Asymptotic problems and numerical schemes for traffic flows with unilateral constraints describing the formation of jams

  • Received: 01 December 2016 Revised: 01 August 2017
  • Primary: 65M08; Secondary: 35L60, 90B20, 35Q91

  • We discuss numerical strategies to deal with PDE systems describing traffic flows, taking into account a density threshold, which restricts the vehicle density in the situation of congestion. These models are obtained through asymptotic arguments. Hence, we are interested in the simulation of approached models that contain stiff terms and large speeds of propagation. We design schemes intended to apply with relaxed stability conditions.

    Citation: Florent Berthelin, Thierry Goudon, Bastien Polizzi, Magali Ribot. Asymptotic problems and numerical schemes for traffic flows with unilateral constraints describing the formation of jams[J]. Networks and Heterogeneous Media, 2017, 12(4): 591-617. doi: 10.3934/nhm.2017024

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  • We discuss numerical strategies to deal with PDE systems describing traffic flows, taking into account a density threshold, which restricts the vehicle density in the situation of congestion. These models are obtained through asymptotic arguments. Hence, we are interested in the simulation of approached models that contain stiff terms and large speeds of propagation. We design schemes intended to apply with relaxed stability conditions.



    1. Introduction

    The subject of fractional calculus deals with the investigations of derivatives and integrals, of any arbitrary real or complex order, which unify and extend the notions of integer-order derivative and n-fold integral. It can be considered as a branch of mathematical analysis which deals with integrodifferential operators and equations where the integrals are of convolution type and exhibit (weakly singular) kernels of power-law type. It is strictly related to the theory of pseudo-differential operators. Fractional order models have the tendency to capture non-local relations in space and time, thus forming an improvised model for analyzing complex phenomena. It is a successful tool for describing complex quantum field dynamical systems, dissipation and long-range phenomena that cannot be well illustrated using ordinary differential and integral operators. For an introductory study on fractional calculus and fractional derivatives, see the literatures [19,21,25].

    Inducing randomness into the model helps us to analyze better by taking into consideration the effect of uncertainty, thus leading to stochastic fractional differential equations (refer [24] and references therein). The theory of existence, controllability and stability of fractional differential equations has been studied by many authors (for instance, see [1,2,15,16]). However there seems to be possibly limited literature to the study of large deviations for stochastic fractional differential equations.

    Large deviation theory is a branch of probability theory that deals with the study of rare events. Though the probability of occurrence of rare events is too small, their impact may be large and so it is significant to study such rare events. Large deviation theory finds its application in many areas such as mathematical finance, statistical mechanics and various fields ranging from physics to biology. The origin of large deviations dates back to the 1930s where there was a necessity to solve the problem of total claim exceeding the reserve fund set aside in an insurance company. The solution was discovered by the Swedish mathematician Cramer via refinement of the central limit theorem. Subsequent developments has been made since then and there was major breakthrough into the subject after Varadhan [31] established a general framework for large deviation principle and formulated the Varadhan's lemma in 1966. In 1970, Wentzell and Freidlin [13] developed a theory to enhance the large deviation principle for differential equations with small stochastic perturbations, which involves time discretization of the original problem and then analyzing the large deviation principle in the limit. Fleming [12] developed a stochastic control approach to establish large deviation principle and then Dupuis and Ellis [11] combined the weak convergence approach with the theory of Fleming. These developments indeed explore the close association of large deviation theory with optimal controllability problems.

    Using the weak convergence approach, the large deviations for homeomorphism flows of non-Lipschitz Stochastic Differential Equations (SDEs) was studied by Ren and Zhang [27]; the large deviations for two-dimensional stochastic Navier-Stokes equations by Sritharan and Sundar [28], and for stochastic evolution equations with small multiplicative noise by Liu [18]. For more references on this approach, one may refer [5,6,11,14,26]. By using the approximating method, Mohammed and Zhang [23] established a Freidlin-Wentzell type large deviation principle for the stochastic delay differential equations. Mo and Luo [22] also studied the large deviations for the stochastic delay differential equations by employing the weak convergence approach. Bo and Jiang [4] analyzed the large deviation for Kuramoto-Sivashinsky stochastic partial differential equation. A large deviation principle for stochastic differential equations with deviating arguments is dealt with in [30].

