Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 836814, 18 pages

doi:10.1155/2012/836814

Review Article

Robust Stabilization for Continuous Takagi-Sugeno Fuzzy System Based on Observer Design

Yassine Manai and Mohamed Benrejeb

LA.R.A. Automatic, National Engineering School of Tunis, University of Tunis-ElManar, BP 37 Belvedere, 1002 Tunis, Tunisia

Received 30 June 2012; Revised 19 September 2012; Accepted 19 September 2012

Academic Editor: Mohammed Chadli

Copyright © 2012 Yassine Manai and Mohamed Benrejeb. 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 investigates the influence of a new parallel distributed controller (PDC) on the stabilization region of continuous Takagi-Sugeno (T-S) fuzzy models. Using a nonquadratic Lyapunov function, a new sufficient stabilization criterion is established in terms of linear matrix inequality. The criterion examines the derivative membership function; an approach to determine state variables is given based on observer design. In addition, a stabilization condition for uncertain system is given. Finally, numeric simulation is given to validate the developed approach.

1. Introduction

Fuzzy control systems have experienced a big growth of industrial applications in the recent decades, because of their reliability and effectiveness. Many researches have investigated the Takagi-Sugeno models [1–3] during the last decades. Two classes of Lyapunov functions are used to analyze these systems: quadratic Lyapunov functions and nonquadratic Lyapunov ones which are less conservative than the first class. Many researches have investigated with nonquadratic Lyapunov functions [4–10].

As the information about the time derivatives of membership function is considered by the PDC fuzzy controller, it allows the introduction of slack matrices to facilitate the stability analysis. The relationship between the membership function of the fuzzy model and the fuzzy controllers is used to introduce some slack matrix variables. The boundary information of the membership functions is brought to the stability condition and thus offers some relaxed stability conditions [6]. In order to determine the state variables many approaches of observer design are given [9–11].

In this paper, new stabilization conditions for Takagi-Sugeno uncertain fuzzy models based on the use of fuzzy Lyapunov function are presented. This criterion is expressed in terms of linear matrix inequalities (LMIs) which can be efficiently solved by using various convex optimization algorithms [12, 13]. The presented method is less conservative than existing results.

The organization of the paper is as follows. In Section 2, we present the system description and problem formulation and we give some preliminaries which are needed to derive results. Section 3 will be concerned with stabilization analysis for continuous T-S fuzzy systems by the use of new PDC controller based on derivative membership functions. An observer approach design is derived to estimate state variables. In Section 4, a new stabilization condition for uncertain system is given. Next, a new robust PDC controller design approach is presented. Illustrative examples are given in Section 5 for a comparison of previous results to demonstrate the advantage of the proposed method. Finally Section 6 makes the conclusion.

Notation. Throughout this paper, a real symmetric matrix denotes being a positive definite matrix. The superscript “T” is used for the transpose of a matrix.

2. System Description and Preliminaries

Consider an uncertain T-S fuzzy continuous model for a nonlinear system as follows:

where is the fuzzy set and is the number of model rules, is the state vector, is the input vector, , are constant real matrices, and are known premise variables. , and are time-varying matrices representing parametric uncertainties in the plant model. These uncertainties are admissibly norm-bound and structured.

The final outputs of the fuzzy systems are:

where

The term is the grade of membership of in

for all .

We have the following property:

This study investigates the PDC controller influence on the closed-loop stability region and gives robustness analysis of uncertain Takagi-Sugeno fuzzy system. Thus, we consider a PDC fuzzy controller which examines the derivative membership function and it is given by

The fuzzy controller design consists to determine the local feedback gains , , and in the consequent parts. The state variables are determined by an observer and are detailed in the next section.

The open-loop system is given by

By substituting (2.6) into (2.2), the closed-loop fuzzy system can be represented as:

where and .

Assumption 2.1. The time derivative of the premises membership function is upper bound such that , for , where, are given positive constants.

Assumption 2.2. The matrices denote the uncertainties in the system and take the form of

where and are known constant matrices and and are unknown matrix functions satisfying:

where is an appropriately dimensioned identity matrix.

Lemma 2.3 (Boyd et al. Schur complement [12]). Given constant matrices , and with appropriate dimensions, where and , then

if and only if

Lemma 2.4 (Peterson and Hollot [2]). Let , and satisfying are appropriately dimensional matrices then the following inequality

is true, if and only if the following inequality holds for any

The aim of the next section is to find conditions for the stabilization of the closed-loop T-S fuzzy system by using the Lyapunov theory.

