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Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 324738, 13 pages

http://dx.doi.org/10.1155/2013/324738

## Delay-Dependent Stability Analysis of Uncertain Fuzzy Systems with State and Input Delays under Imperfect Premise Matching

^{1}Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China^{2}Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, 00076 Aalto, Finland

Received 1 December 2012; Accepted 4 February 2013

Academic Editor: Peng Shi

Copyright © 2013 Zejian Zhang 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

This paper discusses the stability and stabilization problem for uncertain T-S fuzzy systems with time-varying state and input delays. A new augmented Lyapunov function with an additional triple-integral term and different membership functions of the fuzzy models and fuzzy controllers are introduced to derive the stability criterion, which is less conservative than the existing results. Moreover, a new flexibility design method is also provided. Some numerical examples are given to demonstrate the effectiveness and less conservativeness of the proposed method.

#### 1. Introduction

Since the Takagi-Sugeno (T-S) [1] fuzzy systems were firstly proposed in 1985, their stability analysis has received considerable research attention [2, 3]. However, most of the existing results are only for the T-S fuzzy systems free of time delay. Actually, time delay often occurs in many practical systems, such as [4, 5]. It has been shown that the existence of time delay usually becomes the source of instability and deteriorated performance of systems. Therefore, study of the time delay is important in both theory and practice [6]. The first stability analysis work on the T-S fuzzy systems with time delay is done in [7, 8] by using the Lyapunov-Razumikhin functional approach. If some uncertainties exist in a T-S fuzzy time delay system, they may also significantly affect the system performances and even cause unstable system outputs. Therefore, the issue of the stability for the uncertain T-S fuzzy time delay systems has been widely explored [9–11]. In the literature, two basic approaches have been utilized, that is, delay-independent approach [12] and delay-dependent approach. The latter makes use of the information on the length of the delays, and it is less conservative than the former one. A lot of stability analysis results have been reported based on the delay-dependent approach [13, 14].

During the recent years, some research work for different types of the delays of the T-S fuzzy systems has been published, such as constant delay [15, 16], bounded time delay [17], time varying delay [18], and interval time varying delay [19, 20]. However, all these results are only for the T-S fuzzy systems with state delays. Thus, they may be invalid when applied to the systems with input delays. As we know, input delays extensively exist in industrial processes and can cause instability or serious performance deterioration. In fact, in modern industrial systems, sensors, controllers, and plants are often connected together, and the sampled data and controller signals are transmitted through networks. In view of this, the input delays should be taken into consideration for robust controller design. Intensive results on the stabilization for the T-S fuzzy systems with state and input delays are reported in [21–28]. For example, the work in [21] is based on the linear systems with input delays. In [22, 24], the authors only consider the fuzzy systems with constant input delays, and their results are conservative. In [23], the authors study the fuzzy systems with both the state and input delays. Unfortunately, the results are obtained without any uncertainty, and the state delay is assumed to be equal to the input delay. In [26], the uncertainty has been considered in the analysis, but the state delay is also assumed to be the same as the input delay. Some interesting results for the uncertain fuzzy systems with state and input delays have been obtained in [25, 27, 28], most of which introduce some Lyapunov-Krasovskii functions containing integral terms, for example, , and double-integral terms, . Several triple-integral terms are used in the Lyapunov function [29] to yield less conservative results for the fuzzy systems with state delay. A large portion of robust controller design topics have been investigated on the basis of the Parallel Distribution Compensation (PDC) design technique, where the fuzzy controller shares the same premise membership functions as those of the T-S fuzzy time delay model [7–11, 15–20, 22–28]. As a matter of fact, if the membership functions in the premise of the fuzzy rules of the fuzzy controllers are allowed to be designed arbitrarily, we can even achieve better design flexibility. For instance, a fuzzy controller not sharing the same premise rules as those of the T-S fuzzy model referred to as imperfect premise matching is employed to control the nonlinear plants [30, 31], and [12] extends the available results to the T-S fuzzy time-delay systems with only the state delay.

In this paper, an augmented Lyapunov-Krasovskii function that contains a triple-integral term is introduced to investigate the stability and stabilization problem for uncertain T-S fuzzy systems with the state and input delays under the imperfect premise matching, in which the fuzzy time delay model and fuzzy controller are with different premise. Some less conservative delay-dependent stability and robust stability conditions are obtained by two integral inequalities and a parameterized model transformation method. Moreover, different from the general PDC design technique, a new design approach of robust stable controllers is proposed. Two simulation examples are further given to illustrate that the proposed design methods are less conservative and more flexible.

