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Journal of Control Science and Engineering
Volume 2018, Article ID 8198603, 17 pages
https://doi.org/10.1155/2018/8198603
Research Article

Nonfragile Filter Design for Nonlinear Continuous-Time System with Interval Time-Varying Delay

1College of Mechatronic Engineering, Qiqihar University, Qiqihar 161006, China
2College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
3State Grid Heilongjiang Electric Power Company Qiqihar Power Supply Company, Qiqihar 161006, China

Correspondence should be addressed to Zhongda Lu; moc.361@adgnohzul

Received 9 May 2018; Revised 18 July 2018; Accepted 2 August 2018; Published 5 September 2018

Academic Editor: Jongrae Kim

Copyright © 2018 Zhongda Lu 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 investigates nonfragile filter design for a class of continuous-time delayed Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Filter parameters occur multiplicative gain variations according to the filter’s implementation, to handle this variations, a nonfragile filter is presented and a novel filtering error system is established. The nonfragile filter guarantees the filtering error system to be asymptotically stable and satisfies given performance index. By constructing a novel Lyapunov-Krasovskii function and using the linear matrix inequality (LMI), delay-dependent conditions are exploited to derive sufficient conditions for nonfragile designing filter. Using new matrix decoupling method to reduce the computational complexity, the filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the effectiveness of the proposed method.

1. Introduction

As we all known, in practical control systems, nonlinearity and time delay phenomena are often encountered in various industry and control system, such as networked control system and mechanical drive control system. The control of nonlinear systems has been explored and studied by many scholars in related fields. T-S fuzzy model is a powerful tool to deal with nonlinearity; much effort has been devoted on the networked control system for T-S fuzzy system or time-delayed (see [14]). The actuator and sensor faults estimation based on T-S fuzzy model with unmeasurable premise variables were investigated in [5]. The problem of exponential stabilization for sampled-data T-S fuzzy control systems with packet dropouts was investigated in [6]; a switched system approach is proposed to model the data-missing phenomenon. There always exist many kinds of noise interference in the process of transmission among real industrial control system’s signal, causing the error between the obtained signals and the desired signals; in order to obtain the accurate data information about the control signal and eliminate the influence of disturbances on the system, it is essential to be filtering. At present, there are the Kalman filtering, fault detection filter, filtering, filtering, and so on. Compared with other filtering methods, filtering does not need the exactly known statistics of the external disturbance [7, 8] and filtering has excellent robustness against unmodeled dynamics. In recent years, filtering system based on the Takagi-Sugeno (T-S) model has attracted much attention from the control community [9, 10], many studies have addressed filtering for T-S fuzzy systems with time-varying delay, and the proposed filtering technology has been applied to many actual communications system. Authors in literature [1113] investigated the problem of filter design for continuous-time via Takagi-Sugeno fuzzy model approach. In literature [14, 15], the problem of filtering for a class of discrete fuzzy system has been reported. Based on discrete inequality technique and the Lyapunov-Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities. In literature [16], the event-triggered filtering for networked control systems with quantization and network-induced delays was investigated; it improved the usage of network resource.

However, in practical system, it is difficult for an exactly implemented filter to meet the actual requirements because inaccuracies or uncertainties, which include collection error and component aging, may occur during filter implementation. It often degrades the performance of the control system and even instability; the filter has a higher sensitivity to the parameter uncertainty [17]. Thus, we need to design nonfragile filter considering the parameter variation and uncertainty. Some achievements have been reported in journal about nonfragile filtering for T-S fuzzy systems with time-varying delay. In literature [18], design an filter with the gain variations such that the filtering error system was quadratically D stable and guarantees a prescribed performance level. Literature [19] is concerned with the problem of nonfragile filtering for discrete-time nonlinear systems and considered additive interval uncertainty. In literature [20], the designed nonfragile filter was in standard form and the filter was designed, which have two types of multiplicative gain variations; these models were in standard form. In literature [21], the problem of nonfragile filter design for linear continuous-time systems was studied; it proposed a notion of structured vertex separator. In literature [22], this paper studied the nonfragile filtering problem for a class of discrete-time T-S fuzzy systems with both randomly occurring gain variations and channel fading. In literature [23], the problem of nonfragile filter design for linear continuous-time systems has been studied. The filter has been designed; it included additive gain variations. In literature [24], it studied the nonfragile filtering design for a kind of fuzzy stochastic system with time-varying delay and parameter uncertainties. Sufficient conditions for stochastic input-to-state stability (SISS) of the fuzzy stochastic systems were obtained. Papers proposed the filter design methods with occurring additive gain variations according to the filter’s implementation.

Motivated by the aforementioned discussion, in this paper, a nonfragile filter design method is proposed to enhance the nonfragility of the filter. By considering the multiplicative gain variations and interval time-varying delays according to the filter implementation, a novel filtering error system is established. Different from some existing works, Jensen’s inequality is used to tackle the integral items of the derivative of Lyapunov-Krasovskii; a more relaxed performance stability criterion is derived. By constructing a novel Lyapunov-Krasovskii function and using the linear matrix inequality (LMI), delay-dependent conditions are exploited to derive sufficient conditions for nonfragile designing filter. Our objective is to design nonfragile filter which guarantees the filtering error system to be asymptotically stable and satisfies given performance index. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs).