    A Freidlin-Wentzell type large deviation principle is discussed in Dembo and Zeitouni [8] for the following stochastic differential equation:

    dX(t)=b(t,X(t))dt+ϵσ(t,X(t))dW(t),t(0,T],X(0)=X0.} (1)

    In the case that the system is affected by hereditary influences, the drift and diffusion coefficients (b and σ) also depend on an integral component, thus giving rise to stochastic integrodifferential equations. The large deviations for stochastic integrodifferential equations has been carried out in [29]. In this paper, we consider the stochastic fractional integrodifferential equations with Gaussian noise perturbation of multiplicative type and establish the large deviation principle by using the results developed by Budhiraja and Dupuis [7]. The compactness argument is done with the associated control equation and weak convergence result is obtained by observing the nature of the solution of the stochastic control equation as the perturbation of the noise term tends to zero.


    2. Preliminaries

    Let X and H be separable Hilbert spaces. Denote by L(X) the space of all bounded linear operators from X to X. Denote by J the time interval [0,T]. Let {Ω,F,P} be a complete filtered probability space equipped with a complete family of right continuous increasing sub σ-algebras {Ft,tJ} satisfying {FtF}. Let Q be a symmetric, positive, trace class operator on H and W() be a H-valued Wiener process with covariance operator Q. Denote the space H0:=Q1/2H. Then H0 is a Hilbert space with the inner product (X,Y)0:=(Q1/2X,Q1/2Y) for all X,YH0 and the corresponding norm is denoted by 0. Let LQ denote the space of all Hilbert-Schmidt operators from H0 to X. Consider the nonlinear stochastic fractional integrodifferential equation in X of the form

    CDαX(t)=AX(t)+b(t,X(t),t0f(t,s,X(s))ds)+σ(t,X(t),t0g(t,s,X(s))ds)dW(t)dt, tJ,X(0)=X0,} (2)

    where 1/2<α1,X0X and A:XX is a bounded linear operator. Also the drift coefficient b:J×X×XX, the noise coefficient σ:J×X×XLQ(H0;X) and f,g:J×J×XX. Assume the following Lipschitz conditions on the drift and noise coefficients: For all x1,x2,y1,y2X and 0stT, there exist constants Lb,Lσ,Lf,Lg>0 such that

    b(t,x1,y1)b(t,x2,y2)XLb[x1x2X+y1y2X],σ(t,x1,y1)σ(t,x2,y2)LQLσ[x1x2X+y1y2X],f(t,s,x1)f(t,s,x2)XLfx1x2X,g(t,s,x1)g(t,s,x2)XLgx1x2X.} (3)

    Also assume the following linear growth assumptions on the coefficients: For all x,yX and 0stT, there exist positive constants Kb,Kσ,Kf,Kg>0 such that

    b(t,x,y)2XKb[1+x2X+y2X],σ(t,x,y)2LQKσ[1+x2X+y2X],f(t,s,x)2XKf[1+x2X],g(t,s,x)2XKg[1+x2X].} (4)

    Let us first quote some basic definitions from fractional calculus. For α,β>0, with n1<α<n, n1<β<n and nN, D is the usual differential operator and suppose fL1(R+), R+=[0,).

    (ⅰ) Caputo Fractional Derivative:

    The Riemann Liouville fractional integral of a function f is defined as

    Iαf(t)=1Γ(α)t0(ts)α1f(s)ds,

    and the Caputo derivative of f is CDαf(t)=Inαf(n)(t), that is,

    CDαf(t)=1Γ(nα)t0(ts)nα1f(n)(s)ds,

    where the function f(t) has absolutely continuous derivatives up to order n1.

    (ⅱ) Mittag-Leffler Operator Function: Two parameter family of Mittag-Leffler operator functions is defined as

    Eα,β(A)=k=0AkΓ(kα+β),α,β>0.

    Here A is the bounded linear operator. In particular, for β=1, the one parameter Mittag-Leffler operator function is

    Eα(A)=k=0AkΓ(kα+1).

    The Mittag-Leffler functions are in fact generalizations of the exponential function and are applicable in varied situations involving fractional derivatives, see for example [9]. Assume the following boundedness on the Mittag-Leffler operator functions with one and two parameters:

    M1=suptJEα(Atα)L(X),  M2=suptJEα,α(Atα)L(X). (5)

    In order to find the solution representation, we need the following hypothesis and make use of the Lemma that follows.

