- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents

The Scientific World Journal

Volume 2013 (2013), Article ID 963986, 8 pages

http://dx.doi.org/10.1155/2013/963986

## Robust Adaptive Control for a Class of Uncertain Nonlinear Systems with Time-Varying Delay

^{1}Computer and Information Engineering College, Guangxi Teachers Education University, Nanning 530023, China^{2}Yantai Nanshan Vocational Technology School, Yantai, China^{3}School of Mathematical Sciences, Guangxi Teachers Education University, Nanning 530023, China^{4}Department of Basic Courses, Haikou College of Economics, Haikou, China

Received 27 February 2013; Accepted 23 May 2013

Academic Editors: Y.-M. Cheung and Y. Wang

Copyright © 2013 Ruliang Wang 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.

#### Abstract

We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories. The simulation results demonstrate the effectiveness of the proposed control scheme.

#### 1. Introduction

In the practical control process, control system is usually required to meet the stability and the corresponding performance index, which affects the system stability factors mainly including the uncertainties and time delays. On the study of the uncertain time-delay many scholars have achieved valuable fruits [1, 2]. Paper [3] has analyzed and designed the optimal feedback controller by the LMI method. In recent decades, the delay nonlinear systems with neural network research have received extensive attention [4–20]. Paper [4] has solved the problem of chaotic synchronization phenomenon by the neural network method. In [5–11], the study of the nonlinear continuous system and discrete nonlinear system is based on adaptive neural network control. The tracking and stabilization problem of nonlinear systems has been studied by neural network backstepping method [12, 13]. In [14], neural network control has been applied to a piece of triangle structure of multiple-input multiple-output nonlinear time-delay system, in which a dynamic system neural network is used mainly for unknown function approximation and separation. In multiple input and multiple output nonlinear system, [15] presents a new adaptive neural network controller design method but does not consider with external disturbance and time-varying delay. In [18], the problem of the adaptive neural networks control for a class of nonlinear state-delay systems with unknown virtual control coefficients is considered. In [19], a control scheme combined with backstepping, radial basis function (RBF) neural networks and adaptive control are proposed for the stabilization of nonlinear system with input and state delay.

This paper mainly aims at studying the simultaneous presence of uncertainties and time-varying delay MIMO nonlinear system. By defining the new quadratic Lyapunov-Krasovskii functionals, it has analyzed and designed the adaptive neural network controller by neural network approximation method in [15, 16].

#### 2. Description of the Problem

Let us consider the following block-triangular structure with the disturbance of nonlinear MIMO systems with time-varying delays: where are the state variable for the differential equations of the th subsystem; , where are the state vector of the th subsystem; , where are unknown time-varying delay of the states, and , , , the output ; are input vector of the th subsystem; , , and are unknown smooth nonlinear function. is the disturbance input and . Let , with ; assume is smooth and bounded.

We make the following assumptions for the system (1).

*Assumption 1. *The desired trajectories , have the th derivation and the derivation is continuous and bounded.

*Assumption 2. *We use to represent some given function. There exist constant and unknown smooth functions , such that . Without loss of generality, we further assume that .

Lemma 3 (see [16]). *There exists smooth positive function with for all continuous functions with , where , such that .*

Lemma 4 (see [14]). *On any normal number and random variable have . *

In this paper, the following radial basis function neural network is used to approximate unknown continuous function (in [13] once had been put forward): where the input vector ; is the weight vector; the number of neural network node and , where is the center of the receptive field, and is the width of the Gaussian function.

#### 3. Adaptive Neural Network Controller Design

In this section, we will introduce a novel adaptive NN control design procedure. There are design steps in the design procedure for the th subsystem. In each step, the unknown nonlinear function will be approximated by a radial neural network approximation function. Define an unknown constant as where the constant is defined as in Assumption 2; function and vector will be specified in each step. Furthermore, for and , choose the virtual control laws as follows: where and are design parameters, represent the estimation of the unknown constant , and is the basis function vector, and define the variables as follows: for . Choose the adaptive laws as follows: where and are design parameters.

*Step* . For the first differential equation of the th subsystem, choose the Lyapunov function candidate
where . Taking the time derivative of , we obtain
With Lemma 3, existence of positive function , such that
Then, we have
Substituting (10) into (8) yields
To overcome the time-varying delay terms of (11), consider the following Lyapunov-Krasovskii functional:
where
Take the time derivative of :
from (11) and (14), one has
where
and is a positive constant.