#### 2. System Description and Preliminaries

Let be the number of the fuzzy rules describing the time delay nonlinear plant. The th rule can be represented as follows.

If is and … and is where is a fuzzy term of rule corresponding to the function . is the system state vector, and is the input vector. The matrices , and , , are of appropriate dimensions. The initial condition is a continuous vector-valued function. The delays and are time varying, and satisfy where are constants representing the upper bound of the delay. is a positive constant. , , and denote the uncertainties in the system, and they are the form of where , ,, and are known constant matrices of appropriate dimensions, and is unknown matrix function satisfying . is an appropriately dimensioned identity matrix. Hence, the overall fuzzy model can be formulized as follows: where is the normalized grade of membership function that is a nonlinear function of . is the grade of the membership corresponding to the fuzzy term of .

Different from the popular PDC design technique, the following fuzzy control law under imperfect premise matching is employed to deal with the problem of stabilization via state feedback. Under the imperfect premise matching, the th rule of the fuzzy controller is defined as follows.

If is and … and is where denotes the fuzzy set. is a positive integer, and is the feedback gain of rule . The state feedback fuzzy control law is represented by where is the normalized grade of membership function that is a nonlinear function of . is the grade of the membership corresponding to the fuzzy term of .

As the time varying delay is included in the control input, we have where

Lemma 1 (see [32] (Schur complement)). *Given constant matrices , and , where and , there is , if and only if
*

Lemma 2 (see [33]). *Let ,,, and satisfies ; the following inequality holds
**
if and only if the following inequality holds for any smaller :
*

Lemma 3 (see [34]). *For any constant matrix and a scalar such that the following integrations are well defined, we have
*

#### 3. Main Results

In this section, some new delay-dependent criteria for the T-S fuzzy systems with state and input delays are proposed by introducing a novel Lyapunov function with an additional triple-integral term under the imperfect premise matching. Moreover, a robust stabilization criterion is also investigated.

##### 3.1. Stability of Nominal Fuzzy Systems

Firstly, we consider the control design of a state feedback control law under the imperfect premise matching that stabilizes the following nominal fuzzy time varying delay system:

Theorem 4. *Given scalars , , and , the closed-loop system (15) is asymptotically stable for any via the imperfect premise matching controller design technique, if the membership functions of the fuzzy model and fuzzy controller satisfy for all , and , where , and there exist matrices , , , , , , , and
**
and real matrices , , and such that (30)–(32) hold. In addition, the stabilizing control law is given by
*

*Proof. *Choose a fuzzy weighting-dependent Lyapunov-Krasovskii functional candidate as
where
and and are the positive-definite matrices to be determined.

The derivatives of along the trajectories of system (15) are as follows:
From (15), the following equation can be obtained for any matrices ,
By Lemma 3, we have
Using the above inequalities, and with the zero quantities (21), we can obtain
where

From (25), it is obvious that if
.

From (26), we can discover that the feedback gains are predefined. The following proof presents the controller design method. We first pre- and postmultiply both the and transpose to both sides of (25) and pre- and postmultiply the transpose to both sides of , , , and and the and transpose to both sides of , and . Let , and denote new variables , , , , , ,,, ,, , , , , , , , , , , and . With *,* we next get
where
Here, “” denotes the transpose elements in the symmetric positions.

If (27) holds, then . Consider , where , are arbitrary matrices. These terms are introduced to (27) to alleviate the conservativeness. From (27), we also have
with for all , and . Let
for all.

Since , the fuzzy control system with the time varying state and input delays (15) is asymptotically stable with the state feedback control law (17).

##### 3.2. Robust Stability of Uncertain Fuzzy Systems

We also examine the design of a robust stable controller for the uncertain system (4) under the imperfect premise matching. Consider the following uncertain fuzzy time varying delay control system: Based on the above results of Theorem 4, a robust stabilization criterion for the T-S fuzzy systems with time varying state and input delays is investigated. The following result can be obtained.