    (H1) The operator AL(X) commutes with the fractional integral operator Iα on X and A2L(X)<(2α1)(Γ(α))2T2α.

    Lemma 2.1. [17] Suppose that A is a linear bounded operator defined on X (more generally, X may be a Banach space) and assume that AL(X)<1. Then (IA)1 is linear and bounded. Also

    (IA)1=k=0Ak.

    The convergence of the above series is in the operator norm and (IA)1L(X)(1AL(X))1.

    We next show that IαAL(X)<1 and, by the Lemma, we obtain (IIαA)1 is bounded and linear. Let XX; then by (H1), we have

    E[(IαA)X2C(J;X)]T(Γ(α))2E[suptJt0(ts)2α2AX(s)2Xds]T2α(2α1)(Γ(α))2E[suptJAX(t)2X]<EX2C(J;X),

    hence yielding the desired inequality. On the other hand, defining the random differential operator

    dF(t,X(t)):= b(t,X(t),t0f(t,s,X(s))ds)dt+σ(t,X(t),t0g(t,s,X(s))ds)dW(t)

    and operating by Iα on both sides of (2), we have

    X(t)=X0+IαAX(t)+IαdF(t,X(t))dt,X(t)=(IIαA)1(X0+IαdF(t,X(t))dt).

    Therefore, using Lemma 2.1 and the fact that Iα commutes with A, we obtain (see [3,20])

    X(t)=k=0(IαA)k(X0+IαdF(t,X(t))dt)=k=0IkαAkX0+IkαAkIαdF(t,X(t))dt=k=0IkαAkX0+Ikα+αAkdF(t,X(t))dt=k=0AktαkΓ(kα+1)X0+t0(ts)α1(k=0Ak(ts)αkΓ(kα+α))dF(s,X(s)),=Eα(Atα)X0+t0(ts)α1Eα,α(A(ts)α)dF(s,X(s)).

    Thus we obtain the solution representation of (2) as

    X(t)=Eα(Atα)X0+t0(ts)α1Eα,α(A(ts)α)b(s,X(s),s0f(s,τ,X(τ))dτ)ds+t0(ts)α1Eα,α(A(ts)α)σ(s,X(s),s0g(s,τ,X(τ))dτ)dW(s). (6)

    We now present some basic definitions and results from large deviation theory. For this, let {Xϵ} be a family of random variables defined on the space X and taking values in a Polish space Z (i.e., a complete separable metric space Z).

    Definition 2.1. (Rate Function). A function I:Z[0,] is called a rate function if I is lower semicontinuous. A rate function I is called a good rate function if for each N<, the level set KN={fZ:I(f)N} is compact in Z.

    Definition 2.2. (Large Deviation Principle). Let I be a rate function on Z. We say the family {Xϵ} satisfies the large deviation principle with rate function I if the following two conditions hold:

    (ⅰ) Large deviation upper bound. For each closed subset F of Z,

    lim supϵ0ϵlogP(XϵF)I(F).

    (ⅱ) Large deviation lower bound. For each open subset G of Z,

    lim infϵ0ϵlogP(XϵG)I(G).

    Definition 2.3. (Laplace Principle). Let I be a rate function on Z. We say the family {Xϵ} satisfies the Laplace principle with rate function I if for all real-valued bounded continuous functions h defined on Z,

    limϵ0ϵlogE{exp[1ϵh(Xϵ)]}=inffZ{h(f)+I(f)}.

    One of the main results of the theory of large deviations is the equivalence between the Laplace principle and the large deviation principle when the underlying space is Polish. For a proof we refer the reader to Theorem 1.2.1 and Theorem 1.2.3 in [11].

    Theorem 2.1. The family {Xϵ} satisfies the Laplace principle with good rate function I on a Polish space Z if and only if {Xϵ} satisfies the large deviation principle with the same rate function I.


