Time-Delay Systems and Its Applications in Engineering 2014View this Special Issue
Research Article | Open Access
Filtering for Discrete Markov Jump Singular Systems with Mode-Dependent Time Delay Based on T-S Fuzzy Model
This paper investigates the filtering problem of discrete singular Markov jump systems (SMJSs) with mode-dependent time delay based on T-S fuzzy model. First, by Lyapunov-Krasovskii functional approach, a delay-dependent sufficient condition on -disturbance attenuation is presented, in which both stability and prescribed performance are required to be achieved for the filtering-error systems. Then, based on the condition, the delay-dependent filter design scheme for SMJSs with mode-dependent time delay based on T-S fuzzy model is developed in term of linear matrix inequality (LMI). Finally, an example is given to illustrate the effectiveness of the result.
Recently, some efforts on filtering and control problem have been put on the Markov jump linear systems with Markovian jump time delays [1–7]. For example,  dealt with the robust stabilizability and disturbance attenuation for a class of uncertain descriptor time-delay systems with jumping parameters. The transition of the jumping parameters in the systems is governed by a finite state Markov process.  addressed the problems of delay-dependent robust control and filtering for Markovian jump linear systems with norm-bounded parameter uncertainties and time-varying delays. The robust filtering problem for mode-dependent time-delay discrete Markov jump singular systems with parameter uncertainties is discussed in . The energy-to-peak filtering problem of Markov jump systems with interval time-varying delay is investigated in . The - fuzzy control problem was considered for nonlinear stochastic Markov jump systems with neutral time-delays in .
In practical applications, to research the nonlinear time-delay system, the scholars considered the T-S fuzzy time-delay model which is a kind of effective representation, and many analysis and synthesis methods for T-S fuzzy time-delay systems have been developed over the past years [8–17]. So, many scholars extended this model to Markov jump systems with time delays [18–28]. Reference  was concerned with an control for a class of T-S fuzzy Markov jump system under unreliable communication links. Reference  concerned with the adaptive synchronization for T-S fuzzy neural networks with stochastic noises and Markovian jumping parameters. Reference  was concerned with the stability and stabilization problems for a class of nonlinear systems with Markovian jump parameters. The T-S fuzzy model was employed to represent the Markovian jump nonlinear systems with partly unknown transition probabilities. Reference  dealt with the delay-dependent asymptotic stability analysis problem for a class of fuzzy bidirectional associative memory neural networks with time-varying interval delays and Markovian jumping parameters by T-S fuzzy model. In , the asymptotic stability of fuzzy Markovian jumping genetic regulatory networks with time-varying delays by delay decomposition approach was investigated.
For singular systems, since the regularity, causality, and stability are needed to be considered, this class of systems is more complicated and difficult compared with standard state space systems. The strict conditions of linear matrix inequalities (LMI) are not easy to obtain. Many references [29–31] discussed the control problems for SMJSs.
However, to the best of our knowledge, the filtering problem of discrete SMJSs with mode-dependent time delay based on T-S fuzzy model has not been addressed, which is the focus of this paper.
This paper is organized as follows: In Section 2, the filter for SMJSs with mode-dependent time delay based on T-S fuzzy model is formulated. In Section 3 we give the sufficient condition to assure asymptotic stability and the noise-attenuation level bound for the SMJSs filtering-error systems. Based on the condition in Section 3, we present a stable fuzzy filter in term of LMIs. Section 4 provides illustrative examples to demonstrate the effectiveness of the proposed method. Conclusions are given in Section 5.
Notations. The notations used throughout this paper are fairly standard. The superscript “” stands for matrix transpose, and the notation () means that matrix is real symmetric and positive (or being positive semi-definite). and are used to denote appropriate dimensions identity matrix and zero matrix, respectively. The notation in a symmetric always denotes the symmetric block in the matrix. The parameter denotes a block-diagonal matrix. Matrices, if not explicitly stated, are assumed to have compatible dimensions for algebraic operations.
2. System Descriptions and Preliminaries
Given a probability space , where is the sample space, is the algebra of events and is the probability measure define on . We consider a class of discrete-time uncertain nonlinear SMJSs with time-varying delay over space . which can be represented by the following T-S model.
