of the system without uncertainties Case 1, where Figure 1 shows the tracking
response curve of the state x11 , Figure 2 shows the response curve of the state x12 , Figure 3
shows the response curve of the state x21 , and Figure 4 shows the response curve of the
state x22 . Figures 5–8 show the state responses in case of the system with uncertainties Case
2, where Figure 5 shows the tracking response curve of the state x11 , Figure 6 shows the
response curve of the state x12 , Figure 7 shows the response curve of the state x21 , and Figure 8
shows the response curve of the state x22 . The control signals are shown in Figures 9 and 10.
Although no exact model of the plant is available and the initial NN weights are set to
zero, through the NN learning phase and the action of the robust term, it can be seen that the
output tracking performance shown in Figure 5 is quite well and the output tracking error
converges to a quite small set after 4 s in Case 2.
From the figures, one can conclude that the proposed control method presents a good
quality control in both cases.
Mathematical Problems in Engineering
17
5. Conclusions
In this paper, an NN-based sliding mode-like controller is presented for a class of uncertain
block nonaffine systems. The controller is designed using NN control, dynamic feedback,
backstepping design, sliding mode-like technique, and feedback linearization techniques,
which makes the stability analysis simple for block nonaffine systems and guarantees the
stability of the closed-loop system. The sliding mode-like technique can be applied to
other classes of nonlinear systems in strict feedback form. The simulation results show the
effectiveness of the proposed scheme.
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant
no. 60674090.
References
1 A. Isidori, Nonlinear Control Systems, Springer, Berlin, Germany, 3rd edition, 1995.
2 M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic, Nonlinear and Adaptive Control Design, Wiley, New
York, NY, USA, 1995.
3 S. S. Ge, T. H. Lee, and C. J. Harris, Adaptive Neural Network Control of Robotic Manipulators, World
Scientific, London, UK, 1998.
4 F. L. Lewis, S. Jagannathan, and A. Yesildirek, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor and Francis, London, UK, 1999.
5 R. Marino and P. Tomei, Nonlinear Control Design: Geometric, Adaptive, &Robust, Prentice-Hall, Englewood Cliffs, NJ, USA, 1995.
6 A. Rovithakis and M. A. Christodoulou, Adaptive Control with Recurrent High-Order Neural Networks,
Springer, London, UK, 2000.
7 T. Spooner, M. Maggiore, R. Ordóñez, and K. M. Passino, Stable Adaptive Control and Estimation for Nonlinear Systems-Neural and Fuzzy Approximator Techniques, Wiley, New York, NY, USA, 2002.
8 S. S. Ge, C. C. Hang, and T. Zhang, “Nonlinear adaptive control using neural network and its application to cstr systems,” Journal of Process Control, vol. 9, no. 4, pp. 313–323, 1998.
9 J. D. Boskovic, L. Chen, and R. K. Mehra, “Adaptive control design for nonaffine models arising in
flight control,” Journal of Guidance, Control, and Dynamics, vol. 27, no. 2, pp. 209–217, 2004.
10 G. Bartolini and E. Punta, “Reduced-order observer in the sliding-mode control of nonlinear nonaffine
systems,” Institute of Electrical and Electronics Engineers, vol. 55, no. 10, pp. 2368–2373, 2010.
11 S. H. Lane and R. F. Stengel, “Flight control design using nonlinear inverse dynamics,” Automatica,
vol. 24, no. 4, pp. 471–483, 1988.
12 H. Nijmeijer and A. van der Schaft, Nonlinear Dynamical Control Systems, Springer, New York, NY,
USA, 1990.
13 B. J. Yang and A. J. Calise, “Adaptive control of a class of nonaffine systems using neural networks,”
IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1149–1159, 2007.
14 W. Lin, “Feedback stabilization of general nonlinear control systems: a passive system approach,”
Systems & Control Letters, vol. 25, no. 1, pp. 41–52, 1995.
15 W. Lin, “Global asymptotic stabilization of general nonlinear systems with stable free dynamics via
passivity and bounded feedback,” Automatica, vol. 32, no. 6, pp. 915–924, 1996.
16 E. Moulay and W. Perruquetti, “Stabilization of nonaffine systems: a constructive method for polynomial systems,” Institute of Electrical and Electronics Engineers, vol. 50, no. 4, pp. 520–526, 2005.
17 R. Sepulchre, M. Jankovic, and P. V. Kokotovic, Constructive Nonlinear Control, Springer, New York, NY,
USA, 1997.
18 A. S. Shiriaev and A. L. Fradkov, “Stabilization of invariant sets for nonlinear non-affine systems,”
Automatica, vol. 36, no. 11, pp. 1709–1715, 2000.
19 E. D. Sontag, “A “universal” construction of Artstein’s theorem on nonlinear stabilization,” Systems
18
Mathematical Problems in Engineering
& Control Letters, vol. 13, no. 2, pp. 117–123, 1989.