    3. Large Deviation Principle

    In this section, we consider the stochastic fractional integrodifferential equation (2) with the random noise term being perturbed by a small parameter ϵ>0 in the form

    CDαXϵ(t)=AXϵ(t)+b(t,Xϵ(t),t0f(t,s,Xϵ(s))ds)+ϵσ(t,Xϵ(t),t0g(t,s,Xϵ(s))ds)dW(t)dt, t(0,T],Xϵ(0)=X0.} (7)

    Let Gϵ:C(J:H)Z be a measurable map defined by Gϵ(W()):=Xϵ(), where Xϵ is the solution of the above equation (7). We implement the variational representation developed by Budhiraja and Dupuis to study the large deviation principle for the solution processes {Xϵ}. Let

    A={v:visH0- valuedFt- predictable process andT0v(s,ω)20ds< a.s. },
    SN={vL2(J;H0):T0v(s)20dsN},

    where L2(J;H0) is the space of all H0 -valued square integrable functions on J. Then SN endowed with the weak topology in L2(J;H0) is a compact Polish space (see [10]). Let us also define

    AN={vA:v(ω)SN Pa.s}.

    We now state the variational representation developed by Budhiraja and Dupuis [7,Theorem 4.4] that provides sufficient conditions under which Laplace principle (equivalently, large deviation principle) holds for the family {Xϵ}:

    Proposition 3.1. Suppose that there exists a measurable map G0:C(J:H)Z such that the following hold:

    (ⅰ) Let {vϵ:ϵ>0}AN for some N<. Let vϵ converge in distribution as SN-valued random elements to v. Then Gϵ(W()+1ϵ0vϵ(s)ds) converges in distribution to G0(0v(s)ds).

    (ⅱ) For every N<, the set

    KN:={G0(.0v(s)ds):vSN}

    is a compact subset of Z.

    For each hZ, define

    I(h):=inf{vL2(J:H0):h=G0(.0v(s)ds)}{12T0v(s)20ds}, (8)

    where the infimum over an empty set is taken as . Then the family {Xϵ:ϵ>0}=Gϵ(W()) satisfies the Laplace principle in Z with the rate function I given by (8).

    In Proposition 3.1, (ⅱ) is a compactness criterion and it is to be noticed that it has a coincidence with the fact that the level set for a good rate function is compact. Thanks to the variational representation prescribed by Budhiraja and Dupuis, the study of large deviation principle for any stochastic differential equation can now be simplified to the problem of identifying Borel measurable function G0 so that the hypothesis in the above proposition is satisfied.

    Consider the controlled equation associated to (7) with control vSN.

    CDαXv(t)=AXv(t)+b(t,Xv(t),t0f(t,s,Xv(s))ds)+ σ(t,Xv(t),t0g(t,s,Xv(s))ds)v(t), t(0,T],Xv(0)=X0,} (9)

    and let Xv(t) denote the solution of the equation (9). The main result in this chapter is the following Freidlin-Wentzell type theorem:

    Theorem 3.1. With the assumption (H1) on the bounded linear operator A, the family {Xϵ(t)} of solutions of (7) satisfies the large deviation principle (equivalently, Laplace principle) in C(J;X) with the good rate function

    I(h):=inf{12T0v(t)20dt;Xv=h}, (10)

    where vL2(J;H0) and Xv denotes the solution of the control equation (9) with the convention that the infimum of an empty set is infinity.

    In order to prove the theorem, the main work is to verify the sufficient conditions in Proposition 3.1esponding to (7):

    CDαXϵv(t)=AXϵv(t)+b(t,Xϵv(t),t0f(t,s,Xϵv(s))ds)+ σ(t,Xϵv(t),t0g(t,s,Xϵv(s))ds)v(t) + ϵσ(t,Xϵv(t),t0g(t,s,Xϵv(s))ds)dW(t)dt, t(0,T],Xϵv(0)=X0.} (11)

    The solution representation is given by

    Xϵv(t)=Eα(Atα)X0+t0(ts)α1Eα,α(A(ts)α)b(s,Xϵv(s),s0f(s,τ,Xϵv(τ))dτ)ds+t0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)v(s)ds+ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s). (12)

    Before proceeding further analysis, we show that the solution Xϵv(t) obeys the following energy estimate:

    Theorem 3.2. The solution Xϵv(t) of (11) is bounded in the space L2(Ω;C(J;X)), that is, there exists a positive constant K>0 such that

    E[suptJXϵv(t)2X]K. (13)

    Proof. First we define the stopping time τN:=inf{t:Xϵv(t)2N}. And, for any t[0,TτN], consider the solution representation of (11) given by (12), take 2X on both sides and use the algebraic identity (a+b+c+d)24(a2+b2+c2+d2) to get

    Xϵv(t)2X 4Eα(Atα)2L(X)X02X+ 4t0(ts)α1Eα,α(A(ts)α)b(s,Xϵv(s),s0f(s,τ,Xϵv(τ))dτ)ds2X+ 4t0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)v(s)ds2X+ 4ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s)2X.

