The nonfragile H∞ filtering problem for a kind of Takagi-Sugeno (T-S) fuzzy stochastic system which has a time-varying delay and parameter uncertainties has been studied in this paper. Sufficient conditions for stochastic input-to-state stability (SISS) of the fuzzy stochastic systems are obtained. Attention is focused on the design of a nonfragile H∞ filter such that the filtering error system can tolerate some level of the gain variations in the filter and the H∞ performance level also could be satisfied. By using the SISS result, the approach to design the nonfragile filter is proposed in terms of linear matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.
1. Introduction
As the performance of a control system is affected by parameter perturbations, exogenous disturbances, measurement errors, and other uncertainties, the research of the robust control problem has had a vital status in the studies of control theory. Considering Lyapunov stability theory which is not suitable for analyzing and processing the state responses of the system with perturbations, some new methods have been developed, such as input-to-state stability (ISS). Since Sontag presented the qualitative aspect of ISS for the system response to input with bounded magnitude in 1989 [1], ISS has become an essential concept in modern controller and filter design for the nonlinear system. The ISS problem has been extensively investigated by many authors [2–6] until now. At the same time, ISS has been studied for stochastic systems. Stochastic input-to-state stability (SISS) of Lure distributed parameter control system has been investigated in [7], and sufficient conditions for SISS in Hilbert space have been presented in terms of linear operator inequalities. In [8], SISS and the H∞ filtering problem have been considered, and the filter has been designed in LMIs. A mean-square exponential ISS problem for stochastic delay neural networks has been investigated in [9].
On the other hand, fuzzy model could turn the nonlinear models into a linear representation by partitioning the original dynamic differential equations into linear ones [10]. T-S fuzzy model [11] has been considered as an efficient technique to linearize the nonlinear systems. This model has been first put forward in the truck trailer system [12]. And another typical application is in the stirred tank reactor system which has been addressed in [13]. Until now, there have been a lot of results of T-S fuzzy system reported in literature. The stability and control problem have been investigated in [14–19] and the references therein.
Meanwhile, it is well known that state estimation can estimate the unavailable state variables or their linear combination for a given system [20, 21], and it has been found in many practical applications over decades. As a branch of state estimation theory, H∞ filter can process the estimation problem without exact statistical data for the external noise. This problem for the T-S fuzzy system has been addressed in [22–27]; and the robust filters for stochastic systems are designed in [28, 29]. During the filter design, gain perturbations are usually unavoidable. According to [30], those gain perturbations could destabilize the filtering error system even if they are very small, which makes the filter fragile. Hence, it is reasonable to design a filter that could tolerate some level of the gain variations, which is called nonfragile filter. The nonfragile filter has received considerable attention over the past two decades; refer to [31–34] and the references therein. From what is mentioned above, it is worth noting that T-S fuzzy model can be used to divide the nonlinear stochastic systems into several subsystems. The solution to fuzzy stochastic differential equations with local martingales has been presented in [35]. The work in [36] has considered the robust fault detection problem for T-S fuzzy stochastic systems. And the stabilization for the fuzzy stochastic systems with delays has been investigated in [37–39]. The control problem has been considered in [40–45].
Motivated by the above discussion, this paper will focus on the filter design for the fuzzy stochastic system, where few results have been found. The nonfragile fuzzy delay-dependent H∞ filter design for a T-S time-delay fuzzy stochastic system with norm-bounded parameter uncertainties is studied in this paper. The Lyapunov-Krasovskii functional technique is used and the sufficient conditions obtained are expressed in terms of linear matrix inequality (LMI) approach. This paper is organized as follows. Section 2 presents the problem formulation and preliminaries. Section 3 gives main results for the nonfragile filter design. In Section 4, a numerical example is shown to illustrate the effectiveness of the proposed methods. Section 5 concludes the paper.
