- Open Access
Exponential robust stability of T-S fuzzy stochastic p-Laplace PDEs under zero-boundary condition
© Pu and Rao; licensee Springer. 2013
- Received: 19 September 2013
- Accepted: 8 November 2013
- Published: 3 December 2013
In this paper, the stability of a class of time-delay Takagi-Sugeno (T-S) fuzzy Markovian jumping partial differential equations (PDEs) with p-Laplace diffusion are investigated, and several criteria for asymptotical stability and robust exponential stability are obtained. Different from all the previous related literature, the authors use the contraction mapping theory to obtain in this paper the existence of other solutions for PDEs with p-Laplace besides the trivial solution. In fact, if there is only the trivial solution for the PDEs, the stability criteria about the trivial solution would become meaningless. Moreover, infinitely many solutions for these PDEs of all the previous related literature can be obtained by employing the methods of this paper. In a word, the works of all the related literature were found more meaningful owing to the methods and results of this paper.
- Fuzzy Rule
- Trivial Solution
- Exponential Robust Stability
- Real Constant Matrice
- Global Exponential Robust Stability
In this paper, we are to study the stability of a class of delayed Takagi-Sugeno (T-S) fuzzy Markovian jumping p-Laplace partial differential equations (PDEs) which owns a wide range of physics and engineering backgrounds (see, e.g., [1–3]). Below, we shall introduce this Markovian jumping fuzzy mathematical model.
where is transition probability rate from i to j () and , and .
where is a positive scalar, is a bounded domain with a smooth boundary ∂ Ω of class by Ω, . Below, is always denoted by u for the sake of convenience. denotes the Hadamard product of matrix and (see  or ), and satisfies for all . In mode , we denote and . Denote by the time delay , which satisfies for any mode . Functions , , . Boundary condition (1.1b) is called the Dirichlet boundary condition if , and the Neumann boundary condition if . Here, denotes the outward normal derivative on ∂ Ω.
The T-S fuzzy mathematical model with time delay is described as follows.
Fuzzy rule j:
where () is the premise variable, (; ) is the fuzzy set that is characterized by a membership function, r is the number of the IF-THEN rules, and s is the number of the premise variables.
where , , () is the membership function of the system with respect to the fuzzy rule j. can be regarded as the normalized weight of each IF-THEN rule, satisfying and . Motivated by some methods of the above-mentioned literature and recent related studies [6–15, 22–24], we are to investigate the stability of T-S fuzzy system (1.6).
(H1) Let , there exist positive definite diagonal matrices and such that
(H2) There exist constant diagonal matrices , , , with , , , such that
Here is an unknown matrix function satisfying , and , , are known real constant matrices. Throughout this paper, for a matrix , we denote the matrix . In addition, we denote by I the identity matrix with compatible dimension and denote .
for all admissible uncertainties satisfying (2.1).
Note that Definition 2.1 actually provides the definition about the global stochastic exponential robust stability for the trivial solution of PDEs (1.6).
Lemma 2.1 ()
Lemma 2.2 (Schur complement )
where , and are dependent on t.
In this section, we assume that the time-varying delays satisfy with for any mode .
then system (1.6) is global stochastic exponential robust stability in the mean square.
Proof The whole proof may be divided into two big steps.
To apply the fixed point theory, we need to define the complete metric space as follows.
is continuous on ;
where and .
and , for each .
In view of the fact that for each j, it is not difficult to verify that the fixed point of Φ is the solution of system (3.3), and the solution of (3.3) is the fixed point of Φ. So we only need to prove that Φ has the corresponding fixed point on Θ for any given initial condition .
Next, we claim that for each .
Indeed, for any given , it follows from (3.4) and the assumptions on , that is continuous on , and hence condition (a) is satisfied. In addition, , , and then condition (b) holds. Thereby, for all j.
Finally, we claim that Φ is a contraction mapping on Θ.
which implies that Φ is a contraction mapping on Θ. And then the contraction mapping theory yields that Φ has the fixed point on Θ, which means is the solution to system (3.3) for a given initial condition .
Remark 3.1 If the number of the IF-THEN rules , then (1.6) is just (1.1). Hence, we have also shown the existence of solutions for (1.1).
Remark 3.2 From the arbitrariness of initial condition , we know that system (1.6) may in all probability own infinitely many solutions under zero-boundary condition (1.1b). Below, we shall show that all the above-mentioned solutions converge to the trivial solution as for an arbitrary initial condition . In fact, we shall prove that the trivial solution of PDEs (1.6) is globally exponentially stable. However, in much previous related literature (e.g., ), the existence of solutions for PDEs is not discussed. Naturally, people want to ask whether the system owns the other solutions besides the trivial solution. Now we provide a sufficient condition for the existence. Compared with , it is a major advance. In addition, we provide the first method, by which we can also give similar sufficient conditions for the existence of the PDEs in the previous related literature, including .