Plant Rule . If is and is andand is then where, , are the premise variables that are measurable and each is fuzzy set. is the state vector. is the measurement. is the signal to be estimated. is the disturbance input vector which belongs to . is the number of IF-THEN rules, is a vector-valued initial function. is a Markov chain taking values in finite space , with a transition probability from mode at time to mode at time as with for . , is a positive integer, denoting the time delay of the system involved in node and . For each , the matrix is a known constant and singular, and .
For each , , , , , , , , , and are known constant matrices with appropriate dimensions.
Remark 1. In this paper, we consider only the case , with , .
By using center-average defuzzifier, product inference, and singleton fuzzifier, the dynamic fuzzy model (1) can be expressed by the following global model: with
is the grade of membership of in , and it is assumed that , . for all . Therefore, and for all .
The system (4) can be represented by the following equation: with
Definition 2. System or the pair is said to be(1)regular if for any , ,(2)causal if it is regular and for any , .
Definition 3. System (6) is said to be stochastically stable, if for every initial state , the following condition is satisfied.
In this paper, employing the idea of PDC, we consider the filter based on T-S fuzzy-model with order ( for full-order filter, and for reduced-order filter): where,
For each , , the matrices , , , and are to be calculate.
Remark 4. may be either equal to or less than .
The filtering problem is formulated as follows. Given system (1) and a scalar , determine a filter in the form of (8) such that the filtering error system (11) is regular, causal, stochastically stable and satisfies the following performance:
For the convenience, we use the following notations:
Lemma 5 (see ). Given matrices , , and with appropriate dimensions, is symmetric and . Then
Remark 6. Without loss of generality, we assume that and also .
3. Main Results
In this section, we proposed the sufficient conditions and design method for the filter in term of LMIs. First of all, we present a delay-dependent condition such that system (6) is regular, causal, and stochastically stable. Consider system (6) with
Theorem 7. For a given scalar , the filtering error system (11) is regular, causal, and stochastically stable and satisfies (13), if, for each mode , there exist symmetric matrices , , , and matrices and that satisfy the following set of coupled LMIs:
, , is any constant matrix satisfying with .
Proof. The proof is omitted because it is similar to .
Theorem 8. Let , , , , and be given scalars. There exists a filter in the form of (8) with such that the filtering error system (11) is regular, causal, and stochastically stable and satisfies (13); if for each mode , there exist matrices , , , , , , , , , , and that satisfy the following coupled LMIs:
where ; is any constant matrix satisfying with .
Proof. Since , (17) is equivalent to where then, based on Lemma 5 and , , , for any matrix with appropriate dimensions, and scalars , , the following inequalities hold: Let , . According to the inequalities (23), and applying the Schur complement, it is obtained that if (19) holds, then (21) holds. The proof is completed.
Then, we readily obtain the following theorem.
Theorem 9. Let , , , , and be given scalars. There exists a filter in the form of (8) with such that the filtering error system (11) is regular, causal, stochastically stable and satisfies (13); if for each mode , there exist matrices , , , , , , , , , , that satisfy the following coupled LMIs:
where ; is any constant matrix satisfying with .
In this section, we give a numerical example to illustrate the use of the presented method. Consider the system of the form with two fuzzy rules and two modes.
The first rule parameters with 1st mode are as follows: The first rule parameters with 2nd mode are as follows: The second rule parameters with 1st mode are as follows: The second rule parameters with 2nd mode are as follows:
The time-delays , . The mode switching is governed by a Markov chain that has the following transition probability: For the full-order filter with , we choose that , and let , , , , , , . Solving the LMIs (24). The solutions are as follows:
The problem on filter design has been addressed for T-S fuzzy-model-based SMJSs. Based on the Lyapunov-Krasovskii functional approach, a sufficient condition for the existence of filter, which stabilizes the T-S fuzzy-model-based filtering-error systems and guarantees a prescribed level on disturbance attenuation, has been obtained in terms of LMI form. The numerical example has shown the effectiveness of the proposed method. In addition, the - filtering problems for T-S fuzzy delayed systems are also challenging, which could be our further work.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by National Natural Science Foundation of China (61203005), supported in part by National Natural Science Foundation of Heilongjiang province (QC2013C068), and supported in part by Postdoctoral Science-Research Developmental Foundation of Heilongjiang province (LBH-Q12130).
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Copyright © 2014 Cheng Gong and Yi Zeng. 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.