From Lemma 4, the function is defined at and can be approximated by a neural network. Therefor the function will be approximated by the NN , such that, for given , where is the approximation error. Furthermore, a straightforward calculation shows that In additions, from (6), we obtain that for any initial conditions , for all . Therefor Substituting (18)–(20) into (15) yields that

*Step* . Define the Lyapunov-Krasovskii functional as
differentiating yields
From (10), we have
can be expressed as
Similar to (24), we can get
Substituting (24)–(26) into (23) yields that
To overcome the delay terms in (27), let us consider the following Lyapunov-Krasovskii functional:
where
Differentiating yields
where
Then, combining (27) and (30) results in
where
The NN is used to approximate such that for given we have
where represent the approximation error. Similar to (18) and (20), we have

*Step* . In the final step of the th subsystem to construct the actual control law , let us consider the following Lyapunov-Krasovskii function:
where
and . Similar to (32) we get
where can be defined by (33) with .

We use the NN to approximate such that, for given , we have where express the approximation error.

Choose the control law as Similar to (21) we have Let . Combining (21), (35), and (41) gives that The control law design is thus completed.

#### 4. Stability Analysis

Now, the main result in this paper can be presented as follows.

Theorem 5. *Consider the nonlinear time-delay system (1) with the NN adaption law (6) and the control law (40) satisfying Assumptions 1–2. All the closed-loop trajectories can guarantee boundedness if the unknown function can be approximated by neural network and the approximating error is boundedness.*

*Proof. *Define functions , such that
Let , where is the number of neural network weights.

From (6), we can get
By choosing function as
(43) holds.

In a similar way, we can get
Now, choose the Lyapunov function as .

Combining (42)–(46) gives that
where
is a constant. Thus, by (47) the boundedness follows immediately from the same line used in the proof in [9–11].

#### 5. Simulation Examples

In this section, we will give one example to demonstrate the effectiveness of the proposed method in this paper. Let us consider the following example: where .

And the time delays are chosen as given the reference output signals as . The control law is given by (40). The NN adaptation law is given by (6). Choose the design parameters Take the external disturbance as

The simulation is run under the initial conditions , and . The result of control scheme is displayed in Figures 1–5. Figures 1 and 2 demonstrate the outputs of system and the reference signals. The responses of state variables and are shown in Figure 3. The control input signals and are illustrated in Figures 4 and 5which depict the boundedness of adaptive parameters and .

#### 6. Conclusion

For a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form, an adaptive neural control design is presented. Although there are some fluctuations of the systems and control output under the influence of interference, the required performance can be achieved in a short period of time by using the controller designed in this paper and guarantees the boundedness of all the signals in the closed-loop system. It is further extended on the bases in [14, 15], which makes it suitable for wider range of applications. The effectiveness of the proposed approach is provided by a simulation example.

#### Acknowledgments

This work was jointly supported by the Natural Science Foundation of China (60864001) and Guangxi Natural Science Foundation (2011GXNSFA018161).