3. Main Results

Consider the closed-loop system without uncertainties

In order to give stability conditions, the slack matrix variables and the membership function boundary Mozelli et al. [14] are used. Consider the following null product that will serve stability analysis purposes:

3.1. A PDC Controller with Derivative Membership Function

By substituting (2.6) into (3.1), the closed-loop fuzzy system can be represented as:

The next Theorem gives sufficient conditions to guarantee stability of system (3.3).

Theorem 3.1. Under Assumptions 2.1 and 2.2, and for , the Takagi-Sugeno fuzzy system (3.1) is stabilizable with the PDC controller (2.6), with gains given by , , and , if there exist positive definite symmetric matrices , and any matrices , and with appropriate dimensions such that the following LMIs hold.

where

Proof of Theorem 3.1. Let’s consider the fuzzy weighting-dependent Lyapunov-Krasovskii functional as:

with , , where , , , and .

This candidate Lyapunov function satisfies

(i) is ,(ii) and for ,(iii).

The time derivative of is given by:

Adding the null product, then

Equation (3.9) can be rewritten as,

where

Then, based on Assumption 2.1, an upper bound of is obtained as:

Based on (2.5), it follows that where is any symmetric matrix of proper dimension.

Suppose that and adding to (3.12), then

where

Then,

Using vector , (3.15) can be rewritten as

where

If and , then and (3.3) is stable. Pre- and postmultiplying and by nonsingular matrices and , respectively, and pre- and postmultiplying by and , respectively, then we obtain

where , and

for the following variables definition:

If LMI in holds then the closed-loop continuous fuzzy system (3.3) is asymptotically stable. The control gains are given by , , and . This completes the proof.

Remark 3.2. The selection of given in Assumption 2.1 is performed by using a simple procedure given in [15].

Remark 3.3. The major contribution of the Theorem 3.1 is represented by the proposed PDC controller given by (2.6). The contribution appears in the gains introduced in the controller term based on derivative membership functions. The stabilization condition proposed is less conservative than some of those in the literature, as is shown in the example below.

3.2. Observer Design

In order to determine state variables of the system, this section gives a solution by the means of fuzzy observer design. The following condition is to be satisfied by the observer:

where denotes the state vector estimated by a fuzzy observer. This condition guarantees that the steady-state error between and converge to and we denote this error by .

A stabilizing observer-based controller can be formulated as follow:

where denotes the output vector.

We consider the proposed PDC controller given by (2.6):

Replacing the fuzzy controller (2.6) in fuzzy observer we obtain the closed-loop fuzzy system as:

The augmented system is represented as follows:

where

By applying Theorem 1 in [16] in the augmented system (3.25) we derive the following Theorem.

Theorem 3.4. Under Assumptions 2.1 and 2.2, and for , the Takagi-Sugeno fuzzy system (3.1) is stable if there exist positive definite symmetric matrices , and , matrices such that the following LMIs holds.

where

Proof of Theorem 3.4. The result follows immediately from the proof of Theorem 1 in [16].

4. Robust Stability Condition with PDC Controller

Consider the uncertain closed-loop system (2.8). A sufficient robust stability condition is given as follows.

Theorem 4.1. Under Assumptions 2.1 and 2.2, and for , the Takagi-Sugeno fuzzy system (2.2) is stabilizable with the PDC controller (2.6), with gains given by , , and , if there exist positive definite symmetric matrices , and any matrices , and with appropriate dimensions such that the following LMIs hold.

where

with

where , , and .

Proof of Theorem 4.1. The result follows immediately from the proof of Theorem 3.1 by replacing in the matrix inequality with and with , we obtain the following inequality:

Using vector , (4.5) can be rewritten as

where

Then, based on Lemma 2.4, an upper bound of obtained as:

by Schur complement, we obtain,

with

If the LMI holds then the system (2.8) is stable. This completes the proof.

The following theorem gives sufficient conditions for robust PDC controller design.

Theorem 4.2. Under Assumptions 2.1 and 2.2, and for , the Takagi-Sugeno robust fuzzy system (2.2) is stabilizable with the PDC controller (2.6), with gains given by , , and , if there exist positive definite symmetric matrices , , , and any matrices , and with appropriate dimensions such that the following LMIs hold

where

with

Proof of Theorem 4.2. Consider (4.8) and pre- and postmultiplying by and , respectively, then we obtain

with defined by (3.5).

By Schur complement, we obtain,

with

for the following variables definition:

If LMI in holds then the closed-loop continuous fuzzy system (2.8) is asymptotically stable.