    Using the Holder inequality and the bounds on Eα()L(X) and Eα,α()L(X) given by (5), we obtain the estimate

    Xϵv(t)2X4M21X02X+4M22t0(ts)2α2dst0b(s,Xϵv(s),s0f(s,τ,Xϵv(τ))dτ)2Xds+ 4M22t0(ts)2α2σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)2LQds  t0v(s)20ds+ 4ϵt0(ts)2α2Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s)2X.

    Now using the linear growth property of 'b' and 'σ' given by (3) results in

    Xϵv(t)2X4M21X02X+4KbM22T2α12α1t0[1+Xϵv(s)2X+s0f(s,τ,Xϵv(τ))dτ2X]ds+ 4KσM22Nt0(ts)2α2[1+Xϵv(s)2X+s0g(s,τ,Xϵv(τ))dτ2X]ds+ 4ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s)2X.

    Using Holder's inequality for the integrands s0f(s,τ,Xϵv(τ))dτ2X and s0g(s,τ,Xϵv(τ))2X and also making use of the linear growth property of 'f' and 'g' given by (4), we get, on simplifying,

    Xϵv(t)2X4M21X02X+4KbM22T2α12α1t0[1+Xϵv(s)2X+KfTt0[1+Xϵv(τ)2X]dτ]ds+ 4KσM22Nt0(ts)2α2[1+Xϵv(s)2X+KgTt0[1+Xϵv(τ)2X]dτ]ds+ 4ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s)2X. (14)

    The stochastic integral term can be estimated by means of the Burkholder-Davis-Gundy inequality as

    E{sup0tTτN[t0(ts)α1Eα,α(A(ts)α)σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)dW(s)2X]}M22T0(Ts)2α2σ(s,Xϵv(s),s0g(s,τ,Xϵv(τ))dτ)2LQdsKσM22T0(Ts)2α2[1+Xϵv(s)2X+KgTT0[1+Xϵv(τ)2X]dτ]dsKσM22T0(Ts)2α2[1+Xϵv(s)2X]ds+KσM22KgTT2α12α1T0[1+Xϵv(s)2X]ds.

    Hence (14) becomes, after taking supremum and expectation on both sides and simplifying,

    E[sup0tTτNXϵv(t)2X]4M21EX02X+4KbM22(1+KfT2)T2α12α1ET0[1+Xϵv(s)2X]ds+ 4KσM22(N+ϵ)ET0(Ts)2α2[1+Xϵv(s)2X]ds+ 4KσM22KgTT2α12α1(N+ϵ)ET0[1+Xϵv(s)2X]ds.

    Further simplifying and applying the well known Gronwall inequality, we end up with

    E[sup0tTτNXϵv(t)2X] (4M21EX02X+CT)eCT=K, (15)

    where CT=4M22T2α12α1[Kb(1+KfT2)T+Kσ(1+KgT)(N+ϵ)]. Observe that TτNT as N, hence resulting in (13).

    Lemma 3.1 (Compactness). Define G0:C(J;H)C(J;X) by

    G0(h):={Xv, if h=.0v(s)ds for some vSN,0, otherwise.

    Then, for each N<, the set

    KN={G0(.0v(s)ds):vSN}

    is a compact subset of C(J;X).

    Proof. Let {vn} be a sequence of controls from SN that converge weakly to v in L2(J;H0) and let Xvn(t) denote the solution of (9) with control v replaced by vn. Take Yn(t)=Xvn(t)Xv(t). Then the equation corresponding to Yn(t) would be

    CDαYn(t)=AYn(t)+b(t,Xvn(t),t0f(t,s,Xvn(s))ds)b(t,Xv(t),t0f(t,s,Xv(s))ds)+σ(t,Xvn(t),t0g(t,s,Xvn(s))ds)vn(t)σ(t,Xv(t),t0g(t,s,Xv(s))ds)v(t),Yn(0)=0.} (16)

    The solution representation is

    Yn(t)= t0(ts)α1Eα,α(A(ts)α)[b(s,Xvn(s),s0f(s,τ,Xvn(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)]ds+t0(ts)α1Eα,α(A(ts)α)[σ(s,Xvn(s),s0g(s,τ,Xvn(τ))dτ)vn(s)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)v(s)]ds=: I1(t)+I2(t)+I3(t), (17)

    where

    I1(t):= t0(ts)α1Eα,α(A(ts)α)[b(s,Xvn(s),s0f(s,τ,Xvn(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)]ds, (18)
    I2(t):= t0(ts)α1Eα,α(A(ts)α)[σ(s,Xvn(s),s0g(s,τ,Xvn(τ))dτ)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)]vn(s)ds, (19)
    I3(t):=t0(ts)α1Eα,α(A(ts)α)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))ds. (20)

    First consider the integral I1(t) and taking X on both sides, we get

    I1(t)Xt0(ts)α1Eα,α(A(ts)α)L(X) b(s,Xvn(s),s0f(s,τ,Xvn(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)Xds.

    Using the boundedness of Eα,α() given by (5) and Lipschitz continuity of 'b' from (3), we obtain

    I1(t)X LbM2t0(ts)α1[Yn(s)X+s0f(s,τ,Xvn(τ))f(s,τ,Xv(τ))X dτ]ds.

    Using the Lipschitz continuity of 'f', we get subsequently

    I1(t)X LbM2t0(ts)α1[Yn(s)X+Lfs0Yn(τ)X dτ]ds LbM2t0(ts)α1Yn(s)Xds+LbLfM2t0(ts)α1t0Yn(τ)Xdτds= LbM2t0(ts)α1Yn(s)Xds+LbLfM2Tααt0Yn(s)Xds. (21)

    In a similar way, consider the integral I2(t) and estimating using the boundedness of Eα,α() and Lipschitz continuity of 'σ', we get

    I2(t)XLσM2t0(ts)α1[Yn(s)X+s0g(s,τ,Xvn(τ))g(s,τ,Xv(τ))X dτ]vn(s)0ds.

    Now, using the Lipschitz continuity of 'g', we obtain

    I2(t)X LσM2t0(ts)α1Yn(s)Xvn(s)0ds +LσLgM2t0Yn(τ)Xdτt0(ts)α1vn(s)0ds. (22)

    For the third integral, applying Holder's inequality, one gets

    I3(t)X t0(ts)α1 Eα,α(A(ts)α)L(X)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))Xds M2(t0(ts)2α2ds)1/2(t0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))2Xds)1/2 M2Tα(t0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))2Xds)1/2, (23)

    where Tα=Tα1/22α1. Now (17) becomes, after substituting (21 -(23) and applying Gronwall's inequality,

    Yn(t)X M2Tα(t0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))2Xds)1/2×exp{LbM2t0(ts)α1ds+LbLfM2Tα+1α+LσM2t0(ts)α1vn(s)0ds+LσLgM2Tt0(ts)α1vn(s)0ds}.

    Applying Holder's inequality to the last two integral terms on the exponential index, one gets

    Yn(t)X M2Tα[t0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))2Xds]1/2×exp{LbM2Tαα+LbLfM2Tα+1α+(LσM2+LσLgM2T)(t0(ts)2α2ds)1/2(t0vn(s)20ds)1/2}.

    On simplifying and taking supremum over tJ, we get

    suptJYn(t)X M2Tα[T0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vn(s)v(s))2Xds]1/2×exp{LbM2Tαα(1+LfT)+LσM2T2α12α1(1+LgT)N}. (24)

    Since vnv weakly in L2(J;H0) and σ is a Hilbert-Schmidt operator and hence compact, we have that σvnσv strongly in L2(J;X) and so Yn=XvnXv0 in C(J;X), thereby proving the compactness.

    Lemma 3.2 (Weak Convergence). Let {vϵ:ϵ>0}AN for some N<. Assume that vϵ converge to v in distribution as SN-valued random elements; then

    Gϵ(W()+1ϵ.0vϵ(s)ds)G0(.0v(s)ds)

    in distribution as ϵ0.