20 Y. H. Chien, W. Y. Wang, Y. G. Leu, and T. T. Lee, “Robust adaptive controller design for a class of
uncertain nonlinear systems using online T-S fuzzy-neural modeling approach,” IEEE Transactions on
Systems, Man, and Cybernetics B, vol. 41, no. 2, pp. 542–552, 2011.
21 S. S. Ge and J. Zhang, “Neural-network control of nonaffine nonlinear system with zero dynamics by
state and output feedback,” IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 900–918, 2003.
22 C. J. Goh, “Model reference control of non-linear systems via implicit function emulation,” International Journal of Control, vol. 60, no. 1, pp. 91–115, 1994.
23 S. Labiod and T. M. Guerra, “Indirect adaptive fuzzy control for a class of nonaffine nonlinear systems
with unknown control directions,” International Journal of Control, Automation, and Systems, vol. 8, no.
4, pp. 903–907, 2010.
24 E. Lavretsky and N. Hovakimyan, “Adaptive dynamic inversion for nonaffine-in-control uncertain
systems via time-scale separation. II,” Journal of Dynamical and Control Systems, vol. 14, no. 1, pp. 33–
41, 2008.
25 J. Teo, J. P. How, and E. Lavretsky, “Proportional-integral controllers for minimum-phase nonaffinein-control systems,” Institute of Electrical and Electronics Engineers, vol. 55, no. 6, pp. 1477–1483, 2010.
26 W. Y. Wang, Y. H. Chien, Y. G. Leu, and T. T. Lee, “Adaptive T-S fuzzy-neural modeling and control
for general MIMO unknown nonaffine nonlinear systems using projection update laws,” Automatica,
vol. 46, no. 5, pp. 852–863, 2010.
27 W. Y. Wang, Y. H. Chien, and T. T. Lee, “Observer-based T-S fuzzy control for a class of general nonaffine nonlinear systems using generalized projection-update laws,” IEEE Transactions on Fuzzy Systems,
vol. 19, no. 3, pp. 493–504, 2011.
28 B. J. Yang and A. J. Calise, “Adaptive control of a class of multivariable nonaffine systems,” in Proceedings of the 46th IEEE Conference on Decision and Control, pp. 4809–4814, New Orleans, LA, USA,
December 2007.
29 A. J. Calise, M. Sharma, and S. Lee, “Adaptive autopilot design for guided munitions,” Journal of Guidance, Control, and Dynamics, vol. 23, no. 5, pp. 837–843, 2000.
30 H. B. Du and X. C. Chen, “NN-based output feedback adaptive variable structure control for a class of
non-affine nonlinear systems: a nonseparation principle design,” Neurocomputing, vol. 72, no. 7–9, pp.
2009–2016, 2009.
31 Y. Q. Jin, Research on the robust adaptive control of block control uncertain nonliear systems, Ph.D. thesis,
Naval Aeronautical and Astronautical University, 2006.
32 B. S. Kim and A. J. Calise, “Nonlinear flight control using neural networks,” Journal of Guidance, Control, and Dynamics, vol. 20, no. 1, pp. 26–33, 1997.
33 Y. G. Leu, W. Y. Wang, and T. T. Lee, “Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems,” IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 853–861, 2005.
34 T. Zhang, S. S. Ge, and C. C. Hang, “Design and performance analysis of a direct adaptive controller
for nonlinear systems,” Automatica, vol. 35, no. 11, pp. 1809–1817, 1999.
35 J. Q. Han and W. Wang, “Nonlinear tracking differentiator,” System Science and Mathematics, vol. 14,
no. 2, pp. 177–183, 1994.
36 S. S. Ge, C. C. Hang, T. H. Lee, and T. Zhang, Stable Adaptive Neural Network Control, Kluwer Academic
Publishers, Norwell, Mass, USA, 2001.
Advances in
Operations Research
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Advances in
Decision Sciences
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Mathematical Problems
in Engineering
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Journal of
Algebra
Hindawi Publishing Corporation
http://www.hindawi.com
Probability and Statistics
Volume 2014
The Scientific
World Journal
Hindawi Publishing Corporation
http://www.hindawi.com
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
International Journal of
Differential Equations
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Volume 2014
Submit your manuscripts at
http://www.hindawi.com
International Journal of
Advances in
Combinatorics
Hindawi Publishing Corporation
http://www.hindawi.com
Mathematical Physics
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Journal of
Complex Analysis
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
International
Journal of
Mathematics and
Mathematical
Sciences
Journal of
Hindawi Publishing Corporation
http://www.hindawi.com
Stochastic Analysis
Abstract and
Applied Analysis
Hindawi Publishing Corporation
http://www.hindawi.com
Hindawi Publishing Corporation
http://www.hindawi.com
International Journal of
Mathematics
Volume 2014
Volume 2014
Discrete Dynamics in
Nature and Society
Volume 2014
Volume 2014
Journal of
Journal of
Discrete Mathematics
Journal of
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Applied Mathematics
Journal of
Function Spaces
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Optimization
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014
Hindawi Publishing Corporation
http://www.hindawi.com
Volume 2014