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Research article

A critical analysis of the role of energy generation from municipal solid waste (MSW)

  • Received: 10 May 2020 Accepted: 20 July 2020 Published: 09 October 2020
  • Municipal solid waste (MSW) management in Nigeria is a major problem that has reached alarming proportions. The increasing difficulty in managing MSW has become one of the most intractable environmental and health problems. Energy generation from MSW plays both negative and positive roles which can be viewed in an environmental and socio-economic perspective. This paper evaluated the socio-environmental and economic benefits of energy generation from MSW in Nigeria using secondary data and various analytical tools such as content analysis, in-depth analysis, critical review and narrative analysis. Some of the principal findings of this paper are that informal MSW recycling is also a contributor to inadequate energy generation from MSW, WTE technologies such as anaerobic digestion (AD) and combined heat and power (CHP) is a promising and viable MSW management strategy that can be adopted in most villages in rural areas in Nigeria. Energy generation from MSW in Nigeria has not been appropriately promoted due to weak environmental policies, inadequate funding and lack of environmental education. As such the need to review Nigerian waste management policies, creation of environmental, economic awareness and enlightenment campaigns and development of appropriate MSW collection and disposal agency in Nigeria should be provided. This paper concludes that it is essential to reassess all legislations regarding waste management in Nigeria with the aim to promote various WTE technologies most especially anaerobic digestion (AD) and combined heat and power (CHP) as these technologies are most suitable in Nigeria where the emissions of GHGs are rapidly increasing.

    Citation: Obidike Emeka Esae, Jatau Sarah, Ayu Mofe. A critical analysis of the role of energy generation from municipal solid waste (MSW)[J]. AIMS Environmental Science, 2020, 7(5): 387-405. doi: 10.3934/environsci.2020026

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  • Municipal solid waste (MSW) management in Nigeria is a major problem that has reached alarming proportions. The increasing difficulty in managing MSW has become one of the most intractable environmental and health problems. Energy generation from MSW plays both negative and positive roles which can be viewed in an environmental and socio-economic perspective. This paper evaluated the socio-environmental and economic benefits of energy generation from MSW in Nigeria using secondary data and various analytical tools such as content analysis, in-depth analysis, critical review and narrative analysis. Some of the principal findings of this paper are that informal MSW recycling is also a contributor to inadequate energy generation from MSW, WTE technologies such as anaerobic digestion (AD) and combined heat and power (CHP) is a promising and viable MSW management strategy that can be adopted in most villages in rural areas in Nigeria. Energy generation from MSW in Nigeria has not been appropriately promoted due to weak environmental policies, inadequate funding and lack of environmental education. As such the need to review Nigerian waste management policies, creation of environmental, economic awareness and enlightenment campaigns and development of appropriate MSW collection and disposal agency in Nigeria should be provided. This paper concludes that it is essential to reassess all legislations regarding waste management in Nigeria with the aim to promote various WTE technologies most especially anaerobic digestion (AD) and combined heat and power (CHP) as these technologies are most suitable in Nigeria where the emissions of GHGs are rapidly increasing.


    In the recent years, it has been realized that fractional calculus has an important role in various scientific fields. Fractional differential equations (FDE), which is a consequence of the development of fractional calculus, have attracted the attention of many researchers working in different disciplines ([28]). Scientific literature has witnessed the appearance of several kinds of fractional derivatives, such as the Riemann-Liouville fractional derivative, Caputo fractional derivative, Hadamard fractional derivative, Grünwald-Letnikov fractional derivative and Caputo-Fabrizio etc (for more details, see [11,13,16,21,22,44,48,51,54]). It is worthy mentioning here that almost all researches have been conducted within Riemann-Liouville or Caputo fractional derivatives, which are the most popular fractional differential operators.

    J. Hadamard suggested a construction of fractional integro-differentiation which is a fractional power of the type (tddt)α. This construction is well suited to the case of the half-axis and is invariant relative to dilation ([53,p. 330]). The dilation is interpreted in various forms in relation to the field of application. Furthermore, Riemann-Liouville fractional integro-differentiation is formally a fractional power (ddt)α of the differentiation operator (ddt) and is invariant relative to translation if considered on the whole axis. On the other hand, the investigations in terms of Hadamard or Grünwald-Letnikov fractional derivatives are comparably considered seldom.

    The boundary value problems defined by FDE have been extensively studied over the last years. Particularly, the study of solutions of fractional differential and integral equations is the key topic of applied mathematics research. Many interesting results have been reported regarding the existence, uniqueness, multiplicity and stability of solutions or positive solutions by means of some fixed point theorems, such as the Krasnosel'skii fixed point theorem, the Schaefer fixed point theorem and the Leggett-Williams fixed point theorem. However, most of the considered problems have been treated in the frame of fractional derivatives of Riemann-Liouville or Caputo types ([12,14,15,41,45]). The qualitative investigations with respect to Hadamard derivative have gained less attention compared to the analysis in terms of Riemann-Liouville and Caputo settings. Recent results on Hadamard FDE can be consulted in ([1,4,5,7,10,17,42,43,52]).