Notation. The notation used in this paper is fairly standard. The superscript “T” stands for matrix transposition. Throughout this paper, for real symmetric matrices X and Y, the notation X≥Y (resp., X>Y) means that the matrix X-Y is positive semidefinite (resp., positive definite). Rn denotes the n-dimensional Euclidean space and Rm×n denotes the set of all m×n real matrices. I stands for an identity matrix of appropriate dimension, while In∈Rn denotes a vector of ones. The notation * is used as an ellipsis for terms that are induced by symmetry. diag(⋯) stands for a block-diagonal matrix. |·| denotes the Euclidean norm for vectors and · denotes the spectral norm for matrices. L2[0,∞) represents the space of square-integrable vector functions over [0,∞). E(·) stands for the mathematical expectation operator. Matrix dimensions, if not explicitly stated, are assumed to be compatible for algebraic operations.
2. Problem Formulation and Preliminaries
Consider the time-delay T-S fuzzy stochastic system with time-varying parameter uncertainties in the following form:
(1)Σ:dxt=∑i=1rhistψidt+kidωt,dy(t)=∑i=1rhistCixt+Cdixt-τt+Divtdt,z(t)=∑i=1rhistLi+ΔLitxt,xt=φt,t∈-h2,0;
where x(t)∈Rm is the state; ψi=(Ai+ΔAi(t))x(t)+(Adi+ΔAdi(t))x(t-τ(t))+(Bi+ΔBi(t))v(t)+Eig(x(t)), ki=(Hi+ΔHi(t))x(t)+(Hdi+ΔHdi(t))x(t-τ(t)); φ(t) is a given real-value initial function on [-h2,0]; ω(t) is a scalar zero mean Gaussian white noise process with unit covariance; y(t)∈Rn is the measured output; z(t)∈Rl is a signal to be estimated; v(t)∈Rs is the input noise signal which belongs to L2[0,∞); τ(t) is a continuous differentiable function representing the time-varying delay in x(t), which is assumed to satisfy 0≤h1≤τ(t)<h2 for all t≥0; and the real nonlinear function g(x(t)) satisfies a linear-type growth condition and local Lipschitz condition g(x(t))2≤lx(t)2 and g(x1(t))-g(x2(t))2≤κx1(t)-x2(t)2, where l and κ are two known positive constant scalars. And using the fuzzy theory, there always exists, for all t, hi(s(t))≥0, i=1,2,…,r, ∑i=1rhi(s(t))=1.
In the above nonlinear fuzzy stochastic system, Ai, Adi, Bi, Ei, Hi, Hdi, Ci, Cdi, Di, and Li are known constant matrices with appropriate dimensions. ΔAi(t), ΔAdi(t), ΔBi(t), ΔHi(t), ΔHdi(t), and ΔLi(t) represent the unknown time-varying parameter uncertainties and are assumed to satisfy
(2)ΔAi(t)ΔAdi(t)ΔBi(t)ΔHi(t)ΔHdi(t)ΔLi(t)=M1iM2iFi(t)N1iN2iN3i,
where M1i, M2i, N1i, N2i, and N3i are known real constant matrices and the unknown time-varying matrix function satisfying Fi(t)TFi(t)≤I for all t.
Now, we consider a dynamical nonfragile fuzzy filter for system (Σ):
(3)dx^t=∑i=1rhi(s(t))Afi+ΔAfitx^(t)dtpppppppippp+Bfi+ΔBfitdyt+Efigx^t,z^t=∑i=1rhistLfi+ΔLfitx^t,
in which the fuzzy rules have the same representations as in (1). Consider x^(t)∈Rn and z^(t)∈Rl. Afi, Bfi, Lfi, and Efi are the filters needed to be determined. ΔAfi(t), ΔBfi(t), and ΔLfi(t) represent the unknown time-varying parameter uncertainties and are assumed to satisfy
(4)ΔAfi(t)ΔBfi(t)ΔLfi(t)=M4iFai(t)N4iN5iN6i,
where M4i, N4i, N5i, and N6i are known real constant matrices and the unknown time-varying matrix function satisfying Fai(t)TFai(t)≤I for all t.
Remark 1.
There are two approaches to design the filter for fuzzy systems. One is dependent on the fuzzy rules when the fuzzy model is available while the other one is independent of the fuzzy rules. In this paper, we choose the first approach since the fuzzy model is known here and this approach is less conserve. So the nonfragile fuzzy rule-dependent filter is investigated in this paper as in (3).