    Proof. Consider the nonlinear stochastic fractional integrodifferential equation (11) with control vϵL2(J;H0) and let the solution be denoted by Xϵvϵ(t). Take Yϵ(t)=Xϵvϵ(t)Xv(t). Then

    Yϵ(t)= t0(ts)α1Eα,α(A(ts)α)[b(s,Xϵvϵ(s),s0f(s,τ,Xϵvϵ(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)]ds+t0(ts)α1Eα,α(A(ts)α)[σ(s,Xϵvϵ(s),s0g(s,τ,Xϵvϵ(τ))dτ)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)]vϵ(s)ds+t0(ts)α1Eα,α(A(ts)α)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vϵ(s)v(s))ds+ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵvϵ(s),s0g(s,τ,Xv(τ))dτ)dW(s).

    Taking 2 on both sides and using the algebraic inequality (a+b+c+d)24(a2+b2+c2+d2), we obtain

    Yϵ(t)2XI1(t)+I2(t)+I3(t)+I4(t), (25)

    where

    I1(t):= 4t0(ts)α1Eα,α(A(ts)α)[b(s,Xϵvϵ(s),s0f(s,τ,Xϵvϵ(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)]ds2X, (26)
    I2(t):= 4t0(ts)α1Eα,α(A(ts)α)[σ(s,Xϵvϵ(s),s0g(s,τ,Xϵvϵ(τ))dτ)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)]vϵ(s)ds2X, (27)
    I3(t):= 4 t0(ts)α1Eα,α(A(ts)α)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vϵ(s)v(s))ds2X, (28)
    I4(t):= 4 ϵt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵvϵ(s),s0g(s,τ,Xv(τ))dτ)dW(s)2X. (29)

    First consider the integral I1(t) and applying Holder's inequality along with the bound for Eα,α()L(X) given by (5) and the Lipschitz continuity of 'b' given by (3), one gets

    I1(t) 4t0(ts)2α2Eα,α(A(ts)α)2L(X)ds×t0b(s,Xϵvϵ(s),s0f(s,τ,Xϵvϵ(τ))dτ)b(s,Xv(s),s0f(s,τ,Xv(τ))dτ)2Xds4L2bM22t0(ts)2α2dst0[Yϵ(s)X+s0f(s,τ,Xϵvϵ(τ))f(s,τ,Xv(τ))dτ]2ds.

    Using the algebraic identity (a+b)22(a2+b2) and Holder's inequality to the last integral term on the right hand side and then using the Lipschitz continuity of 'f', we obtain simultaneously

    I1(t) 8L2bM22T2α12α1t0[Yϵ(s)2X+Ts0f(s,τ,Xϵvϵ(τ))f(s,τ,Xv(τ))2X]ds 8L2bM22T2α12α1t0[Yϵ(s)2X+L2fTs0Yϵ(τ)2Xdτ]ds.

    On simplifying, the integral I1(t) can be estimated as

    I1(t) 8L2bM22(1+L2fT2)T2α12α1t0Yϵ(s)2Xds. (30)

    Similarly consider the integral I2(t), apply Holder's inequality followed by the bound for Eα,α()X and the Lipschitz continuity of 'σ' to get

    I2(t)4t0(ts)2α2Eα,α(A(ts)α)2L(X)ds×t0σ(s,Xϵvϵ(s),s0g(s,τ,Xϵvϵ(τ))dτ)σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)2LQvϵ(s)20ds8L2σM22T2α12α1t0[Yϵ(s)2X+L2gTs0Yϵ(τ)2Xdτ]vϵ(s)20ds.

    On further simplifying and making use of the fact that the control variable vSN, we obtain

    I2(t)8L2σM22T2α12α1[t0Yϵ(s)2Xvϵ(s)20ds+L2gNTt0Yϵ(τ)2Xdτ]. (31)

    Now consider the integral I3(t), apply Holder's inequality and the bound for Eα,α()L(X) to obtain

    I3(t)4M22T2α12α1t0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vϵ(s)v(s))2Xds. (32)

    Finally consider the stochastic integral I4(t) and taking supremum and then taking expectation on both sides and making use of the Burkholder-Davis-Gundy inequality, we get

    E[suptJI4(t)]=4ϵE{suptJt0(ts)α1Eα,α(A(ts)α)σ(s,Xϵvϵ(s),s0g(s,τ,Xv(τ))dτ)dW(s)2X}4ϵM22ET0(Ts)2α2σ(s,Xϵvϵ(s),s0g(s,τ,Xϵvϵ(τ))dτ)2LQds.

