# H

Authors: Sha-sha Li; Zhan-shan Zhao; Jing Zhang; Jie Sun; Lian-kun Sun
Journal: Mathematical Problems in Engineering
 (2018)

---

## Abstract

The issue ofH∞ control for the coronary artery input time-delay system with external disturbance is of concern. To further reduce conservation, we utilize the free-matrix-based integral inequality, Wirtinger-based integral inequality, and reciprocal convex combination approach to construct Lyapunov-Krasovskii function (LKF). Then a sufficient condition for controller design which can guarantee robust synchronization the coronary artery system is represented in terms of linear matrix inequality (LMI). Finally, a numerical example is exploited to show the effectiveness of the proposed methods.


---

## Body

## 1. Introduction

As we all know, nonlinear dynamical systems have gotten increasing attraction in various fields researches [1–4]. As one of complex nonlinear dynamical systems, chaotic systems have been widely investigated because of their potential application in the model of chemical reaction, nervous systems, biological engineering systems, and so on [5–7]. Based on the feature of extreme sensitivity to initial conditions and the systems parameters for the chaotic systems, chaos synchronization has become a much more important task. The purpose of chaotic synchronization is to achieve synchronization of the slave and master systems, which leads the investigations of synchronization control to be a central topic. Up to now, many developed synchronization control schemes have been presented, such as feedback control [8], H∞ control [9], sliding mode control [10], and impulsive control [11].Nonlinear systems combined with biological engineering have become a hot issue. The intensive study of the coronary artery system as a practical biomathematical case of chaotic systems has significant meaning not only in clinic but also in engineering areas. From the clinic point of view, the purpose is to drive pathological vessel into the trajectory of healthy vessel to treat many complex cardiovascular diseases. From the engineering point of view, we demand a suitable synchronization controller to reduce undesired chaotic motion and achieve the synchronization of the two systems. In [12], to further lower the effect of external uncertainties, a chaos suppression controller was designed by sliding mode control to drive chaotic coronary artery system into the normal orbit, which can effectively reduce the probability of angina disease. A new terminal sliding mode control algorithm for synchronization of coronary artery system was proposed [13], which can ensure that trajectories of the spastic vessel asymptotically approach the ones of the normal vessel in finite time.Input time-delay is likely to exist in modeling practical coronary artery system due to the speed of different patients absorbing the drug. The presence of input time-delay could degrade system performance and it may lead to vibration or chaotic behavior [14]. Therefore, it is very reasonable and necessary not only in practical applications but also in theory to investigate chaotic systems with input time-delay. In [15], the input time-delay was dealt by free matrix zero equality approach to achieve synchronization for chaotic Lur’e systems. The chaotic finance systems with input time-delay were proposed based on Jensen inequality [16]. The above-mentioned literature is still great exploring room in practical application. It is well known that the stability and synchronization of the considered systems with time delay are often studied by introducing the LKF method, which can provide more useful state information and delay information. In order to reduce the possible conservatism of LKF method, many methods are proposed, such as the Jensen inequality [17], Wirtinger inequality [18], and free-matric-based integral inequality [19]. As a kind of earliest inequality, Jensen inequality was widely applied [20]. Because of avoiding the introduction of plentiful matrices, Jensen inequality was proposed to decrease much computational burden in [21]. In order to reduce Jensen’s gap, Wirtinger inequality was regarded as an effective technique to improve the performance of time-delay systems [22, 23]. Then, free-matric-based integral inequality [24] had a wide application in stability analysis of time-delay systems due to its less conservatism than the above proposed inequality [21–23]. In addition, reciprocally convex combination can derive a stability condition with much less decision variables, which could achieve performance behavior identical to above approaches [25].Motivated by above discussion, the issue ofH∞ control is further investigated for coronary artery input time-delay system with disturbance. The main contributions of this paper are presented as follows:(
1
) An augmented LKF including double integral and triple integral terms is constructed, which can provide more valuable time-delay information. To get lower bound of ∫t-ht∫utX(s)S1X(s)dsdu(S1>0), the Wirtinger-based integral inequality is employed.(
2
) Free-matric-based integral inequality and reciprocally convex combination are applied to reduce the enlargement in bounding the derivative of LKF as much as possible. Then, a state feedback controller subjecting to input time-delay is designed to guarantee asymptotical convergence to zero for the error system and reduce the effect of external disturbance to a prescribed H∞ performance level. It means that the abnormal chaotic behavior of a coronary artery system is suppressed to a normal periodic orbit, which effectively relieves or eliminates angina symptoms.The feasibility of the above strategy is illustrated based on a numerical example.Main Structure. Section 2 describes the problem formulation and preliminaries; Section 3 provides the main proof result which could ensure orbit of chaotic coronary artery system with external disturbance asymptotically approaching the ones of normal system. Section 4 shows that the simulation result of the error system is asymptotical to approach zero. Section 5 reaches conclusion of chaotic synchronization.Notation. Rn denotes n-dimensional Euclid space. · is the vector Euclid norm. ∗ is a diagonal of a symmetric matric. sym{Y} indicates Y+YT.