    The physical phenomena in fluctuating environments are adequately described using the so called Langevin differential equation (LDE) which was proposed by Langevin himself in [31,1908] to give an elaborated interpretation of Brownian motion. Indeed, LDE is a powerful tool for the study of dynamical properties of many interesting systems in physics, chemistry and engineering ([9,32,57]). The generalized LDE was introduced later by Kubo in [29,1966], where a fractional memory kernel was incorporated into the equation to describe the fractal and memory properties. Since then the investigation of the generalized LDE has become a hot research topic. As a result, various generalizations of LDE have been offered to describe dynamical processes in a fractal medium. One such generalization is the generalized LDE which incorporates the fractal and memory properties with a disruptive memory kernel. This gives rise to study fractional Langevin equation ([36]). As the intensive development of fractional derivative, a natural generalization of the LDE is to replace the ordinary derivative by a fractional derivative to yield fractional Langevin equation (FLE). The FLE was introduced by Mainardi and Pironi in earlier 1990s ([40]). Afterwards, different types of FLE were introduced and studied in [2,3,8,19,30,34,37,38,39,47,50,60,61,62]. In [3], the authors studied a nonlinear LDE involving two fractional orders in different intervals with three-point boundary conditions. The study of FLE in frame of Hadamard derivative has comparably been seldom; see the papers [27,56] in which the authors discussed Sturm-Liouville and Langevin equations via Caputo-Hadamard fractional derivatives and systems of FLE of Riemann-Liouville and Hadamard types, respectively.

    In the paper by Kiataramkul et al. [27]: Generalized Sturm–Liouville and Langevin equations via Hadamard fractional derivatives with anti-periodic boundary conditions. In particular, the authors initiate the study of the existence and uniqueness of solutions for the generalized Sturm-Liouville and Langevin fractional differential equations of Caputo-Hadamard type ([21]), with two-point nonlocal anti-periodic boundary conditions, by applying the Banach contraction mapping principle. Moreover, two existence results are established via Leray-Schauder nonlinear alternative and Krasnosleskii's fixed point theorem. In addition, the article by W. Sudsutad et al. [56]: Systems of fractional Langevin equations of Riemann-Liouville and Hadamard types subject to the nonlocal Hadamard and standard Riemann-Liouville with multi-point and multi-term fractional integral boundary conditions, respectively. In particular, the authors also studied the existence and uniqueness results of solutions for coupled and uncoupled systems are obtained by Banach's contraction mapping principle, Leray-Schauder's alternative.

    In the present work, we study the existence, uniqueness and stability of solutions for the following FLE with Hadamard fractional derivatives involving local boundary conditions

    {Dα1(D2+λ2)x(t)=f(t,x(t),Dα1[x](t)),t(1,e),D2x(1)=x(1)=0,x(e)=βx(ξ),ξ(1,e], (1.1)

    where 0<α<1, λ,β>0, such that

    sinλ(e1)βsinλ(ξ1),

    Dα1 denotes the Hadamard fractional derivative of order α, D is the ordinary derivative and

    f:[1,e]×C([1,e],R)×C([1,e],R)C([1,e],R),

    is a continuous function.

    Our approach is new and is totally different from the ones obtained in [27,56] in the sense that different fractional derivatives, ordinary and Hadamard fractional order, are accommodated. Different boundary conditions are associated to problem (1.1) such as three point local boundary conditions and associating different fixed point theorems. It is worthwhile to mention that the nonlinear term f in papers [27,56] is independent of fractional derivative of unknown function x(t). But the opposite case is more difficult and complicated. The dependence of the solution on the parameters is discussed, which has not been investigated in [27,56]. It is worth mentioning here that Ulam and generalized Ulam-Hyers-Rassias stability results have not been considered in [27,56]. Furthermore, the presented work illustrates a numerical simulation obtained through a discretization methods for the evaluation of the Hadamard derivative.

    Our method differs from that used by [27,56] in our emphasis on the Schaefer fixed point theorem is utilized to investigate existence results for problem (1.1). We also employ the generalization Gronwall inequality techniques to prove the Ulam stability for problem (1.1), and we use important classical and fractional techniques such as: integration by parts in the settings of Hadamard fractional operators, right Hadamard fractional integral, method of variation of parameters, mean value theorem, Dirichlet formula, differentiating an integral, incomplete Gamma function and discretization methods. To the best of the authors' knowledge, there is no work in literature which treats local boundary value problems on mixed type ordinary differential equations involving the Hadamard fractional derivative using the above mentioned techniques.

    The rest of the paper is organized as follows: In Section 2, we introduce some notations, definitions and lemmas that are essential in our further analysis. In Section 3, we systemically analyze problem (1.1). An equivalent integral equation is constructed for problem (1.1) and some infra structure are furnished for the use of fixed point theorems. The main results of existence and stability are discussed in Sections 4 and 5, respectively. We prove the main results via the implementation of some fixed point theorems and Ulam's approach. We study the solution's dependence on parameters in Section 6. Indeed, we give an affirmative response to the question on how the solution varies when we change the order of differential operator, the initial values or the nonlinear term f. In Section 7, some illustrative examples along with graphical representations are presented to prove consistency with our theoretical findings.

    In this section we introduce notations, lemmas, definitions and preliminary facts which are used throughout this paper. In terms of the familiar Gamma function Γ(t), the incomplete Gamma function γ(α,t) and its complement Γ(α,t) are defined by (see, for details, [16,20])

    γ(α,t)=t0τα1eτdτ,e(t)>0, |arg(t)|<π,

    and

    Γ(α,t)=tτα1eτdτ,

    for all complex t. For fixed α, γ(α,t) is an increasing function of t with limtγ(α,t)=Γ(α). The classical Riemann-Liouville fractional integral of order α for suitable function x is defined as

    Jαa[x](t):=Jαa+[x(τ)](t)=1Γ(α)ta(tτ)α1x(τ)dτ, (2.1)

    for 0<a<t and e(α)>0. The corresponding left-sided Riemann-Liouville fractional derivative of order α is defined by

    Dαa[x](t)=1Γ(nα)(ddt)nta(tτ)nα1x(τ)dτ, (2.2)

    for α[n1,n). However, the left and right Hadamard fractional integrals of order e(α)>0, for suitable function x, introduced essentially by J. Hadamard fractional integral in [18,1892], are defined by

    Jαa+[x](t)=1Γ(α)ta(lntτ)α1x(τ)dττ, (2.3)

    and

    Jαb[x](t)=1Γ(α)bt(lnτt)α1x(τ)dττ, (2.4)

    respectively. Definition (2.3) is based on the generalisation of the nth integral

    Jna[x](t)=tadτ1τ1τ1adτ2τ2τn1ax(τn)dτnτn1Γ(n)ta(lntτ)n1x(τ)dττ,

    where n=[e(α)]+1 and [e(α)] means the integer part of e(α). Hadamard also proposed [18,53] a definition of the fractional integral as

    Jαa[x](t)=tαΓ(α)10(1s)α1x(ts)ds. (2.5)

    It should be emphasized that expression (2.5) contains x(ts) in place of x(s). Therefore we can consider the term s>0 as a variable that describes dilation. As a consequence, using the change of variables τ=ts, would results in the definition of the classical Riemann-Liouville fractional integral. It should be noted that in order to describe the change of dilation we can use the operator Υs (see [53,p. 330]) such that (Υsx)(t)=x(exp(ts)) where s>0. It is known that the dilation of Euclidean geometric figures changes in size while the shape is unchanged. The connection

    Jαa[x]=Υ1sJαaΥs[x],(Υsx)(t)=x(exp(ts)), (2.6)

    allows us to extend various properties of operators Jαa to the case of operators Jαa. It is directly checked that such connections for the operators (2.5) and (2.1) are given by the relations (2.6). The corresponding left-sided Hadamard fractional derivative of order α is defined by

    Dαa[x](t)=δn1Γ(nα)ta(lntτ)nα1x(τ)dττ, (2.7)

    where α[n1,n) and δn=(tD)n is the so-called δ-derivative and Dddt.

    Firstly, from the above definitions, we see the difference between Hadamard derivative and the Riemann–Liouville one. As a clarification, the aforementioned derivatives differ in the sense that the kernel of the integral in the definition of the Hadamard derivative contains a logarithmic function, while the Riemann-Liouville integral contains a power function. On the other hand, the Hadamard derivative is viewed as a generalization of the operator (tD)n, while the Riemann–Liouville derivative is considered as an extension of the classical Euler differential operator (D)n. Secondly, we observe that formally the relationship between Hadamard-type derivatives and Riemann-Liouville derivatives is given by the change of variable tln(t), leading to the logarithmic kernel.