Step 2. We prove that system (1.6) is global stochastic exponential robust stability in the mean square.
Then we get , satisfying .
where positive scalars , satisfy and for any mode , scalars , . Therefore, we can see by (3.13) and Definition 2.1 that system (1.6) is global stochastic exponential robust stability in the mean square.
Remark 3.3 It is the first time to obtain the robust exponential stability criterion for T-S fuzzy PDEs (1.6). Theorem 3.1 admits more effectiveness and less conservatism due to the large allowable variation range of time-delay, which will be illustrated by a numerical example (below).
Denote and .
Example 4.1 Consider the T-S fuzzy p-Laplace PDEs with Markovian jumping parameters as follows.
Fuzzy rule 1:
Fuzzy rule 2:
Hence, we conclude from Theorem 3.1 that PDEs (4.1)-(4.2) is global exponential robust stability in the mean square.
Remark 4.1 Example 4.1 illustrates the effectiveness and less conservatism due to the allowable upper bound of time-delay ().
The stability of the nonlinear p-Laplace () Markovian jumping dynamic PDEs was first studied in . Since then, there have been a lot of related literature [1–3, 16–18] involving the stability analysis of the nonlinear p-Laplace () dynamic PDEs under various complicated and practical factors, such as impulse, parameter uncertainties and so on. However, in all the previous related literature, the existence of solutions of those PDEs was neglected. Naturally, people want to know whether there are other solutions besides the trivial solution. If there exists only the trivial solution as the unique solution for the PDEs, all those stability criteria about the trivial solution would become meaningless though these PDEs of all the previous related literature can actually own infinitely many solutions only if the similar sufficient conditions are also given. So, in this paper, we present a sufficient condition for the existence of PDEs (1.6) in our Theorem 3.1 by way of the contraction mapping theory. Moreover, we have provided the methods, by which the existence of solutions for those PDEs in the above related literature can similarly be proved. The works of all the above related literature become more meaningful owing to the contribution of this paper (see Remark 3.2). So the further study is no longer the existence of solutions for dynamic PDEs with the nonlinear p-Laplace.
Note that almost all the above related literature did not point out the role that the nonlinear p-Laplace items play, except . In fact, when , 2-Laplace is the linear Laplace, and there are many papers (see, e.g., [9, 10, 19–21]) in which the Laplace diffusion item plays its role in their stability criteria for the linear Laplace PDEs can be considered in the special Hilbert space that can be orthogonally decomposed into the direct sum of infinitely many eigenfunction spaces. However, the nonlinear p-Laplace (, ) brings great difficulties, for the nonlinear p-Laplace PDEs should be considered in the frame of Sobolev space that is only a reflexive Banach space. Indeed, owing to the great difficulties, the authors only provide in  the stability criterion in which the nonlinear p-Laplace items play roles in the case of . So a further profound study is very interesting, which may call for some new mathematical methods, and even new mathematical theories. Under the Dirichlet or Neumann boundary condition, the problem of the role of the nonlinear p-Laplace ( or ) item in the stability criteria for PDEs still remains open and challenging.
The authors would like to thank the anonymous referees for their detailed comments and valuable suggestions which considerably improved the presentation of this paper. This research is supported by the Scientific Research Fund of Science Technology Department of Sichuan Province (2010JY0057, 2012JYZ010), and by the Scientific Research Fund of Sichuan Provincial Education Department (12ZB349).