## 2. Problem Formulation and Preliminaries

The chaotic behavior of blood vessel more easily resists external disturb than the sine periodic motion; thus the chaotic state of physiology system is a protection mechanism and is beneficial for normal blood vessel [26]. It is not always harmful and even is desired goal in some practical applications. In this paper, the main goal is to make the biomedical model of pathological blood vessel synchronize with a prescribed chaotic system of the normal vessel [27]. From the biological point of view, the purpose is to achieve synchronization between the chaotic states of pathological blood vessel and normal one. Some chaotic conditions may cause coronary artery obstruction and have an effect on transport of nutrients and oxygen to the heart which could result in coronary atherosclerosis and angina. In order to decrease those diseases morbidity, the synchronization of coronary artery system is investigated. Describe the mathematical model of the coronary artery system as follows in [12]:(1)x˙1t=-cx1-dx2,x˙2t=-τ+cτx1-τ+dτx2+τx13+Ecos⁡ωt,where x1 and x2 are state vector, which represent deviation of inner radius and pressure of vein, t is time variable, c, d, and τ are the coronary artery system parameters which satisfy cτ-τd>0, and more information can be obtained from [28]. The variation of the blood vessel radius and change of blood pressure could be affected with different c and d; chaos can be characterized by the static rheological characteristics of blood vessels caused with the change of τ [12]. Ecos⁡ωt is disturbance term, which represents a period disturbance acting on the blood vessel from biological significance [29]. To simplify form of system (1), the healthy coronary artery system is presented as follows:(2)x˙mt=Axmt+Cfxmt+vt,where matrices A and C depend on the value of c, d, and τ, xm(t)=[x1,x2], f(xm(t))=[0,x13], v(t)=[0,Ecos⁡ωt].The blood vessel in chaotic states may induce various complex diseases, for instance, myocardial infarction. With the purpose of suppressing the chaotic phenomenon of coronary artery system, we could design the feedback controlleru(t) to achieve the chaotic synchronization. The controller u(t) is the dosage of the single and double nitrate isosorbide nitrate used for treatment of angina and other diseases [12]. We control the dosage of the single and double nitrate isosorbide nitrate to change blood vessel inner radius and pressure and achieve chaotic synchronization of normal and abnormal blood vessel, which can improve transport of nutrients and oxygen