The rest of this paper is organized as follows. The problem formulation is stated in Section 2; nonfragile filter scheme and filtering error system are employed to enhance system’s stabilization. Stability analysis and fuzzy filter design are obtained in Section 3; by constructing a Lyapunov-Krasovskii functional, a new stability criterion is proposed to prove being less conservative than the existing ones. An applicable filter is designed in Section 4, which guarantees stability and a desire performance of the filtering error system. In order to show the effectiveness of the proposed method, simulation results are presented in Section 5.

2. Problem Formulation

Consider a nonlinear system with time-varying delay which could be approximated by a class of T-S fuzzy systems with time-varying delays. The T-S fuzzy model with plant rules can be described by the following.

Plant Rule . If is ... and is , then

, where is the continuous initial vector function defined on , are the fuzzy sets, , and is the number of IF-THEN rules. are the premise variables, is the state vector, is the measured output, is the signal vector to be estimated, is the disturbance signal vector which belongs to , and , , , , , , , , are known constant matrices with appropriate dimensions. is interval time-varying delay that satisfies the following inequality: , where , , and are constant scalars.

By employing the commonly used center-average defuzzifier, product interference, and singleton fuzzifier, the overall fuzzy model is inferred as follows:

where , , , represents the grade of membership for in , , and . It can be seen that , .

Consider the nonfragile fuzzy filter with multiplicative gain uncertainties; we design the following fuzzy filter:

Consider the following filter form which is analogous to the fuzzy control form through parallel distributed compensation.

Plant Rule . If is ... and is , then

, where is the continuous initial vector function, is the filter state vector, the estimated signal vector is , and , , , are the filter parameters. , , represent the gain variations.

The multiplicative gain uncertainties are defined aswhere , , , , , are constant matrices with appropriate dimensions and , , are uncertain matrices bounded, such that

By combining (2) with (4), we can obtain the following filtering error system:where

In this paper, our purpose is to design the fuzzy filter in the form of (3), meanwhile, satisfying the following requirements.

The filtering error system (7) with is said to be asymptotic stability for any initial condition

For a given positive scalar , the filtering error system (7) is said to be asymptotically stable with guaranteed performance , if it is asymptotic stability and the filtering error satisfiesfor all and nonzero subject to the zero initial condition.

3. Stability and Filtering Performance Analysis

The purpose of this paper is to design nonfragile filter such that the filtering error system (7) is asymptotically stable with performance index. A sufficient condition is presented in the following theorem to guarantee the existence of the filter in form of (3).

Lemma 1. Let , , and be real matrices with appropriate dimensions and . Then, for any scalar ,

Lemma 2. For any vectors and any scalar , matrices are real matrices of appropriate dimensions with ; then the following inequalities hold:

Lemma 3. Let , , , , and be real matrices of appropriate dimensions such that and . Then, for any scalar such that , we have

Lemma 4. Let and be any real matrices of appropriate dimensions. Then

Lemma 5 (Jenson’s inequality). Suppose and ; for any positive matrix , the following inequality holds:

Theorem 6. For nonlinear systems (1) and the filtering error system (7), the given positive scalar performance and the filtering error system (7) are asymptotically stable with performance if there exist symmetric positive scalars , , , , , , , , , , and symmetric positive definite matrices , , , , , such that we have the following inequality.

Proof. We construct a novel Lyapunov-Krasovskii function as follows: By Lemma 2, for , , we can obtainSimilar to (25), if there exists , we can obtainBy Lemma 2, if there exists , we can obtainSimilar to (25), if there exists , we can obtainCombining with inequalities (25), (27), and (28) givesLemma 4 giveswhereBy Lemma 3, if there exist , such that , from (31), it follows thatBy Lemma 3, if there exist , such that , from (32), it follows thatBy Lemma 3, if there exist such that , from (33), we can getCombining with formulas (31)~(36), from (30), we haveSimilarly, for formula (30) we haveBy Lemma 5, according to Jensen’s inequality, we have thatCombining with formulas (25)~(30), from (23), we havewhereBy Schur complement and formula (26), we havewhereConsequently, it follows from inequality (20), and , and we have , which implies that (9) holds.
Thus, performance is verified. In addition, when the zero disturbance input , by Schur complement, we can obtain that the time derivative of Lyapunov-Krasovskii ; that means that the filtering error system (7) with is asymptotically stable.

4. Fuzzy Filter Design

Theorem 6 provides a sufficient condition for filter design with time delay and satisfied the performance. However, there exist some coupled matrix variables in the matrix inequality (15); filter parameter can not be calculated directly. In order to decouple the variables in (15), we will use decoupling technique. Using this method, inequality (15) can be equivalently expressed in another form; hence, we can obtain filter parameter.

Theorem 7. For given scalars ,  ,  ,  , , ,  ,  ,  ,  ,  ,  , and , the filtering error system (6) is asymptotically stable as well as with the performance level , if there exist matrices ,  ,  ,  ,  ,  ,  ,  , and such that the following linear matrix inequalities are satisfied:where