- Wang X, Rao R, Zhong S: LMI approach to stability analysis of Cohen-Grossberg neural networks with p -Laplace diffusion. J. Appl. Math. 2012., 2012: Article ID 523812Google Scholar
- Pan Q, Zhang Z, Huang J: Stability of the stochastic reaction-diffusion neural network with time-varying delays and p -Laplacian. J. Appl. Math. 2012., 2012: Article ID 405939Google Scholar
- Rao R, Wang X, Zhong S, Pu Z: LMI approach to exponential stability and almost sure exponential stability for stochastic fuzzy Markovian-jumping Cohen-Grossberg neural networks with nonlinear p -Laplace diffusion. J. Appl. Math. 2013., 2013: Article ID 396903Google Scholar
- Wang Y, Xie L, Souza D, Carlos E: Robust control of a class of uncertain nonlinear system. Syst. Control Lett. 1992, 19(2):139-149.View ArticleMATHGoogle Scholar
- Boyd S, Ghaoui L, Feron F, Balakrishnan V: Linear Matrix Inequalities in Systems and Control Theory. SIAM, Philadelphia; 1994.View ArticleMATHGoogle Scholar
- Zhu Q, Li X, Yang X: Exponential stability for stochastic reaction-diffusion BAM neural networks with time-varying and distributed delays. Appl. Math. Comput. 2011, 217(13):6078-6091.MathSciNetView ArticleMATHGoogle Scholar
- Sathy R, Balasubramaniam P: Stability analysis of fuzzy Markovian jumping Cohen-Grossberg BAM neural networks with mixed time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 2011, 16(4):2054-2064.MathSciNetView ArticleMATHGoogle Scholar
- Zhu Q, Li X: Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen-Grossberg neural networks. Fuzzy Sets Syst. 2012, 203: 74-94.View ArticleMathSciNetMATHGoogle Scholar
- Zhou C, Zhang H, Zhang H, Dang C: Global exponential stability of impulsive fuzzy Cohen-Grossberg neural networks with mixed delays and reaction-diffusion terms. Neurocomputing 2012, 91: 67-76.View ArticleGoogle Scholar
- Wang C, Kao Y, Yang G: Exponential stability of impulsive stochastic fuzzy reaction-diffusion Cohen-Grossberg neural networks with mixed delays. Neurocomputing 2012, 89: 55-63.View ArticleGoogle Scholar
- Song Q, Cao J: Global dissipativity on uncertain discrete-time neural networks with time-varying delays. Discrete Dyn. Nat. Soc. 2010., 2010: Article ID 810408Google Scholar
- Song Q, Wang Z, Liang J: Analysis on passivity and passification of T-S fuzzy systems with time-varying delays. J. Intell. Fuzzy Syst. 2013, 24(1):21-30.MathSciNetMATHGoogle Scholar
- Song Q: Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays. Neurocomputing 2011, 74(5):838-845.View ArticleGoogle Scholar
- Song Q, Wang Z: New results on passivity analysis of uncertain neural networks with time-varying delays. Int. J. Comput. Math. 2010, 87(3):668-678.MathSciNetView ArticleMATHGoogle Scholar
- Xu D, Xu L: New results for studying a certain class of nonlinear delay differential systems. IEEE Trans. Autom. Control 2010, 55(7):1641-1645.View ArticleMathSciNetGoogle Scholar
- Rao R, Zhong S, Wang X: Delay-dependent exponential stability for Markovian jumping stochastic Cohen-Grossberg neural networks with p -Laplace diffusion and partially known transition rates via a differential inequality. Adv. Differ. Equ. 2013., 2013: Article ID 183Google Scholar
- Rao R, Zhong S, Wang X: Stochastic stability criteria with LMI conditions for Markovian jumping impulsive BAM neural networks with mode-dependent time-varying delays and nonlinear reaction-diffusion. Commun. Nonlinear Sci. Numer. Simul. 2014, 19(1):258-273.MathSciNetView ArticleGoogle Scholar
- Rao R, Pu Z: Stability analysis for impulsive stochastic fuzzy p -Laplace dynamic equations under Neumann or Dirichlet boundary condition. Bound. Value Probl. 2013., 2013: Article ID 133Google Scholar
- Pan J, Zhong S: Dynamical behaviors of impulsive reaction-diffusion Cohen-Grossberg neural network with delays. Neurocomputing 2010, 73: 1344-1351.View ArticleMATHGoogle Scholar
- Pan J, Liu X, Zhong S: Stability criteria for impulsive reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. Math. Comput. Model. 2010, 51: 1037-1050.MathSciNetView ArticleMATHGoogle Scholar
- Pan J, Zhong S: Dynamic analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with delays. Adv. Differ. Equ. 2009., 2009: Article ID 410823Google Scholar
- Xu D, Wang X, Yang Z: Further results on existence-uniqueness for stochastic functional differential equations. Sci. China Math. 2013, 56(6):1169-1180.MathSciNetView ArticleMATHGoogle Scholar
- Li B, Li D, Xu D: Stability analysis for impulsive stochastic delay differential equations with Markovian switching. J. Franklin Inst. 2013, 350(7):1848-1864.MathSciNetView ArticleGoogle Scholar
- Rao R, Pu Z: LMI-based stability criterion of impulsive T-S fuzzy dynamic equations via fixed point theory. Abstr. Appl. Anal. 2013., 2013: Article ID 261353Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.