to the heart. The diseased system can be defined as the follows:(3)x˙st=Axst+Cfxst+vt+Dϱt+ut-rt,where xs(t)=[xs1(t),xs2(t)] is the state vector, f(xs(t))=[0,xs13(t)] is nonlinear function vector, v(t)=[0,Ecos⁡ωt] represents period disturbance, ϱ(t)=[ϱ1(t),ϱ2(t)] represents uncertain external disturbance. Our intent is to synchronize the healthy and pathological coronary artery system, which means resulting tracking error need be driven to zero. Hence, defining e(t)=xs(t)-xm(t), the error system based on (2) and (3) can be presented as follows:(4)e˙t=Aet+ut-rt+Cft+Dϱt,where input time-delay r(t)∈[rm,rM], its derivative r˙(t)≤rd, rm, rM, and rd are given positive integers, f(t)=f(xs(t))-f(xm(t)). Due to the time of drug absorption, we design the state feedback controller u(t-r(t))=K[xs(t-r(t))-xm(t-r(t))] to achieve the synchronization of the normal and pathological coronary artery system. The error system can be obtained:(5)e˙t=Aet+Ket-rt+Cft+Dϱt.In the paper, a controller obtained by feasible sets ofK is designed to deal with the synchronization problem of chaotic coronary artery system. In other words, our goal is to drive pathological vessel into the trajectory of healthy vessel and ensure asymptotically approaching zero for the error system. To achieve the above proposed aim, we give the following assumption and lemmas.Assumption 1.
A constant matrixL with appropriate dimension satisfies(6)fxst-fxmt≤Lxst-xmt.From (4), the following inequality can be obtained:(7)fTtft≤LTLeTtet.Lemma 2 (see [24]).
h is a continuously differentiable function: [ϑ1,ϑ2]→Rn. For symmetric matrices R∈Rn∗n, M1,M3∈R3n∗3n, and any matrices M2∈R3n∗3n, N1,N2∈R3n∗n are satisfying(8)M1M2N1∗M3N2∗∗Q≥0.We can obtain the following inequality:(9)-∫ϑ1ϑ2h˙TuRh˙udu≤ω1Tϑ2-ϑ1M1+13M3ω1+symω1TN1ω2+ω1TN2ω3,where(10)ω1=hTϑ2,hTϑ1,1ϑ2-ϑ1∫ϑ1ϑ2hTuduT,ω2=hTϑ2-hTϑ1,ω3=2ϑ2-ϑ1∫ϑ1ϑ2hTudu-hTϑ2-hTϑ1.Lemma 3 (see [30]).
Order matrixV=VT≥0 and continuously differentiable function h in [e,f]→Rn. The following inequality holds:(11)∫efh˙TuVh˙udu≥1f-eγ1Vγ1+γ2Vγ2,where(12)γ1=hf-he,γ2=3e+3f-23f-e∫efhudu.Lemma 4 (see [25]).
In an open subsetD of Rm, order positive value Li:Rm→R. The reciprocally convex combination of Li is satisfying(13)minbi∣bi>0,∑ibi=1∑i1biLit=∑iLit+maxKi,jt∑i≠jsubjecttoKi,j:Rm⟶R,Ki,j≜Ki,jt,LitKi,jtKi,jtLjt≥0.Lemma 5 (see [22]).
Order matrixW=WT≥0. For continuously differentiable function h∈[b1,b2]→Rn, we can obtain(14)∫b1b2∫sb2h˙TuWh˙ududs≥2b2-b12ν1Tb1,b2Wν1b1,b2+ν2Tb1,b2Wν2b1,b2,where(15)ν1b1,b2=b2-b1hb2-∫b1b2hsds,(16)ν2b1,b2=2d2-d12hb2+2∫b1b2hsds-32d2-d1∫b1b2∫sb2hududs.


