## Selected Papers from the 7th International Conference on Computational Intelligence and Security (CIS'2011)

View this Special IssueResearch Article | Open Access

Ruliang Wang, Jie Li, "Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems", *Mathematical Problems in Engineering*, vol. 2012, Article ID 852161, 16 pages, 2012. https://doi.org/10.1155/2012/852161

# Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

**Academic Editor:**Yiu-ming Cheung

#### Abstract

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

#### 1. Introduction

The study of the time-delay systems has been one of the most active research topics in recent years [1–15]. The time-delay systems can be divided into four types: systems with input delay [1–5], systems with state delay [6–9, 16–18], systems with both input and state delays, and systems with both input and output delays [19]. The effect of time delay on stability and asymptotic performance has been investigated in [20]. In [21], Lyapunov-Krasovskii functionals were used with backstepping to obtain a robust controller for a class of single-input single-output (SISO) nonlinear time-delay systems with known bounds on the functions of delayed states, but it was commented that results could not be constructively obtained in [22]. In [23], the problem of the adaptive neural-networks control for a class of nonlinear state-delay systems with unknown virtual control coefficients is considered. In [24], An adaptive control scheme combined with radius basis function (RBF) neural networks, backstepping, and adaptive control is proposed for the output tracking control problem of a class of MIMO nonlinear system with input delay and disturbances. Neural networks are employed to estimate the unknown continuous functions; the control scheme ensures that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB). In [11] A control scheme combined with backstepping, radius basis function (RBF) neural networks, and adaptive control is proposed for the stabilization of nonlinear system with input and state delay.

In this paper, we present an adaptive neural controller design procedure for a class of output time-delay nonlinear systems with perturbed, based on backstepping, adaptive control, and neural networks. RBF neural network is employed to the unknown continuous function. A numerical example is provided to show the effectiveness of the control scheme.

#### 2. Problem Formulation and Preliminaries

Consider the nonlinear time-delay system is described as follows: where is state, is control and is output vectors, respectively. is a time-varying disturbance. , , , are unknown continuous functions.

*Assumption 2.1. *The unknown function satisfies , where is a known constant.

*Assumption 2.2. *The time-varying disturbance satisfies , , where is a known constant.

Lemma 2.3. *, where , is an unknown constant.*

#### 3. RBF NN Approximation

In this paper, for a given and any continuous function defined on , there is a perfect RBF neural network, which satisfies where is the weight vector of the neural networks, is the number of the NN nodes, is the input vector, is defined by According to the discussion in [21, 22], denote the best weight vector as follows: which is unknown and needs to be estimated in control design. Let be the estimate of , and define .

#### 4. Main Result

In this section, we will consider system (2.1).

(I) when ,

Let us define error variables assistant functions and the virtual control , respectively, as follows: Define the following sets: where is a small constant. Define assistant functions as Define the virtual control as where

Theorem 4.1. * System (2.1) with both input delay and state delay satisfies Assumptions 2.1 and 2.2. The virtual control can be selected as (4.4). If the control law and the adaptive law are selected as follows:
**
then the closed-loop system is semi-globally uniformly ultimately bounded.*

*Proof. *Define the Lyapunov-Kresovskii functional as
*Step 1*. For the first differential equation of the the first subsystem, by (4.1), (4.3), we can get
By
differentiating (4.10) and using (4.12), the inequality below can be obtained easily.
(1) If , then . Thus, substituting (4.4) and (4.7) into (4.14) results in
where , . .

If there is no item in (4.15), then
where . Thus is bounded.

(2) If , then , is bounded. By the integral median theorem, we can obtain
By Assumption 2.1 and (4.9), it can be concluded that is bounded.

Differentiating ,
where
is the number of neurons of the neural networks. Choose the parameter so that . Therefore
where . Thus is bounded. Because are all bounded, is bounded when .*Step **.* For the th subsystem, by utilizing (4.1)(4.3), we have
Differentiating (4.10) along track (4.21), we have

(1) If , then . Thus, substituting (4.4) and (4.7) into (4.22) results in
where , . .

If there is no item in (4.23), then
where . Thus is bounded.

(2) If , similar to step 1, we have is bounded.*Step *. This is the last step for the th subsystem, similarly to the th subsystem, if , then . Thus we have
where , . .

By (4.25), it is easy to have
where . Thus is bounded.

(1) If , similar to step 1, we have is bounded.

The is bounded when . In :
where , .

Then
where . Thus is bounded.

(II) When .

Let us define error variables assistant functions and the virtual control , respectively, as follows: Define the following sets: where is a small constant. Define assistant functions as Define the virtual control as where

Theorem 4.2. *System (2.1) with both input delay and state delay satisfies Assumptions 2.1 and 2.2. The virtual control can be selected as (4.32). If the control law and the adaptive law are selected as follow:
**
then the closed-loop system is semi-globally uniformly ultimately bounded.*

*Proof. *Define the Lyapunov-Kresovskii functional as
*Step 1*. For the first differential equation of the first subsystem, by (4.29), (4.31), We can get
By
differentiating (4.39) and using (4.41), the inequality below can be obtained easily.

(1) If , then . Thus, substituting (4.32) and (4.36) into (4.43) results in
where , . .

If there is no item in (4.44), then
where . Thus is bounded.

(2) If , then , is bounded. By the integral median theorem, we can obtain
By Assumption 2.1 and (4.38), it can be concluded that is bounded.

Differentiating ,
where
is the number of neurons of the neural networks. Choose the parameter so that . Therefore
where . Thus is bounded. Because are all bounded, is bounded when .*Step *. For the th subsystem, by utilizing (4.29), (4.31), we have
Differentiating (4.39) along track (4.50),we have

(1) If , then . Thus, substituting (4.32) and (4.36) into (4.51) results in
where , . .

If there is no item in (4.52), then
where . Thus is bounded.

(2) If , similar to step 1, we have is bounded.*Step *. This is the last step for the th subsystem, similarly to the th subsystem, If , then . Thus we have
where . .

By (4.54), it is easy to have
where . Thus is bounded.

(II) If , similar to step 1, we have is bounded

The is bounded. When . In : where .

Then where . Thus is bounded.

*Simulation Example*

Consider the nonlinear system with input and state delays as follows:
Define virtual control as
where , , , , , , , , .
The result of control scheme is in Figures 1 and 2.

#### 5. Conclusion

For a class of outputs time-delay nonlinear systems with perturbed or not, a control scheme combined with adaptive control, backstepping, and neural network is proposed. The radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. It is shown that the proposed method guarantees the semi-globally uniformly ultimately boundedness of all signals in the adaptive closed-loop systems. Simulation results are provided to illustrate the performance of the proposed approach.

#### Acknowledgments

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

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#### Copyright

Copyright © 2012 Ruliang Wang and Jie Li. 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.