Let ξ(t)=[x(t)Tx^(t)T]T and z~(t)=z(t)-z^(t).
The filtering error dynamic system can be written as
(5)Σ~:dξt=Φtdt+Ktdωt,z~(t)=L~+ΔL~tξ(t),
where
(6)Φt=(A~+ΔA~(t))ξ(t)+A~d+ΔA~dtGξt-τt+B~+ΔB~tv(t)+E~gxt,Kt=H~+ΔH~tξt+H~d+ΔH~dtGξt-τt,A~=A¯0B¯fC¯A¯f,A~d=A¯dB¯fC¯d,H~=H¯000,E~=E¯00E¯f,ΔA~t=ΔA¯(t)0ΔB¯f(t)C¯ΔA¯f(t),ΔA~d(t)=ΔA¯d(t)ΔB¯f(t)C¯d,ΔB~=ΔB¯(t)ΔB¯f(t)D¯,ΔH~t=ΔH¯(t)000,ΔH~d(t)=ΔH¯d(t)0,H~d=H¯d0,B~=B¯B¯fD¯,A¯=∑i=1rhi(s(t))Ai,A¯d=∑i=1rhi(s(t))Adi,ΔA¯(t)=∑i=1rhistΔAit,H¯=∑i=1rhistHi,H¯d=∑i=1rhistHdi,ΔA¯d(t)=∑i=1rhistΔAdi(t),B¯=∑i=1rhi(s(t))Bi,D¯=∑i=1rhi(s(t))Di,L~=L¯-L¯f,G=I0,E¯=∑i=1rhi(s(t))Ei,L¯=∑i=1rhi(s(t))Li,ΔL~(t)=ΔL¯(t)-ΔL¯f(t),C¯=∑i=1rhi(s(t))Ci,C¯d=∑i=1rhistCdi,ΔB¯t=∑i=1rhistΔBit,A¯f=∑i=1rhistAfi,B¯f=∑i=1rhistBfi,L¯f=∑i=1rhistLfi,E¯f=∑i=1rhistEfi,ΔH¯(t)=∑i=1rhi(s(t))ΔHi(t),ΔH¯d(t)=∑i=1rhi(s(t))ΔHdi(t),ΔA¯f(t)=∑i=1rhi(s(t))ΔAfi(t),ΔB¯f(t)=∑i=1rhi(s(t))ΔBfi(t),ΔL¯(t)=∑i=1rhi(s(t))ΔLi(t),ΔL¯ft=∑i=1rhistΔLfit.
We intend to design a dynamical nonfragile fuzzy filter in the form of (3) in this paper, such that, for any scalar 0≤h1<h2 and a prescribed level of noise attenuation γ>0, the filtering error system (Σ~) could be stochastic input-to-state stability and the error system (Σ~) satisfies H∞ performance.
Now, we present the definitions and lemmas used in this paper, which help to complete the proof of the main results.
Definition 2 (see [46]).
In system (Σ), a continuously differentiable function V(x,t)∈ϑ2,1(Rn×R+;R+) is called a SISS Lyapunov function, if there exist functions α1,α2,α3, and α4∈K∞, such that
(7)α1x≤Vx,t≤α2x,LV≤-α3(x)+α4v,
for any x∈Rn and u∈L∞, where
(8)LV(t,x)=Vt(t,x)+Vx(t,x)f+12trhTVxxt,xh,f=Ax(t)+Adx(t-τ)+Bv(t)+Elgxt,h=Dxt+E2xt-τ.
Definition 3 (see [31]).
The robust stochastic stable system (Σ~) is said to satisfy the H∞ performance; for the given scalar γ>0 and any nonzero v(t)∈L2[0,∞), the system (Σ~) satisfies
(9)z~t2<γvt2.
Lemma 4 (see [8]).
The system (Σ) is SISS if there exists an SISS-Lyapunov function.