## 3.H∞ Control for Synchronization of Coronary Artery System

In this section, a novel synchronization criterion will be mentioned on chaotic coronary artery system, which can ensure that the error system asymptotically converges to the zero. In other words, the synchronization will be achieved between the normal and diseased blood vessels based on the following theorem.Theorem 6.
Considering the states of error system (5) and Assumption 1, the error system (5) is asymptotical convergence to zero if there exist positive values rm, rM, rd, κ12, κ13, κ14, κ15, positive symmetric matrices P, R1, R2, R3, Q1, Q2, Q3, Q4, symmetric matrices M1, M3, and appropriate dimensions matric M2, N1, N2, S1, S2, L, such that(17)P=P→11κ12P→11κ13P→11κ14P→11κ15P→11∗P→22P→23P→24P→25∗∗P→33P→34P→35∗∗∗P→44P→45∗∗∗∗P→55≥0P∈5n∗5n,(18)Ψrm2νTrMmνTrm32νTrMm32νTP→11P→11LT∗-P→11Q1-1P→1100000∗∗-P→11Q2-1P→110000∗∗∗-P→11Q3-1P→11000∗∗∗∗-P→11Q4-1P→1100∗∗∗∗∗-I0∗∗∗∗∗∗-I<0,(19)M~1M~2N~1∗M~3N~2∗∗Q~1≥0,(20)Q~2S~d∗Q~2≥0,d=1,2,where(21)Ψ=∑i=15Υ~iTF~i+F~iTΥ~i+R~+H~+rm4ω1TM1+13M3ω1+rm3symω1TN1ω2+ω1TN2ω3+∑i=47ωiTQ~2ωi-ω4TS~1ω6-ω6TS~1Tω4-ω5TS~2ω7-ω7TS~2Tω5-rm2∑i=89ωiTQ~3ωi-rMm2∑i=1011ωiTQ~4ωi,P→11=P11-1,Υ~1=ν=AP→11n1+KP→11n3+Cn11+Dn12,Υ2~=n1-n2,Υ3~=n2-n4,Υ~4=rmn1-n5,Υ~5=rMmn2-n6,F~1=n1I+κ12n5I+κ13n6I+κ14n7I+κ15n8I,F~2=κ12P→11n1+P→22n5+P→23n6+P→24n7+P→25n8,F~3=κ13P→11n1+P→23Tn5+P→33n6+P→34n7+P→35n8,F~4=κ14P→11n1+P→24Tn5+P→34Tn6+P→44n7+P→45n8,F~5=κ15P→11n1+P→25Tn5+P→35Tn6+P→45Tn7+P→55n8,R~=diag⁡R~1+R~2+R~3,-R~2,-1-rdR~1,-R~3,0,0,0,0,0,0,0,0,H~=H1+H2=I+LTL,0,0,0,0,0,0,0,0,0,-I,-β2I,ω1=n1,n2,n5T,ω2=n1-n2,ω3=2n5-n1-n2,ω4=n2-n3,ω5=3n2+3n3-23n9,ω6=n3-n4,ω7=3n3+3n4-23n10,ω8=rmn1-n5,ω9=2rm2n1+2n5-32rmn7,ω10=rMmn2-n6,ω11=2rMm2n2+2n6-32rMmn8,rMm=rM-rm,nkk=1,…,12∈R12n∗ne.g.n2=0,0,I,0,0,0,0,0,0,0,0,0.Proof.
LKF can be constructed as follows:(22)Vt=∑i=14Vit,where(23)V1=ϖTtPϖt,(24)V2=∫t-rtteTsR1esds+∫t-rmteTsR2esds+∫t-rMteTsR3esds,(25)V3=rm3∫-rm0∫t+ste˙TuQ1e˙ududs+rMm∫-rM-rm∫t+ste˙TuQ2e˙ududs,(26)V4=rm42∫t-rmt∫st∫ute˙TvQ3e˙vdvduds+rMm42∫t-rMt-rm∫st-rm∫ute˙TvQ4e˙vdvduds,(27)ϖTt=et,d1t,d2t,d3t,d4t,d1t=∫t-rmteTsds,d2t=∫t-rMt-rmeTsds,d3t=∫t-rmt∫steTududs,d4t=∫t-rMt-rm∫st-rmeTududs.Based on the above condition, the derivative of V(t) can be