    Using the linear growth property of 'σ' and 'g' and simplifying, we get

    E[suptJI4(t)]4ϵKσM22ET0(Ts)2α2[1+Xϵvϵ(s)2X+KgTs0(1+Xϵvϵ(τ)2X)dτ]ds4ϵKσM22[ET0(Ts)2α2[1+Xϵvϵ(s)2X]ds+KgTT2α12α1ET0[1+Xϵvϵ(s)2X]ds]4ϵKσM22T2α12α1(1+KgT2){1+E[suptJXϵvϵ(t)2X]}. (33)

    With all these estimates on the integrals Ii(t),i=1,2,3,4, given by (30) -(33), equation (25) becomes, after taking supremum over tJ and then taking expectation,

    E[suptJYϵ(t)2X]8L2bM22(1+L2fT2)T2α12α1ET0Yϵ(s)2Xds+ 8L2σM22T2α12α1[ET0Yϵ(s)2Xvϵ(s)20ds+L2gNTET0Yϵ(τ)2Xdτ]+ 4M22T2α12α1ET0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vϵ(s)v(s))2Xds+ 4ϵKσM22T2α12α1(1+KgT2){1+E[suptJXϵvϵ(t)2X]}.

    Applying Gronwall's inequality and further simplifying, we end up with

    E[suptJYϵ(t)2X]{4M22T2α12α1ET0σ(s,Xv(s),s0g(s,τ,Xv(τ))dτ)(vϵ(s)v(s))2Xds+ 4ϵKσM22T2α12α1(1+KgT2)(1+E[suptJXϵvϵ(t)2X])}×exp(8M22T2α12α1[L2b(1+L2fT2)T+L2σ(1+L2gT2)N]). (34)

    Since σ is a Hilbert-Schmidt operator and hence compact and since vϵv weakly in L2(J;H0) as ϵ0, we have that σvϵσv strongly in L2(J;X) and so Yϵ=XϵvϵXv0 in probability in the space L2(Ω;C(J;X)). Since convergence in probability always implies convergence in expectation, we have finally proved the required weak convergence criterion.


    4. Examples

    Example 4.1. Consider the stochastic fractional integrodifferential equation with additive noise given by

    CDαX(t)=t0X(s)ds+sin(X(t))+t01+X2(s)ds+ϵdW(t)dt, t(0,T],X(0)= X0,} (35)

    with X0R and 1/2<α1. The corresponding controlled equation with control vL2(0,T;R) takes the form

    CDαXv(t)=t0Xv(s)ds+sin(Xv(t))+t01+X2v(s)ds+v(t),t(0,T],Xv(0)=X0.

    It is observed that if there exists a unique solution Xv() for the above mentioned equation, then the control vL2([0,T],R) with which the unique solution Xv is attained is also unique and hence the rate function I:C([0,T];R)[0,] is given explicitly by

    I(ϕ)=12T0|CDαϕsinϕt0(ϕ(s)+1+ϕ2(s))ds|2dt, (36)

    if ϕ satisfies (35) for appropriate control v, and otherwise.

    Example 4.2. As an example for (7) with multiplicative type noise, consider the following stochastic equation:

    CD3/4X(t)=βt0[X(s)+exp(11+X2(s))]ds+ϵηt0X(s)dsdW(t)dt, t(0,1],X(0)=1,} (37)

    where η,β>0 are positive constants. Then the rate function I:C([0,1];R)[0,] is given by

    I(ϕ)=inf{1210|v(t)|2dt:vL2([0,1],R) such that Xv=ϕ}, (38)

    where inf= and Xv is the unique solution of

    Xv(t)=1+βΓ(34)t01(ts)1/4s0[Xv(r)+exp(11+X2v(r))]drds+ηΓ(34)t0v(s)(ts)1/4s0Xv(r)drds,t[0,1]. (39)

    It is evident from (38) that estimating the rate function I(ϕ) is a problem of finding the minimal cost 1210|v(t)|2dt, out of all the controls v that steers the desired solution ϕ=Xv from (39).


    Acknowledgments

    The first author would like to thank the Department of Science and Technology, New Delhi for their financial support under the INSPIRE Fellowship Scheme. The work of the third author is supported by the University Grants Commission [grant number: MANF-2015-17-TAM-50645] from the Government of India.


    Conflict of Interest

    All authors declare that there is no conflict of interest.


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