3. Robust Stochastic StabileTheorem 5.
The filtering error system (Σ~) is SISS with an H∞ attenuation level γ>0, if there exist matrices P=PT>0, S>0, Rj=RjT>0, j=1,2,3, Qi=QiT>0, T1i, T2i, i=1,2, such that the following matrix inequality holds:
(10)Ψ=ΩΨ12Ψ13Ψ14Ψ15*Ψ22000**Ψ3300***-I0****-I<0,
where
(11)Ω=Ω1100Ω140P(B~+ΔB~(t))*Ω220Ω2400**Ω33Ω3400***Ω4400****Ω550*****-γ2I,Ψ12=h2-h1AˇTGTR1h2-h1HˇTGTR2HˇTP,Ψ22=diag{-(h2-h1)R1,-(h2-h1)R2,-P},Ψ13=T~1T~2T~1T~2,Ψ33=diag{-R2,-R2,-(h2-h1)R1,-(h2-h1)R1},Ψ14=L~00000T,Ψ15=E~TP00000T,Ω11=PA~+ΔA~t+A~+ΔA~tTP+GTQ1+Q2+h2-h1R3G+l+S,Ω14=PA~d+ΔA~dt,Ω22=-Q1+T2+T2T,Ω24=-T2+T2T,Ω33=-Q2-T1-T1T,Ω34=T1-T1T,Ω44=-T2-T2T+T1+T1T,Ω55=-R3h2-h1,T~1=00T1TT1T00T,T~2=0T2T0T2T00T,A˘=A~+ΔA~(t)00A~d+ΔA~d(t)0B~+ΔB~(t)T,H˘=H~+ΔH~(t)00H~d+ΔH~d(t)00T.
Proof.
Choose a Lyapunov-Krasovskii candidate for system (Σ~) as follows:
(12)Vξt,t=ξT(t)Pξ(t)+∫-h2-h1∫t+βtΦTsGTR1GΦsppppppippppp+KTsGTR2GKsdsdβ+∑i=12∫t-hitξT(s)GTQiGξ(s)ds+∫-h2-h1∫t+βtξTsGTR3Gξsdsdβ.
Let λ_(P)=λmin(P), λ¯(P)=λmax(P), λ¯(Qi)=λmax(Qi), i=1,2, and λ¯(Rj)=λmax(Rj), j=1,2,3; then there exists a scalar νj, j=1,2,3, such that
(13)λ_(P)ξ(t)2≤ξT(t)Pξ(t)≤λ¯(P)ξ(t)2,0≤∫t-hitξT(s)GTQiGξ(s)ds≤hiλ¯(Q)ξ(t)2,i=1,2,0≤∫-h2-h1∫t+βtΦT(s)GTR1GΦ(s)dsdβ≤ν1λ¯(R)ξ(t)2,0≤∫-h2-h1∫t+βtKT(s)GTR2GK(s)dsdβ≤ν2λ¯(R)ξ(t)2,0≤∫-h2-h1∫t+βtξTsGTR3Gξsdsdβ≤ν3λ¯Rξt2.
It follows that
(14)λ_Pξt2≤V(ξ(t),t)≤λ¯(P)ξt2+h1λ¯(Q)ξt2+h2λ¯(Q)ξt2+ν1λ¯Rξt2+ν2λ¯Rξt2+ν3λ¯Rξt2.
From Itô formula, the stochastic differential equation can be computed as follows: dV(ξ(t),t)=LV(ξ(t),t)+2ξT(t)PK(t)dω(t), where
(15)LVξt,t≤2ξT(t)PΦ(t)+KT(t)PK(t)+ξT(t)GTQ1Gξ(t)+ξT(t)GTQ2Gξ(t)-ξT(t-h1)GTQ1Gξ(t-h1)-ξT(t-h2)GTQ2Gξ(t-h2)+(h2-h1)ΦT(t)GTR1GΦ(t)+KtTGTR2GK(t)oooooooooooooo+ξT(t)GTR3GΦ(t)-∫t-h2t-h1ξT(s)GTR3Gξ(s)+ΦT(s)GTR1GΦ(s)ooooooooooo+KT(s)GTR2GK(s)ds+2ηT(t)T¯2Gξ(t-h1)-ξ(t-τ(t))∫t-τ(t)t-h1ooooooooooooooo-∫t-τ(t)t-h1Φ(s)ds-∫t-τ(t)t-h1K(s)dω(s)+2ηT(t)T¯1Gξ(t-τ(t))-ξ(t-h2)∫t-τ(t)t-h1ooooooooooooooo-∫t-h2t-τ(t)Φ(s)ds-∫t-h2t-τ(t)K(s)dω(s)+(τ(t)-h1)ηT(t)T¯2R1-1T¯2Tη(t)-∫t-τ(t)t-h1ηT(t)T¯2R1-1T¯2Tη(t)ds+(h2-τ(t))ηT(t)T¯1R1-1T¯1Tη(t)-∫t-h2t-τ(t)ηT(t)T¯1R1-1T¯1Tη(t)ds,
where
(16)ηTt=ξT(t)ξT(t-h1)GTξT(t-h2)GT∫t-h2t-h1ξsTds∫t-h2t-h1ξds222ξTt-τtGT∫t-h2t-h1ξsTdsGTv(t).
And the final eight lines of (15) are equal to 0 from the Newton-Leibnitz formula.
Remark 6. In the proof of the theorem, we adopt Newton-Leibnitz formula to reduce the conservatism. Moreover, the results obtained in this theorem can be further extended based on fuzzy or piecewise Lyapunov-Krasovskii function.
Now, it is easy to see that
(17)2ξTtPΦt=2ξT(t)PA~+ΔA~tξtppppppppp+A~d+ΔA~dtGξt-τtppppppppp+B~+ΔB~tvt+ξT(t)PE~E~TPξ(t)+gξtTg(ξ(t))≤2ξT(t)P(A~+ΔA~(t))ξ(t)ppppppppp+A~d+ΔA~dtGξt-τtppppppppp+B~+ΔB~tvt+ξT(t)(PE~E~TP+lI)ξ(t).
Moreover,
(18)-∫t-h2t-h1ξT(s)GTR3Gξ(s)ds≤-1h2-h1∫t-h2t-h1ξsdsTGTR3G∫t-h2t-h1ξsds.
By the above formulas (15)–(18), we can deduce that
(19)LVξt,t≤ηT(t)Ω¯+Ψ12Ψ22-1Ψ12T+Ψ13Ψ33-1Ψ13T99999999+Ψ15Ψ15Tη(t)-ξT(t)Sξ(t)+ρvT(t)v(t),
where ρ>0 is a given positive scalar and
(20)Ω¯=Ω1100Ω140PB~+ΔB~t*Ω220Ω2400**Ω33Ω3400***Ω4400****Ω550*****-ρI.
From (10) and (20), we can deduce
(21)ηTtΩ¯+Ψ12Ψ22-1Ψ12T+Ψ13Ψ33-1Ψ13T+Ψ15Ψ15Tη(t)<0,
which means
(22)LVξt,t≤-ξT(t)Sξ(t)+ρvT(t)v(t)≤-λmin(S)ξt2+ρvt2.
Together with (14), (22), and Lemma 4, the system (Σ~) is SISS.
Now, we are in the position to proof that (Σ~) satisfies an H∞ attenuation level.
By Itô’s formula, there is
(23)EVξt,t=E∫0tLVξs,sds.
Now, we consider the H∞ performance of the filtering error system (Σ~). Define J(t)=E{∫0t[z~(s)Tz~(s)-γ2v(s)Tv(s)]ds} and consider (23). It is obvious that
(24)Jt=E∫0tz~Tsz~s-γ2vTsvs+LVξs,sds-EVξt,t≤E∫0tz~Tsz~s-γ2vTsvs+LVξs,sds=E∫0tξTtL~+ΔL~tTL~+ΔL~tξt∫0tiiiiiiiiiii∫0t-γ2vTtvt+LVξs,sds.
Using the same method in (15), we can deduce the following formula:
(25)ξTtL~+ΔL~tTL~+ΔL~tξt-γ2vT(t)v(t)+LVξs,s≤ηT(t)Ω+Ψ12Ψ22-1Ψ12T+Ψ13Ψ33-1Ψ13Tppppppppp+Ψ14Ψ14T+Ψ15Ψ15Tη(t).
Then, applying the Schur complement formula to (10), there is
(26)ηTtΩ+Ψ12Ψ22-1Ψ12T+Ψ13Ψ33-1Ψ13Toooooo+Ψ14Ψ14T+Ψ15Ψ15Tη(t)<0
for all t>0. Therefore, for all η(t)≠0, J(t)<0, which means that (9) is satisfied. This completes the proof.
Remark 7.
Since not all the delays begin at 0 moments, the delay we considered here contains both the upper bound and the lower bound, which is different from most of the existing works. Instead of the [0,h) expression of the time delay, a more reliable sufficient condition is proposed in this paper.
Based on the above results, a sufficient condition for the solvability of robust H∞ filtering problem for system (Σ~) is considered in the next theorem.
Theorem 8.
Consider the uncertain T-S fuzzy stochastic time-varying delay system (Σ~) and a constant scalar γ>0. The robust H∞ filtering problem is solvable if there exist scalars ε1i>0, ε2i>0, ε3i>0, ε4i>0, and ε5i>0 and matrices P1>0, P2>0, S1>0, S2>0, Rj>0, j=1,2,3, Qi>0, T1i, T2i, i=1,2; W1i,W2i,W3i, 1≤i≤r, ϱi=ϱiT,1≤i≤r, and πij,1≤i<j≤r, such that the following LMIs hold:
(27)ϱ1π12⋯π1r*ϱ2⋯π2r⋮⋮⋱⋮**⋯ϱr<0,ςiiχ1iχ2iχ3iχ4iχ5i*-ε1i0000**-ε2i000***-ε3i00****-ε4i0*****-ε5i<0,1≤i≤r,ςijχ1iχ1jχ2iχ2jχ3iχ3jχ4iχ4jχ5iχ5j*-ε1i000000000**-ε1j00000000***-ε2i0000000****-ε2j000000*****-ε3i00000******-ε3j0000*******-ε4i000********-ε4j00*********-ε5i0**********-ε5j<0,1≤i<j≤r,
where
(28)ςii=Γii-ϱi+ε1iΞ1iΞ1iT+ε2iΞiΞ2iT+ε3iΞ3iΞ3iT+ε4iΞ4iΞ4iT+ε5iΞ5iΞ5iT,ςij=Γij+Γji-πij-πji+ε1iΞ1iΞ1iT+ε2iΞiΞ2iT+ε3iΞ3iΞ3iT+ε4iΞ4iΞ4iT+ε5iΞ5iΞ5iT+ε1jΞ1jΞ1jT+ε2jΞjΞ2jT+ε3jΞ3jΞ3jT+ε4jΞ4jΞ4jT+ε5jΞ5jΞ5jT,χ1i=M1iTP101*6(h2-h1)M1iTR101*10T,Ξ1iT=N1iT01*3N2iT0N3iT01*11,Ξ5iT=0-N6i01*16,χ2i=01*8(h2-h1)M2iTR2M2iTP101*8T,Ξ2iT=N1i01*3N2i01*13,χ3i=0M4iTP201*16T,Ξ3iT=N5iCjN4i00N5iCdj0N5iDj01*11,χ4i=01*17M2iTT,Ξ4iT=0N3i01*16,χ5i=01*17M4iTT,Γij=Γ11Γ12Γ13Γ14Γ15*Γ22000**Γ3300***-I0****-I,Γ11=G11CjTW2iT00P1Adi0P1Bi*G2200W2iCdj0W2iDj**G330-T2+T2T00***G44T1-T1T00****G5500*****-R3h2-h10******-I,G11=P1Ai+AiTP1+l+Q1+Q2+(h2-h1)R3+S1,G22=W1iT+W1i+l+S2,G33=-Q1+T2+T2T,G44=-Q2-T1-T1T,G55=-T2-T2T+T1+T1T,Γ12=(h2-h1)AiTR1(h2-h1)HiTR2HiTXHiT(h2-h1)AiTR1(h2-h1)HiTR2HiTXHiT00000000(h2-h1)AdiTR1(h2-h1)HdiTR2HdiTXHdiT0000(h2-h1)BiTR1000,Γ22=diag-h2-h1R1,-h2-h1R2,-P100-P2,Γ13=T~1T~2(h2-h1)T~1(h2-h1)T~2,Γ33=diag-R2,-R2,-h2-h1R1,-h2-h1R1,Γ14T=LiT-LfiT00000,Γ15T=P1EiT0000000W3iT00000.
When the LMIs (27) are feasible, the nonfragile filter we desired here can be chosen as
(29)Afi=P2-1W1i,Bfi=P2-1W2i,Lfi,Efi=P2-1W3i,i=1,…,r.
Proof.
Define
(30)P=P100P2,S=S100S2.
By using the same methods in [31], it can be easily proven that the condition in Theorem 5 and the LMIs in (27) are equivalent. Then, we can conclude that the filtering error system (Σ~) is SISS with H∞ performance level γ.
Remark 9.
The desired H∞ filters can be constructed by solving the LMIs in (27), which can be implemented by using standard numerical algorithms, and no tuning of parameters will be involved.
4. Numerical Example
In this section, a numerical example is provided to show the effectiveness of the results obtained in the previous section.
Example. Consider the T-S fuzzy stochastic system (Σ~) with model parameters given as follows:
(31)A1=-3.51-1.7-5.8,Ad1=-0.15-0.400.3,A2=-2.10.6-1.7-2.9,H1=-0.200-0.2,Hd1=-0.010.020.01-0.05,H2=-0.6-0.10.20.3,C1=-0.10.1,Cd1=-0.05-0.05,C2=0.2-0.4,Cd2=-0.4-0.5,L1=1.5-0.6,L2=-0.30.2,D1=0.2,D2=-0.2,B1=0.9-0.2,B2=-0.2-0.5,Ad2=-0.180-0.22-0.24,Hd2=-0.150.60.010.4.
And the parameter uncertainties are shown as
(32)M11=0.10.2-0.50.1,M12=-0.20.10.3-0.1,M21=0.8-0.1-0.10.2,N11=0-0.30.1-0.2,N21=-0.200.20.1,M22=-0.10.20.4-0.2,N12=-0.500.2-0.3,N22=0-0.200.1.
The membership functions are
(33)h1x1t=1-x11+expx1t+1,h2x1t=1-h1x1t.
By using the Matlab LMI Control Toolbox, the nonfragile robust H∞ filtering problem is solvable to Theorem 8. It can be calculated that, for any 0<h1(t)≤h2(t)≤6 and the nonlinear function g(x(t))=sin(x2(t)), the robust H∞ filtering problem can be solved with the H∞ performance level γ=0.46. And the desired fuzzy filter can be constructed as in the form of (3) with
(34)Af1=-5.43200.45111.8159-1.5495,Af2=-8.11423.49022.9687-5.9058,Bf1=-1.03010.1040,Bf2=-1.01710.0415,Lf1=-0.3063-0.0422,Lf2=-0.2667-0.0422,Ef1=0.003610.00000.00000.00361,Ef2=0.002740.000000.000000.00274.
The simulation results of the state responses in system (Σ) and the filter are given in Figure 1, where the initial conditions are x0t=0.40.3T and x^0t=0.10.1T. Figure 2 shows the simulation results of the signal z~(t).
State responses of x(t) and x^(t).
Responses of the error signal z(t)-z^(t).
5. Conclusion
This paper considers the nonfragile H∞ filter design problem for the uncertain time-delay T-S fuzzy stochastic system. Sufficient conditions have been addressed to guarantee that the system (Σ~) is SISS. An LMI approach has been developed to design the fuzzy filter ensuring a prescribed H∞ performance level of the filtering error system for all admissible uncertainties. Finally, a numerical example has been provided to show the effectiveness of the proposed filter design methods.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grants 61203048, 61304047, 11371013, and 61203047 and the foundation from Shenyang State Key Laboratory of Robotics.
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