Theorem 5. *Given scalars , , and , the uncertain fuzzy control systems with the time varying state and input delays (33) is robustly stable for any via the imperfect premise matching controller design technique, if the membership functions of the fuzzy model and fuzzy controller satisfy for all , , and , where , and there exist common matrices, , , and , , ,, and some matrices , and scalars , , satisfy the following LMIs:
**
where is defined as in (44). The state feedback gains can be constructed as . *

*Proof. *If , and in are replaced with , , and in (28) of Theorem 4, respectively, (27) for system (33) can be rewritten as
where
If , (37) holds. According to Lemma 2, it is straightforward to know that is true, if for each , , there exists scalars , , and such that the following inequality holds:
Based on Schur complement, (40) is equivalent to the following inequality:
where
and are the same as in Theorem 4. Therefore, (41) holds, if and only if the following inequality holds:

To alleviate the conservativeness of our robust stability analysis, similar to the analysis approach of Theorem 4, we introduce to (43), and the following proof is the same as in Theorem 4. We can obtain the conditions and (34)–(36). Thus, the fuzzy control system with the time varying state and input delays (34) is robust stable on the basis of the control law under these conditions, and (34)–(36) hold.

*Remark 6. *If we set , , the closed-loop system in this paper has the same structure as that of paper [26]. On the other hand, let and; we can obtain the system in [16]. Therefore, the system studied in this paper is more general. Additionally, the information of the membership functions of the fuzzy time delay models and controllers are all considered in the stability analysis. If the fuzzy models and fuzzy controllers share the same fuzzy membership functions, that is, , the stability analysis can be referred to [25, 27, 28]. In other words, the problem in [25, 27, 28] is actually a special case of the one investigated in this paper.

*Remark 7. *The augmented Lyapunov function with an additional triple-integral term is introduced in analyzing the stability problem for the T-S fuzzy systems with time varying state and input delays. In addition, two integral inequalities are used to derive Theorem 4, and less free-weighting matrices are introduced, which lead to get less conservative results. It can be observed that some existing results are the special cases of this paper. For example, the method in [25] is from (18) with , and in Theorem 4. The results in [22] are based on system (33) with and (18) with , and.

*Remark 8. *Different from the general PDC technique, the design method under the imperfect premise matching is much more flexible, because the membership functions of the fuzzy controllers do not need to be chosen the same as those of the fuzzy time delay models. Instead, they can be designed arbitrarily. Thus, the design flexibility is significantly enhanced. On the other hand, some simple membership functions of the fuzzy controllers might be employed, which can reduce the implementation cost.

*Remark 9. *In [7–11, 15–20], the authors have considered the robust control for uncertain T-S fuzzy systems with state delays. Some delay-dependent conditions and fuzzy controllers are obtained with a traditional Lyapunov-Krasovskii functional method and PDC method, which all are the special cases of Theorem 5 in this paper.

#### 4. Numerical Examples

In this section, two numerical examples are given to illustrate the conservativeness and effectiveness of the proposed methods. The first example compares our techniques with the existing ones in the literature for stability analysis, which shows that Theorem 4 in this paper is less conservative than the other results. The second example is used to illustrate the advantage of the robust stability conditions of Theorem 5 and demonstrate how to design robust fuzzy controllers by using our approach.

*Example 10. *Consider the following fuzzy system with state and input delays:
The fuzzy membership functions are selected as [31]
where
If we set in system (44), we can obtain the system in [26], which implies that our system is more general. Employing the LMIs in [26] and those in Theorem 4 yields the maximum state and input delays , and , which guarantee the stability of system (44) and the feedback gains for, and , when ,, , and as given in Table 1, which clearly shows the superiority of the results derived in this paper over those obtained from [26].

*Example 11. *Consider the well-known truck-trailer system, which can be described by the following T-S fuzzy system:
where , , , , (), and the fuzzy membership functions are selected the same as in Example 10.(1)Let , system (47) is the same as the fuzzy system of [22], which implies that our system is more general. Table 2 gives the maximum input delay value of , for which the stabilization is guaranteed by Theorem 5 as well as the state feedback gains, when,,,,,,, and . It is clearly visible that the results derived in this paper are better than those from [22, 28].(2)In the case of , the state and input delays of system (47) are all exist. The approach proposed in [22] can not be used to get the maximum of state delay , as the system in [22] only contains the input delay. However, the method in [28] and Theorem 5 can be used. Here, let , ,, ,,,, ,, and , and Table 3 provides the maximum upper bounds of and and the state feedback gains for which the robust stabilization is guaranteed by Theorem 5.

By Theorem 5, we can conclude that the uncertain fuzzy model (47) is robust stable based on the following fuzzy control law: