# RobustH∞ Fault Detection for Networked Control Systems with Markov Time-Delays and Data Packet Loss in Both S/C and C/A Channels

Authors: Yan-feng Wang
Journal: Mathematical Problems in Engineering
 (2019)

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## Abstract

This paper investigates the robustH∞ fault detection problem for networked control systems with Markov time-delays and data packet loss in both S/C and C/A channels. First, the time-delay from sensor to controller (S/C) and the time-delay from sensor to actuator (C/A) are described by two different Markov chains. Two random variables obeying the Bernoulli distribution are used to describe the packet loss between the sensor and the controller together between the controller and the actuator. Based on this, a fault detection filter is constructed and the closed-loop system mathematical model is established. Then, the solution method of the fault detection filter and controller gain matrix is given. The relationship between the probability of successful packet transmission and the ability to suppress external disturbance is obtained. Finally, simulation verifies the effectiveness of the proposed method.


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## Body

## 1. Introduction

Networked control systems (NCSs) have the merits of low cost, easy expansion, and maintenance and are widely used in aerospace, telemedicine, and other fields [1, 2]. However, the introduction of the network inevitably causes time-delay, data packet loss, and other phenomena [3, 4], which makes the performance of the control system degrade and even leads to system instability. The failure analyses of NCSs are more complicated than the traditional point-to-point control system [5–7]. NCSs fault detection (FD) has received extensive attention and has achieved a lot of research results.The networks in a typical NCS exist not only between the sensor and the controller (S/C) but also between the controller and the actuator (C/A), and both networks have time-delay and data packet loss. However, most of the existing literatures only consider time-delay or data packet loss, or only consider time-delay and data packet loss in S/C or C/A channel. The existing research results on NCSs FD could be divided into the following three categories.The first category only considered the time-delay. For example, in literature [8], the time-delay was transformed into the system uncertainty matrix and the H∞ state observer was designed for NCSs. In order to make a compromise between system robustness and FD sensitivity, a threshold optimization method for FD was proposed; in literature [9], for NCSs with time-delay, the control input was treated as external input to the system, and the closed-loop systems were modeled as Markov jump linear systems. The residual generator of the system was constructed, and the FD problem is transformed into the H∞ filtering problem. The sufficient conditions for the existence of FD filter were given in the term of Linear Matrix Inequality (LMI), and the corresponding solution method was given; in literature [10], the sum of S/C delay and C/A delays was modelled as a Markov chain and closed-loop NCSs were modelled as Markov jump linear systems. The sufficient conditions for the existence of FD filter were given. The solution to the system performance optimization problem was given; in literature [11], two independent Markov chains described S/C and C/A time-delay, respectively. The H∞ FD problem of NCSs was researched under the condition that the time-delay transition probabilities were partly unknown. The FD filter was given in the terms of matrix inequalities. The solution method of filter gain matrix was also given by the idea of cone complement linearization (CCL).The second category only considered data packet loss. For example, considering the data packet loss in S/C and C/A channel, the problem of robust FD of NCSs in the presence of external disturbance was researched in literature [12]. The sufficient conditions for the existence of the FD filter gain matrix were given which made the error system to be mean square exponential stable and achieve the given robust H∞ disturbance suppression level; in literature [13], considering the packet loss in S/C and C/A channels, NCSs were modeled as Markov jump systems with four different modes, and a residual generator was constructed to convert the corresponding fault detection problem into the H∞ filtering problem. The fault detection filter was designed by using Markov jump linear system theory.The third category only considered the time-delay and data packet loss in S/C or C/A channels. For example, the FD filter was designed in literature [14], and the FD issue was transformed to the filter design problem for the Markov jump systems with time-delay, but only the time-delay and data packet loss of the S/C channel were considered; in literature [15], for a class of networked switching control systems with short time-delay and data packet loss, an observer-based residual generator was constructed. The stability of the system was analyzed by combining the Lyapunov function method and the average dwell time method. The adaptive threshold of the fault filter was given, but, for the data packet loss, only the C/A side was considered; in literature [16], for the NCSs with short time-delay and data packet loss, the sufficient conditions for the existence of the FD filter which made the closed-loop system stable and achieve a given H∞ attenuation performance were given in terms of LMI. Although the time-delays in S/C and C/A channels were considered, but the data packet loss in C/A channel was ignored.In summary, the current research on NCSs FD is still not sufficient. The FD of NCSs with time-delay and data packet loss in both S/C and C/A channels needs further research which motivates our investigation. Different from the existing literature, this paper considers time-delay and data packet loss in both S/C and C/A channels. The sufficient conditions for the existence of state feedback controller and FD filter gain matrices are obtained in the form of matrix inequalities to ensure that the closed-loop systems are stochastically stable and satisfy given theH∞ disturbance suppression level. The co-design method of the controller and fault filter is proposed.


## 2. Problem Formulations

The structure of the NCSs considered in this paper is shown Figure1 where the switch closure indicates that the packet transmission is successful, and the opening indicates that the data packet loss occurs. μ(k) and d(k) denote the S/C and C/A time-delay and take value from Υ={0,…,μ}, and Θ={0,…,d}, the transition probability matrix is G=[λij], H=[πrs], respectively, where λij and πrs are defined as λij=Probμk+1=j∣μk=i, πrs=Probdk+1=s∣dk=r, where ∑j=0μλij=1,∑s=0dπrs=1,λij≥0,πrs≥0.Figure 1
Structure of NCSs with time-delay and data packet loss in both S/C channel and C/A channels.The random variableα(k) and β(k) taking value from {0,1} denotes the data packet loss in S/C channel and C/A channel, respectively. When the random variable takes the value of 1, it indicates that the data packet transmission is successful; otherwise, it indicates that the data packet transmission fails, and the following characteristics are satisfied:Probαk=1=E{α(k)}=a,Prob{α(k)=0}=1-a,Var{α(k)}=E{(α(k)-a)2}=(1-a)a=b2,Prob{β(k)=1}=E{β(k)}=c,Prob{β(k)=0}=1-c,Var{β(k)}=E{βk-c2}=(1-c)c=e2.The NCSs state equation can be given as follows:(1)xk+1=Apxk+Bpu~k+Bddk+Bffkyk=Cpxkwhere x(k)∈Rw is the state vector, u~(k)∈Rh is the control input vector, y(k)∈Rg is the output vector, d(k)∈Rp is the external disturbance signal with limited energy, and f(k)∈Rq is the system fault signal. Ap, Bp, Bd, Bf, Cp are known real matrixes with appropriate dimensions.Considering the time-delay and data packet loss,y~(k) and u~(k) can be described as(2)y~k=αkyk-μk(3)u~k=βkuk-dkThe following FD filter is constructed at the controller side of the NCSs:(4)x^k+1=Apx^k+Bpuk+Ly~k-αky^k-μky^k=Cpx^krk=Vy~k-αky^k-μkwhere x^(k)∈Rw is the state vector of the filter, y^(k)∈Rg is the output vector of the filter, r(k)∈Rq is the residual vector, V is the residual gain matrix, and L is the filter gain matrix to be determined.Remark 1.
Due to the existence of time-delay and data packet loss in C/A channel, the control input of controlled plant isu~(k) rather than that of the filter input u(k), which makes the stability analyses complicated to some extent.The following feedback control law is used:(5)uk=Kx^kThe state estimation error and residual error are defined as follows:(6)ek=xk-x^k(7)rek=rk-fkDefine augmented vectors:(8)ϕk=xTkeTkT,ωk=dTkfTkTThe closed-loop system equations can be obtained from (1)-(7):(9)ϕk+1=A+B1KI1ϕk+αkI2LCϕk-μk+βkB2KI1ϕk-dk+Bωωkrek=αkVCϕk-μk-I3ωkϕk=υk,k∈-max⁡μ,d,…,0where(10)A=Ap00Ap,B1=0-Bp,B2=BpBp,Bω=BdBfBdBf,C=0-Cp,I1=I-I∈Rn×2n,I2=0I∈R2n×n,I3=0I∈Rq×p+q.Definition 1 ([17]).
Forω(k)=0, the closed-loop system (9) is stochastically stable, if for any initial condition ϕ(0), and any initial time-delay mode μ(0)∈Υ, d(0)∈Θ, there exists positive-definite matrix Q such that(11)E∑k=0∞ϕk2∣ϕ0,μ0,d0<ϕT0Qϕ0.The object of this paper is to design the filter (4) and the feedback control law (5) for NCSs with time-delay and data packet loss in both S/C and C/A channels such that(1)
whenω(k)=0, system (9) is stochastically stable;(2)
under system zero initial conditions, the residual errorre(k) satisfies the following H∞ performance:(12)E∑k=0∞reTkrek≤γ2E∑k=0∞ωTkωkOne step of FD is the residual evaluation stage including residual evaluation function and the threshold. In this paper, the evaluation functionJ(k) and the threshold Jth are selected as follows:(13)Jk=E∑ρ=l0l0+krTρrρ(14)Jth=supωk∈L2,fk=0E∑ρ=l0l0+L0rTρrρwhere l0 denotes the initial evaluation time instant; L0 denotes the evaluation time steps.The occurrence of fault can be detected by comparingJ(k) and Jth:(15)Jk≤Jth⇒alarmforfault,Jk>Jth⇒nofault.A lemma used in this paper to deal with matrix inequalities is given as follows.Lemma 2 ([18]).
For any positive-definite matrixR, two scalars δ,δ0 satisfying δ≥δ0≥1, and vector υ(l), one has(16)∑l=δ0δυTlR∑l=δ0δυl≤δ~∑l=δ0δυTlRυl,where δ~=δ-δ0+1.


## 3. Main Results

Theorem 1.
If there exist matricesK, L and positive-definite matrices Pi,r>0, Pj,s>0, S1>0, S2>0, S3>0, S4>0, Z1>0, Z2>0 such that(17)Φ≜Φ11∗∗∗∗Φ21Φ22∗∗∗Φ31Φ32Φ33∗∗0Z10-S1-Z1∗00Z20-S2-Z2<0where(18)Φ11=A+B1KI1TP~j,sA+B1KI1+μ2A+B1KI1-ITZ1A+B1KI1-I+d2A+B1KI1-ITZ2A+B1KI1-I+S1+S2+μ+1S3+d+1S4-Z1-Z2-Pi,r,Φ21=aI2LCTP~j,sA+B1KI1+μ2aI2LCTZ1A+B1KI1-I+d2aI2LCTZ2A+B1KI1-I+Z1,Φ22=a2+b2I2LCTP~j,sI2LC)+μ2a2+b2I2LCTZ1I2LC+d2a2+b2I2LCTZ2I2LC)-S3-2Z1,Φ31=cB2KI1TP~j,sA+B1KI1+μ2cB2KI1TZ1A+B1KI1-I+d2cB2KI1TZ2A+B1KI1-I+Z2,Φ32=cB2KI1TP~j,saI2LC+μ2cB2KI1TZ1aI2LC+d2cB2KI1TZ2aI2LC,Φ33=c2+e2B2KI1TP~j,sB2KI1+μ2c2+e2B2KI1TZ1B2KI1+dμ2c2+e2B2KI1TZ2B2KI1-S4-2Z3,P~j,s=∑j=0μ∑r=0dλijπrsPj,s,holds for all i,j∈Υ,r,s∈Θ, system (9) is stochastically stable.Proof.
Letφ(k)=ϕ(k+1)-ϕ(k), and construct the following Lyapunov function:(19)Vϕk,μk,dk≜ϕTkΞμk,dkϕk=∑ρ=14Vρϕk,μk,dk,where(20)V1ϕk,μk,dk=ϕTkPμk,dkϕk,V2ϕk,μk,dk=∑l=k-μk-1ϕTlS1ϕl+∑l=k-dk-1ϕTlS2ϕl,V3ϕk,μk,dk=∑l=k-μkk-1ϕTlS3ϕl+∑n=-μ+10∑m=k+nk-1ϕTlS3ϕl+∑l=k-dkk-1ϕTlS4ϕl+∑n=-d+10∑m=k+nk-1ϕTlS4ϕl,V4ϕk,μk,dk=∑n=-μ+10∑m=k+nk-1μφTmZ1φm+∑n=-d+10∑m=k+nk-1dφTmZ2φm.Apparently, we have Ξμ(k),d(k)>0.(21)EΔV1=EϕTk+1Pμk+1,dk+1ϕk+1∣μk=i,dk=r-ϕTkPμk,dkϕk=EA+B1KI1ϕk+aI2LCϕk-μk+αk-aI2LCϕk-μk+cB2KI1ϕk-dk+βk-cB2KI1ϕk-dkT∑j=0μ∑r=0dλijπrsPj,sA+B1KI1ϕk+aI2LCϕk-μk+αk-aI2LCϕk-μk+cB2KI1ϕk-dk+βk-cB2KI1ϕk-dk-ϕTkPi,rϕk=ϕTkA+B1KI1TP~j,sA+B1KI1ϕk+ϕTkA+B1KI1TP~j,saI2LCϕk-μk+ϕTkA+B1KI1TP~j,scB2KI1ϕk-dk+ϕTk-μkaI2LCTP~j,sA+B1KI1ϕk+ϕTk-μkaI2LCTP~j,saI2LCϕk-μk+b2ϕTk-μkI2LCTP~j,sI2LCϕk-μk+ϕTk-μkaI2LCTP~j,scB2KI1ϕk-dk+ϕTk-dkcB2KI1TP~j,sA+B1KI1ϕk+ϕTk-dkcB2KI1TP~j,saI2LCϕk-μk+ϕTk-dkcB2KI1TP~j,scB2KI1ϕk-dk+e2ϕTk-dkB2KI1TP~j,sB2KI1ϕk-dk-ϕTkPi,rϕk,where P~j,s=∑j=0μ∑r=0dλijπrsPj,s.(22)EΔV2=ϕTkS1ϕk-ϕTk-μS1ϕk-μ+ϕTkS2ϕk-ϕTk-dS2ϕk-d.EΔV3=ϕTkS3ϕk-ϕTk-iS3ϕk-i+∑l=k+1-μk+1k-1ϕTlS3ϕl-∑l=k+1-μkk-1ϕTlS3ϕl+μϕTkS3ϕk-∑l=k+1-μkϕTlS3ϕl+ϕTkS4ϕk-ϕTk-rS4ϕk-r+∑l=k+1-dk+1k-1ϕTlS4ϕl-∑l=k+1-dkk-1ϕTlS4ϕl+dϕTkS4ϕk-∑l=k+1-dkϕTlS4ϕl.It is noticed that(23)∑l=k+1-μk+1k-1ϕTlS3ϕl+∑l=k+1-dk+1k-1ϕTlS4ϕl=∑l=k+1-μk+1k-1ϕTlS3ϕl+∑l=k+1-μk+1k-μkϕTlS3ϕl+∑l=k+1-dkk-1ϕTlS4ϕl+∑l=k+1-dk+1k-dkϕTlS4ϕl≤∑l=k+1-μkk-1ϕTlS3ϕl+∑l=k+1-μkϕTlS3ϕl+∑l=k+1-dkk-1ϕTlS4ϕl+∑l=k+1-dkϕTlS4ϕl,Hence, we can get(24)EΔV3≤ϕTkS3ϕk-ϕTk-iS3ϕk-i+μϕTkS3ϕk+ϕTkS4ϕk-ϕTk-rS4ϕk-r+dϕTkS4ϕk,(25)EΔV4=Eμ2φTkZ1φk-E∑l=k-μk-1τφTlZ1φl+Ed2φTkZ2φk-E∑l=k-dk-1dφTlZ2φl=μ2(ϕTkA+B1KI1-ITZ1A+B1KI1-Iϕk+ϕTkA+B1KI1-ITZ1aI2LCϕk-μk+ϕTkA+B1KI1-ITZ1cB2KI1ϕk-dk+ϕTk-μkaI2LCTZ1A+B1KI1-Iϕk+a2+b2ϕTk-μkI2LCTZ1I2LCϕk-μk+ϕTk-μkaI2LCTZ1cB2KI1ϕk-dk+ϕTk-dkcB2KI1TZ1A+B1KI1-Iϕk+ϕTk-dkcB2KI1TZ1aI2LCk-μk+c2+e2ϕTk-dkB2KI1TZ1B2KI1ϕk-dk+d2(ϕTkA+B1KI1-ITZ2A+B1KI1-Iϕk+ϕTkA+B1KI1-ITZ2aI2LCϕk-μk+ϕTkA+B1KI1-ITZ2cB2KI1ϕk-dk+ϕTk-μkaI2LCTZ2A+B1KI1-Iϕk+a2+b2ϕTk-μkI2LCTZ2I2LCϕk-μk+ϕTk-μkaI2LCTZ2cB2KI1ϕk-dk+ϕTk-dkcB2KI1TZ2A+B1KI1-Iϕk+ϕTk-dkcB2KI1TZ2aI2LCϕk-μk+c2+e2ϕTk-dkB2KI1TZ2B2KI1ϕk-dk-E∑l=k-μk-1μφTlZ1φl-E∑l=k-dk-1dφTlZ2φl.Since(26)-E∑l=k-μk-1μφTlZ1φl-E∑l=k-dk-1dφTlZ2φl≤-E∑l=k-ik-1iφTlZ1φl-E∑l=k-μk-i-1μ-iφTlZ1φl-E∑l=k-rk-1rφlTZ2φl-E∑l=k-dk-r-1d-rφlTZ2φl.From Lemma 2, we can obtain(27)-E∑l=k-ik-1iφTlZ1φl-E∑l=k-μk-i-1μ-iφTlZ1φl≤-ϕk-ϕk-iTZ1ϕk-ϕk-i-ϕk-i-ϕk-μTZ1ϕk-i-ϕk-μ.(28)-∑l=k-rk-1rφTlZ2φl-∑l=k-dk-r-1d-rφTlZ2φl≤-ϕk-ϕk-rTZ2ϕk-ϕk-r-ϕk-r-ϕk-dTZ2ϕk-r-ϕk-d.From (21)-(28), we can get(29)EΔVϕk,μk,dk∣μk=i,dk=r≤ξTkΦξk≤-λmin-ΦξTkξk≤-δξk2≤-δϕk2,where(30)ξk=ϕTkϕTk-iϕTk-rϕTk-μϕTk-dT,δ=inf⁡λmin-Φ>0.From (29), for any T≥1, we can obtain(31)E∑k=0∞ϕk2≤1δEVϕ0,μ0,d0-1δEVϕT+1,μT+1,dT+1≤1δEVϕ0,μ0,d0=1δϕT0Ξμ0,d0ϕ0.It can be seen from Definition 1 that the closed-loop system (9) is stochastically stable which completes the proof.Corollary 2.
Whenω(k)≠0, if there exist matrices K, L and positive-definite matrices Pi,r>0, Pj,s>0, Mj,s>0, S1>0, S2>0, S3>0,S4>0, Z1>0, Z2>0,Y1>0, Y2>0 such that(32)Γ11∗∗∗∗Γ21Γ22∗∗∗Γ31Γ32Γ33∗∗Γ4100Γ44∗Γ51Γ5200-I<0(33)Pj,sMj,s=I,ZlYl=I,l∈1,2where(34)Γ11=Λ∗∗Z1-S3-2Z1∗Z20-S4-2Z2,Γ21=0Z1000Z2000,Γ22=-S1-Z1∗∗0-S2-Z2∗00-γ2∗I,Γ31=μA+B1KI1-Iμa2+b2I2LCμc2+e2B2KI1dA+B1KI1-Ida2+b2I2LCdc2+e2B2KI1,Γ32=00Bω00Bω,Γ33=-Y1∗0-Y2,Γ41=ΠA+B1KI1a2+b2I2LCc2+e2B2KI1⋮⋮⋮A+B1KI1a2+b2I2LCc2+e2B2KI1,Π=diag⁡λ00π00,…,λμμπdd,Γ44=diag⁡-M0,0,…,-Mμ,d,Γ51=0a2+b2VC0,Γ52=00-I3,Λ=S1+S2+1+μS3+1+dS4-Z1-Z2-Pi,r,hold for all i,j∈Υ,r,s∈Θ, the closed-loop system (9) satisfies the H∞ performance indicators shown in (12).Proof.
(35) E V ϕ k + 1 , μ k + 1 , d k + 1 - E V ϕ k , μ k , d k + E r e T k r e k - γ 2 E ω T k ω k < ζ T k Φ ~ ζ k .where(36)Φ~≜Φ11∗∗∗∗∗Φ21Φ~22∗∗∗∗Φ31Φ32Φ33∗∗∗0Z10-S1-Z1∗∗00Z20-S2-Z2∗0-I3TVC000Φ66,Φ~22=Φ22+VCTVC,Φ66=-γ2I+I3TI3,ςk=ξTkωTkTBy Schur complement lemma, Φ~<0 is equivalent to(37)Γ11∗∗∗∗Γ21Γ22∗∗∗Γ31Γ32Γ~33∗∗Γ4100Γ~44∗Γ51Γ5200-I<0where(38)Γ~33=-Z1∗0-Z2,Γ~44=diag⁡-P0,0…-Pμ,d.Therefore, if (37) holds, we can obtain(39)EVϕk+1,μk+1,dk+1-EVϕk,μk,dk+EreTkrek-γ2EωTkωk<0.Further, we can get(40)E∑k=0∞reTkrek≤γ2E∑k=0∞ωTkωk,which indicates that system (9) satisfies the performance index (12).
LettingZ1-1=Y1,Z2-1=Y2,Pj,s-1=Mj,s,j∈Υ,s∈Θ, we can get (32) and (33) from (37).
Due to the fact that there exist nonlinear terms in the constraints of the Corollary2, it is difficult to use the Matlab LMI toolbox directly. By use of the CCL [19, 20], it can be transformed into the following nonlinear minimization problem:
m i n  t r ( ∑ l = 1 2 Z l Y l + ∑ j = 0 μ ∑ s = 0 d P j , s M j , s ), s.t. (32), (41) and (42),(41)Pj,sIIMj,s≥0,j∈Υ,s∈Θ,(42)Zl∗IYl≥0,l∈1,2.Further, the algorithm for solving the controller and filter gain matrix is given.
Step 1. Given H∞ performance index γ=γ0, set the maximum number of iterations N.
Step 2. Solve equations (32), (41), and (42) and get a feasible solutions (Pj,s0,Mj,s0,Z10,Z20,Y10,Y20,K0,L0), and let k=0.
Step 3. Solve the following nonlinear minimization problem: mintr(∑l=12(ZlkYl+ZlYlk)+∑j=0μ∑s=0d(Pj,skMj,s+Pj,sMj,sk)) s.t. (32), (41), and (42), and let (Z1k=Z1,Y1k=Y1,Z2k=Z2,Y2k=Y2,Pj,sk=Pj,s,Mj,sk=Fj,s,Kk=K,Lk=L).
Step 4. Check if (32)-(33) is satisfied: if it is satisfied letting γ=γ-σ, σ is a proper positive integer, and letting k=k+1, go to Step 3; if the iterations exceed N, the iteration terminates.
Step 5. Check the value of γ after the iteration is terminated: if γ=γ0, then the optimization problem has no solution within the set number of iterations; otherwise γmin=γ+σ.Remark 3.
In order not to make the conclusion too complicated, this paper assumes that the elements of the transition probability matrix of time-delays are completely known. For partially unknown case, methods of separating known or unknown elements or estimating them with related inequalities can be used (see [21] for details).


## 4. Numerical Simulation

Consider the controlled plant with the following parameters:(43)AP=0.10.3-10.8,Bp=0.390.1,Bd=0.10.1,Bf=0.10.1,CP=1.50.3-0.10.4.Assume the S/C time-delay μ(k)∈Υ={0,1} and the C/A time-delay d(k)∈Θ={0,1}. The transition probability matrices are G=0.40.60.50.5, H=0.30.70.60.4. Packet transmission success probability is E{α(k)}=0.8,E{β(k)}=0.9, respectively. Assume that the initial states are x(-1)=00T, x(0)=1.8-2T, x^(-1)=00T and the network time-delay initial mode μ(0)=d(0)=0. One run of μ(k) and d(k) can been seen in Figures 2 and 3, respectively.Figure 2
S/C time delayμ(k).Figure 3
C/A time delayd(k).Given residual gainV=0.10.1, the external disturbance is a random signal with a mean of 0 and an amplitude of less than 0.01. The fault signal is(44)fk=5,k=15,…,200,According to Corollary 2, the filter gain, controller gain, and H∞ minimum attenuation level are obtained as follows:(45)L=-0.25000.3681-0.44120.3308,K=-0.06790.0323,γmin=1.0247.In addition, the relationship between the probability of successful packet transmission and the minimumH∞ attenuation level is shown in Table 1. It can be seen from the table that the greater the success probability of data packet transmission, the stronger the system disturbance suppression ability.Table 1
Relationship betweenγmin and data packet transmission success probability a,c.
a / c 0.4/0.5 0.5/0.6 0.6/0.7 0.7/0.8 γ m i n 1.0320 1.0296 1.0271 1.0262Select residual evaluation function as(k)=E{∑ρ=0krT(ρ)r(ρ)}, and the FD threshold can be obtained as Jth=supω(k)∈L2,f(k)=0E∑ρ=080rTρrρ=0.0870. The residual signal r(k) and residual evaluation function J(k) curve are shown in Figures 4 and 5, respectively.Figure 4
Residual signalr(k).Figure 5
Residual evaluation functionJ(k) and threshold Jth.It can be seen from Figure5 that the residual signal r(k) and the residual evaluation function J(k) change significantly when the fault occurs. Moreover, it can be obtained that J(16)=0.0157<Jth=0.0870<J(17)=0.1197 which means that the fault detect filter detects the fault in the second time period after the fault has occurred.


## 5. Conclusions

In this paper, the robustH∞ FD problem has been researched for NCSs with time-delay and data packet loss in both S/C and C/A channels. Two dependent Markov chains are used to describe the S/C time-delay and C/A time-delay, respectively. Two random variables obeying the Bernoulli distribution are exploited to describe the S/C data packet loss and C/A data packet loss, respectively. The closed-loop system model is obtained by the method of state augmentation. The sufficient conditions for the stochastic stability of the closed-loop systems have been obtained by the Lyapunov Stability theory. Furthermore, the solution method of controller, fault filter gain matrix, and the minimum H∞ performance indicator is proposed by the idea of CCL. The relationship between data packet loss probability and system H∞ performance is also given. From the simulation example we can see that the fault filter proposed in this paper can detect the fault timely and it is not only robust to the external disturbance but also sensitive to the fault.



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*Source: 4672862-2019-01-20.xml*