#### References

- L. Lv and Z.-S. Li, “Design of robust ${H}_{\infty}$ controller for uncertain systems with time—varying delay—LMI approach,”
*Computing Technology and Automation*, vol. 25, no. 1, pp. 4–7, 2006. - O. M. Kwon and J. H. Park, “On improved delay-dependent robust control for uncertain time-delay systems,”
*IEEE Transactions on Automatic Control*, vol. 49, no. 11, pp. 1991–1995, 2004. View at Publisher · View at Google Scholar · View at Scopus - R. Yang and J. Bao, “Robust ${H}_{\infty}$ optimal control for uncertain time-varying delay systems,”
*Journal of Nanjing University of Information Science: Technology*, vol. 2, no. 1, pp. 62–67, 2010. - H. Zhang, Y. Xie, Z. Wang, and C. Zheng, “Adaptive synchronization between two different chaotic neural networks with time delay,”
*IEEE Transactions on Neural Networks*, vol. 18, no. 6, pp. 1841–1845, 2007. View at Publisher · View at Google Scholar · View at Scopus - L. Chen and K. S. Narendra, “Nonlinear adaptive control using neural networks and multiple models,”
*Automatica*, vol. 37, no. 8, pp. 1245–1255, 2001. View at Publisher · View at Google Scholar · View at Scopus - 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. View at Publisher · View at Google Scholar · View at Scopus - Q. Zhu and L. Guo, “Stable adaptive neurocontrol for nonlinear discrete-time systems,”
*IEEE Transactions on Neural Networks*, vol. 15, no. 3, pp. 653–662, 2004. View at Publisher · View at Google Scholar · View at Scopus - R. Wang and J. Li, “Adaptive neural control for a class of perturbed time-delay nonlinear systems,” in
*Proceedings of the 7th International Conference on Computational Intelligence and Security (CIS '11)*, pp. 358–361, December 2011. View at Publisher · View at Google Scholar · View at Scopus - R. Wang, D. Gao, and Y. Li, “Robust adaptive sliding mode control design for a class of uncertain neutral systems with distributed delays,” in
*Proceedings of the 7th International Conference on Computational Intelligence and Security (CIS '11)*, pp. 1539–1541, December 2011. View at Publisher · View at Google Scholar · View at Scopus - R. Wang and H. Jiang, “Observer-based adaptive neural network robust control of nonlinear time-delay systems with unmodeled dynamics,” in
*Proceedings of the International Conference on Computational Intelligence and Security (CIS '10)*, pp. 506–510, December 2010. View at Publisher · View at Google Scholar · View at Scopus - R. Wang, J. Li, J. J. Zhang, and X. R. Gao, “Adaptive neural control design for a class of perturbed nonlinear time-varying delay and input delay systems,” in
*Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '11)*, pp. 1168–1173, July 2011. View at Publisher · View at Google Scholar · View at Scopus - T. Zhang, S. S. Ge, and C. C. Hang, “Adaptive neural network control for strict-feedback nonlinear systems using backstepping design,”
*Automatica*, vol. 36, no. 12, pp. 1835–1846, 2000. View at Publisher · View at Google Scholar · View at Scopus - S. Zhou, J. Lam, G. Feng, and D. W. C. Ho, “Exponential
*ε*-regulation for multi-input nonlinear systems using neural networks,”*IEEE Transactions on Neural Networks*, vol. 16, no. 6, pp. 1710–1714, 2005. View at Publisher · View at Google Scholar · View at Scopus - S. S. Ge and K. P. Tee, “Approximation-based control of nonlinear MIMO time-delay systems,”
*Automatica*, vol. 43, no. 1, pp. 31–43, 2007. View at Publisher · View at Google Scholar · View at Scopus - B. Chen, X. Liu, K. Liu, and C. Lin, “Novel adaptive neural control design for nonlinear MIMO time-delay systems,”
*Automatica*, vol. 45, no. 6, pp. 1554–1560, 2009. View at Publisher · View at Google Scholar · View at Scopus - W. Lin and C. Qian, “Adaptive control of nonlinearly parameterized systems: the smooth feedback case,”
*IEEE Transactions on Automatic Control*, vol. 47, no. 8, pp. 1249–1266, 2002. View at Publisher · View at Google Scholar · View at Scopus - R. M. Sanner and J.-J. E. Slotine, “Gaussian networks for direct adaptive control,”
*IEEE Transactions on Neural Networks*, vol. 3, no. 6, pp. 837–863, 1992. View at Publisher · View at Google Scholar · View at Scopus - S. S. Ge, F. Hong, and T. H. Lee, “Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients,”
*IEEE Transactions on Systems, Man, and Cybernetics, Part B*, vol. 34, no. 1, pp. 499–516, 2004. View at Publisher · View at Google Scholar · View at Scopus - Q. Zhu, S. M. Fei, T. P. Zhang, and T. Li, “Adaptive RBF neural-networks control for a class of time-delay nonlinear systems,”
*Neurocomputing*, vol. 71, no. 16–18, pp. 3617–3624, 2008. View at Publisher · View at Google Scholar - S. Xu, J. Lam, and Y. Zou, “New results on delay-dependent robust ${H}_{\infty}$ control for systems with time-varying delays,”
*Automatica*, vol. 42, no. 2, pp. 343–348, 2006. View at Publisher · View at Google Scholar · View at Scopus