The control gains are given by , , and . This completes the proof.

5. Numerical Examples

In order to show the improvements of the proposed approaches over some existing results, in this section, we present a numerical example in which we present the feasible area for a T-S fuzzy system. Indeed, we compare the proposed fuzzy Lyapunov approaches (Theorem 3.4) with result provided by [17], and in [14, Theorem  6]. A second example is given to improve the given gains of robust PDC controller.

Example 5.1. Consider the following continuous T-S fuzzy system:

with

where , , considering and .

The proposed approach (Theorem 3.4) gives less conservative stabilization conditions (Figure 1) than some recent results provided by [14, 17].

Figure 1: Feasible area provided by [17] (+), [14] (◯) and Theorem 3.1 (•).

Example 5.2. Consider the uncertain continuous T-S fuzzy system given by (2.8) with

for , , considering , and , we find the following gains values:

6. Conclusion

This paper provided new conditions for the stabilization with a class of PDC controller of Takagi-Sugeno fuzzy systems in terms of a combination of the LMI approach and the use of nonquadratic Lyapunov function as fuzzy Lyapunov function. In addition, the time derivative of membership function is considered by the PDC fuzzy controller and the slack matrix variables are introduced in order to facilitate the stability analysis. An approach to design an observer is derived in order to estimate variable states. In addition, a new condition of the stabilization of uncertain system is given in this paper.

The stabilization condition proposed in this paper is less conservative than some of those in the literature, which has been illustrated via examples.

References

T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985.

M. A. L. Thathachar and P. Viswanath, “On the stability of fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 5, no. 1, pp. 145–151, 1997.

L. K. Wong, F. H. F. Leung, and P. K. S. Tam, “Stability design of TS model based fuzzy systems,” in Proceedings of the 6th IEEE International Conference on Fussy Systems, pp. 83–86, July 1997.

I. Abdelmalek, N. Goléa, and M. L. Hadjili, “A new fuzzy Lyapunov approach to non-quadratic stabilization of Takagi-Sugeno fuzzy models,” International Journal of Applied Mathematics and Computer Science, vol. 17, no. 1, pp. 39–51, 2007.

C. H. Fang, Y. S. Liu, S. W. Kau, L. Hong, and C. H. Lee, “A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems,” IEEE Transactions on Fuzzy Systems, vol. 14, no. 3, pp. 386–397, 2006.

H. K. Lam and F. H. Leung, Analysis of Fuzzy Model based Control Systems, Springer, Hong Kong, China, 2011.

L. A. Mozelli, R. M. Palhares, F. O. Souza, and E. M. A. M. Mendes, “Reducing conservativeness in recent stability conditions of TS fuzzy systems,” Automatica, vol. 45, no. 6, pp. 1580–1583, 2009.

C. Hua and S. X. Ding, “Decentralized networked control system design using TS fuzzy approach,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 9–21, 2012.

M. Chadli and T.-M. Guerra, “LMI solution for robust static output feedback control of takagi-sugeno fuzzy models,” IEEE Transactions on Fuzzy Systems. In press

M. Chadli and H. R. Karimi, “Robust observer design for unknown inputs takagi-sugeno models,” IEEE Transactions on Fuzzy Systems. In press

K. Zhang, B. Jiang, and P. Shi, “Fault estimation observer design for discrete-time takagisugeno fuzzy systems based on piecewise lyapunov functions,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 192–200, 2012.

S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, vol. 15, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pa, USA, 1994.

K. Tanaka and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach, John Wiley and Sons, 2001.

L. A. Mozelli, R. M. Palhares, and G. S. C. Avellar, “A systematic approach to improve multiple Lyapunov function stability and stabilization conditions for fuzzy systems,” Information Sciences, vol. 179, no. 8, pp. 1149–1162, 2009.

K. Tanaka, T. Hori, and H. O. Wang, “A fuzzy Lyapunov approach to fuzzy control system design,” in Proceedings of the American Control Conference, pp. 4790–4795, Arlington, Va, USA, June 2001.

M. Yassine and B. Mohamed, “Condition of stabilisation for continuous takagi-sugeno fuzzy system based on fuzzy lyapunov function,” International Journal of Control and Automation, vol. 4, no. 3, 2011.

M. C. M. Teixeira, E. Assunção, and R. G. Avellar, “On relaxed LMI-based designs for fuzzy regulators and fuzzy observers,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 5, pp. 613–623, 2003.