obtained:(28)V˙t=∑i=14V˙it,where(29)V˙1t=2ϖ˙TtPϖt=2e˙TtP11et+κ12P11d1+κ13P11d2+κ14P11d3+κ14P11d4+2α1Ttκ12P11Tet+P22d1t+P23d2t+P24d3t+P25d4t+2α2Ttκ13P11Tet+P23Td1t+P33d2t+P34d3t+P35d4t+2α3Ttκ14P11Tet+P24Td1t+P34Td2t+P44d3t+P45d4t+2α4Ttκ15P11Tet+P25Td1t+P35Td2t+P45Td3t+P55d4t=ηTt∑i=15symΥiTFiηt,(30)V˙2t=∑i=13eTtRiet-1-r˙teTt-rtR1et-rt-eTt-rmR2et-rm-eTt-rMR3et-rM≤ηTtRηt,(31)V˙3t=e˙Ttrm4Q1+rMm2Q2e˙t+∑i=13ξi=ηTtνTW1νηt+∑i=13ξi,(32)V˙4t=rm42∫t-rmt∫ste˙TtQ3e˙t-e˙TuQ3e˙ududs+rMm42∫t-rMt-rm∫st-rme˙TtQ4e˙t-e˙TuQ4e˙ududs=ηTtνTW2νηt+∑i=45ξi,with(33)ηTt=eTt,eTt-rm,eTt-rt,eTt-rM,d1t,d2t,d3t,d4t,d5t,d6t,fTt,ϱTt,α1=et-et-rm,α2=et-rm-et-rM,α3=rmet-d1t,α4=rMmet-rm-d2t,Υ1=An1+A1n3+Cn11+Dn12,Υ2=n1-n2,Υ3=n2-n4,Υ4=rmn1-n5,Υ5=rMmn2-n6,F1=P11n1+κ12P11n5+κ13P11n6+κ14P11n7+κ15P11n8,F2=κ12P11Tn1+P22n5+P23n6+P24n7+P25n8,F3=κ13P11Tn1+P23Tn5+P33n6+P34n7+P35n8,F4=κ14P11Tn1+P24Tn5+P34Tn6+P44n7+P45n8,F5=κ15P11Tn1+P25Tn5+P35Tn6+P45Tn7+P55n8,R=diag⁡R1+R2+R3,-R2,-1-rdR1,-R3,0,0,0,0,0,0,0,0,W1=rm4Q1+rMm2Q2,W2=rm64Q3+rMm64Q4,ν=An1+Kn3+Cn11+Dn12,ξ1=-rm3∫t-rmte˙TsQ1e˙sds,ξ2=-rMm∫t-rtt-rme˙TsQ2e˙sds,ξ3=-rMm∫t-rMt-rte˙TsQ2e˙sds,ξ4=-rm42∫t-rmt∫ste˙TuQ3e˙ududs,ξ5=-rMm42∫t-rMt-rm∫st-rme˙TuQ4e˙ududs,d5t=1rt-rm∫t-rtt-rmeTsds,d6t=1rM-rt∫t-rMt-rteTsds. According to Lemmas 2 and 3, we can get(34)M1M2N1∗M3N2∗∗Q1≥0,(35)ξ1t≤rm4υ1Tt-rm,tM1+13M3υ1t,t-rm+rm3symυ1Tt-rm,tN1υ2t-rm,t+υ1Tt-rm,tN2υ3t-rm,t,(36)ξ2t≤-1θ1υ4TtQ2υ4t+υ5TtQ2υ5t,(37)ξ3t≤-1θ2υ6TtQ2υ6t+υ7TtQ2υ7t,where(38)θ1=rt-rmrMm,θ2=rM-rtrMm,υ1=eTt,eTt-rm,1rm∫t-rmteTsds,υ2=eTt-eTt-rm,υ3=2rm∫t-rmteTsds-eTt-eTt-rm,υ4=et-rm-et-rt,υ5=3et-rm+3et-rt-23d5t,υ6=et-rt-et-rM,υ7=3et-rt+3et-rM-23d6t. From inequality (35), we have(39)ξ1t≤ηTtrm3rmω1TM1+13M3ω1+symω1TN1ω2+ω1TN2ω3ηt. The real numbers θ1, θ2 satisfy θ1>0, θ2>0, and θ1+θ2=1. Then appropriate dimensions matrices S1, S2 are introduced, such that(40)Q2S1∗Q2≥0,Q2S2∗Q2≥0.Applying Lemma 4 to inequalities (36) (37), we have(41)ξ2t+ξ3t≤-1θ1υ4TtQ2υ4t+1θ2υ6TtQ2υ6t-1θ1υ5TtQ2υ5t+1θ2υ7TtQ2υ7t≤-υ4tυ6tTQ2S1∗Q2υ4tυ6t-υ5tυ7tTQ2S2∗Q2υ5tυ7t=-ηTt∑i=47ωiTQ2ωi+ω4TS1ω6+ω6TS1Tω4+ω5TS2ω7+ω7TS2Tω5ηt. Using Lemma 5, ξ4(t), ξ5(t) can be transformed as follows:(42)ξ4t≤-rm2τ1tτ2tTQ30∗Q3τ1tτ2t=-ηTtrm2∑i=89ωiTQ3ωiηt,ξ5t≤-rMm2τ3tτ4tTQ40∗Q4τ3tτ4t=-ηTtrMm2∑i=1011ωiTQ4ωiηt,where(43)τ1t=rmet-d1t,τ2t=2rm22t+2d1t-32rmd3t,τ3t=rMmet-rm-d2t,τ4t=2r-Mm2et-rm+2d2t-32rMmd4t,Next, we define a performance index J(e(t),ϱ(t)) to study H∞ performance:(44)Jet,ϱt=∫0∞eTueu-β2ϱTuϱudu.Under the zero initial conditions, we can obtain(45)Jet,ϱt=∫0∞eTtet-β2ϱTtϱt+V˙tdt-Vtt→∞≤∫0∞eTtet-β2ϱTtϱt+V˙tdt=∫0∞ηTtΔ+νTW1+W2νηtdt,where(46)Δ=∑i=15ΥiTFi+FiTΥi+R+H1+H2+rm4ω1TM1+13M3ω1+rm3symω1TN1ω2+ω1TN2ω3+∑i=47ωiTQ~2ωi-ω4TS1ω6-ω6TS1Tω4-ω5TS2ω7-ω7TS2Tω5-rm2∑i=89ωiTQ3ωi-rMm2∑i=1011ωiTQ4ωi,H1=diag⁡LTL,0,0,0,0,0,0,0,0,0,0,-I,0,H2=diag⁡I,0,0,0,0,0,0,0,0,0,0,-β2I.Ordering disturbance attenuation parameter β>0 and matric Ψ=Δ+νT(W1+W2)ν, we can get(47)Jet,ϱt≤ηTtΨηt.
To apply congruence transform, multiplying both sides ofΨ with diag⁡P11-1,…,P11-1︸10,I,I and employing Schur complements, inequality (18) can be induced. Similarly, multiplying both sides of matric (34) with diag⁡P11-1,…,P11-1︸7 and matric (40) with diag⁡P11-1,P11-1, we can get inequality (19) (20), where(48)Q~x=P→11Q~xP→11,x=1,2,3,4,R~y=P→11R~yP→11,M~y=P→11M~yP→11,y=1,2,3,N~d=P→11N~dP→11,S~d=P→11S~dP→11,H~d=P→11H~dP→11,d=1,2,P→ij=P→11PijP→11i,j=1,2,3,4,5,F=KP→11.This completes the proof.Due to presence of nonlinear terms in the form ofP→11QxP→11(x=1,2,3,4), we cannot obtain a feasible solution by LMI tool box. To solve this problem, the cone complementary linearization algorithm (CCLA) is proposed [23]. Define new variable Tx(x=1,2,3,4) satisfying(49)P→11Q~xP→11≥Txx=1,2,3,4.Applying Schur complements, expression (49) can be induced as follows:(50)-Q~x-1P→11-1∗-Tx-1≤0,x=1,2,3,4.Set Yx=Q~x-1, Pn=P→11-1, Ux=Tx-1. From (50), we have (51)-YxPn∗-Ux≤0,x=1,2,3,4.Then, the nonlinear optimization problem can be represented: (52)min⁡tr⁡P→11Pn+∑x=14YxQx+UxTx,subject to formulas (18)–(20) and (51):(53)Ψrm2νTrMmνTrm32νTrMm32νTP→11P→11LT∗-G100000∗∗-G20000∗∗∗-G3000∗∗∗∗-G400∗∗∗∗∗-I0∗∗∗∗∗∗-I,(54)P→11I∗Pn≥0,YxI∗Q~x≥0,UxI∗Tx≥0,x=1,2,3,4.Thus, the minimization problem (53) (54) can be solved by the following iterative algorithm.Algorithm 7.
(i) For given scalarsrm,rM,rd,κ1j(j=2,3,4,5), find a feasible set (P→110,Pn0,Yx,Q~x0,Ux0,Tx0,F0) of LMI in (54). If there is none, exit. Set b=0.
(ii) Solve minimization problem with a feasible set:(55)P→110,Pn0,Yx,Q~x0,Ux0,Tx0,F0.Minimize(56)tr⁡P→11bPn+PnbP→11+∑x=14YxbQ~x+Q~xbYx+UxbTx+TxbUxsubject to LMI (54).
SetP→11b+1=P→11, Pnb+1=Pn, Yxb+1=Yx, Q~xb+1=Q~x, Uxb+1=Ux, Txb+1=Tx, Fb+1=F.
(iii) If linear matrix inequalities (18)–(20) are feasible for K=FP→11-1, then drop out. Otherwise, set b=b+1 and go to item (ii).


## 4. Simulation

The simulation will be exploited to express the effectiveness of the above theoretical results.To simulate normal states of blood vessel, give the parametersc=0.15, d=-1.7, E=0.3, ω=1 [12]. The chaotic response can be diversiform with the change of τ. It means that the changes of pathological blood vessels may be induced by different parameter τ. As τ=-0.5, coronary artery system (1) will show the very complex dynamic behavior. The bifurcation diagram of system (1) is shown in Figure 1 without external disturbance and controller.Figure 1
The trajectory of chaotic coronary artery system without disturbance and controller.The single and double nitrate isosorbide nitrate (controlleru) is quickly absorbed by patients; blood vessels will dilate and increase blood supply to the heart muscle, which effectively relieves angina symptoms. To demonstrate the fact, order initial matrices A, C, D, L and the initial state vectors xm(0), xs(0) as follows:(57)A=-0.151.70.575-0.35,C=5005,D=1001,L=0000.36,xm0=0.5,0,xs0=-1,1.5.Then, to solute chaotic behavior and investigate external disturbance on the system performance, the control signal u(t) and the external disturbance signal ϱ(t) are imposed on system (3), where ϱ(t)=[sin⁡40t,sin⁡30t].For simplicity of calculation, orderκ12,κ13,κ14,κ15=0. Based on the above condition, Figure 2 shows the trajectories and error states of error system without controller u(t), which illustrates synchronization error is not asymptotical to approach zero, where ei(t)=xsi-xmi(i=1,2) represent the inner radius deviation and the pressure changes between healthy and pathological system; thus the states of abnormal system are not gradually tracking the normal states without controller u(t).Figure 2
State responses of error system (5) without controller.In order to synchronize the above systems, we formulateH∞ control strategy with the conditions of rm=0.05, rM=0.25, rd=0.2 and obtain the three situations of controller gain matrices.(i) When input time-delayrt=0.02+0.1ssin⁡(t) and disturbance attenuation parameter β=0.38 applying Theorem 6, we can get(58)K1=0.69590.17330.17150.6175. The response of system (5) with u(t) is revealed in Figure 3.Figure 3
State responses of error system (5) with controller gain matrix K1.(ii) When input time-delayrt=0.095+0.09sin⁡(t) and disturbance attenuation parameter β=0.39 applying Theorem 6, we can get(59)K2=0.75100.20960.20750.6550. The response of system (5) with u(t) is revealed in Figure 4.Figure 4
State responses of error system (5) with controller gain matrix K2.(iii) When input time-delayrt=0.1+0.1sin⁡(t) and disturbance attenuation parameter β=0.40 applying Theorem 6, we can get(60)K3=0.75630.20380.20170.6620 The response of system (5) with u(t) is revealed in Figure 5.Figure 5
State responses of error system (5) with controller gain matrix K3.Under the effect of the aforementioned controller, we can see that the error system asymptotically converges to the zero in Figures3–5, which shows that the states of pathological system are gradually tracking the healthy states. As we can see from Figure 5, H∞ control can reduce the effect of the disturbance signal ϱ(t) and drive unhealthy vessel into the normal orbit in the shortest time, when given input time-delay r(t)=0.1+0.1sin⁡(t) and disturbance attenuation parameter β=0.40.It is seen that the strategy ofH∞ control is efficient for the synchronization of coronary artery input time-delay system with external disturbance. Hence, the method has some theory and practice value for the treatment of angina symptoms. In addition, our approaches are advantageous in many input time-delay circumstances and it is worthless to elaborate in all aspects for brevity.


## 5. Conclusions

This paper has studied the problem ofH∞ control for synchronization of the coronary artery input time-delay system with disturbance. Based on triple integral form of Lyapunov-Krasovskii functionals, a sufficient condition has been established to ensure asymptotical convergence to zero for the error system under a given H∞ performance level. Moreover, the free-matrix-based integral inequality, Wirtinger-based integral inequality, and reciprocal convex combination approach are used to obtain less conservatism. Furthermore, a novel H∞ controller guaranteeing synchronization for master-slave system is designed under the above proposed conditions, which means the states of pathological vessel are gradually tracking the healthy states. Finally, simulation result demonstrates the effectiveness and feasibility of the presented strategies. Meanwhile, the research is especially significant to cure angina symptoms. In the future, we hope those methods can be further utilized for other time-delay systems.



---

*Source: 4908459-2018-01-16.xml*