    Supposedly one can reduce the theorems and results to the corresponding ones of Hadamard-type derivatives by a simple change of variables and functions. It is possible to reduce a formula by such a change of operations but not the precise hypotheses under which a formula is valid. As an illustration, the function x(t)=sint is obviously uniformly continuous, but not ln-uniformly continuous on R+, while the function x(t)=sin(lnt) is ln-uniformly continuous but not uniformly continuous on R+. However, the two notions are equivalent on every bounded interval [a,b] with a>0. Besides, the Hadamard derivative (also integral) starts at the initial time a which is bigger than zero, but the Riemann–Liouville derivative (also integral) often begins at the origin (or any other real number). Under certain precise conditions, an equivalence could be obtained between a problem involving Hadamard derivative to another defined using a Riemann Liouville derivative.

    Lemma 2.1. [28] Let e(α)>0, n=[e(α)]+1 and xC[a,+)L1[a,+), then the Hadamard fractional differential equation Dαa[x](t)=0, has a solution

    x(t)=nk=1ck(lnta)αk.

    Further, the following formulas hold

    {JαaDαa[x](t)=x(t)nk=1ck(lnta)αk,DαaJαa[x](t)=x(t), (2.8)

    where ckR,(k=1,2,,n) are arbitrary constants.

    Lemma 2.2. ([21]) If 0<α<1, then

    Dαa[x](t)=1Γ(1α)ta(lntτ)αδ[x(τ)]dττ+x(a)Γ(1α)(lnta)α.

    Theorem 2.3. ([6]) Consider the continuous function x:[a,b]R belongs to C2[a,+) and let ΔT=1nlnba for n1. Denote the time and space grid by

    tN=aexp(NΔT)=an(ba)N, (2.9)

    and xN=x(tN) for N{0,1,2,,n}. Then for all N{1,2,,n},

    Dαa[x](tN)=˜Dαa[x](tN)+O(ΔT),

    where

    ˜Dαa[x](tN)=x(a)Γ(1α)(lntNa)α+ζNk=1(ταNk+1)x(tk)x(tk1)exp(kΔT).tk,

    and limΔT0O(ΔT)=0, here (ταk)=k1α(k1)1α and

    ζ=(ΔT)1αa[1exp(ΔT)]Γ(2α).

    Lemma 2.4. ([26]) If α,β>0, then the following equality holds

    Jαa[τβ](t)=βαtβΓ(α)γ(α,βlnta),

    where a>0 is the starting point in the interval. In particular, for a=0,

    Jα0[τβ](t)=βαtβ.

    The following discussion is essential for our further investigation.

    Remark 2.5. If α,β>0, for t[1,e]. Then

    i) It is easy to verify that

    Jα1[τβ](t)βα(βlnt)ααΓ(α)tβ=(lnt)αΓ(α+1)tβ.

    ii) The function Jα1[sinλ(t1)] is continuous as a result of the continuity of sin function. Furthermore and according to (2.3), we have

    Jα1[sinλ(τ1)](t)Jα1[1](t)=1Γ(1+α)(lnt)α.

    Note that

    Jα1[sinλ(τ1)](1)=limt1+|Jα1[sinλ(τ1)](t)|=0. (2.10)

    iii) From Lemma 2.2, we have

    Dα1[sinλ(τ1)](t)=J1α1[δsinλ(τ1)](t)+0Γ(1+α).(lnt)α=λΓ(1α)t1(lntτ)ατcosλ(τ1)dττλJ1α1[τ](t)λγ(1α,lnt)Γ(1α)tλtΓ(2α)(lnt)1α.

    Remark 2.6. If α>0, for t[1,e]. Then, using the elementary inequality (lns)αsα, we obtain the inequality

    0ρα(t)=t1(lns)αds1α+1maxt[1,e]{tα+11}=eα+1α+1. (2.11)

    Utilizing the particular case of the Fubini's theorem, one can deduce that

    t1Jα1[x](s)ds=1Γ(α)t1ρα1(ts)x(s)ds. (2.12)

    Indeed, interchanging the order of integration with the help of (2.3) and (2.4) and it follows that

    t1Jα1+[x](s)ds=t1s×Jα1[x](s)dss=1Γ(α)t1s×(s1(lnsτ)α1x(τ)dττ)dss.=1Γ(α)t1x(τ)(tτs(lnsτ)α1dss)dττ=t1x(τ)Jαt[τ](s)ds.

    If we take v=sτ, then

    t1x(τ)Jαt[τ](s)ds=1Γ(α)t1x(τ)(tτ1(lnv)α1dv)dτ=1Γ(α)t1x(τ)ρα1(tτ)dτ.

    Following [48], we bring a formula generalizing the well-known rule of differentiating an integral with respect to its upper limit which serves also as a parameter of the integrand

    ddtq(t)p(t)G(t,τ)dτ=q(t)p(t)tG(t,τ)dτ+G(t,q(t))dq(t)dtG(t,p(t))dp(t) dt. (2.13)

    From (2.7), we have for α(0,1) and t(a,b) that

    Dαa[saG(s,τ)dτ](t)=1Γ(1α)tddtta(lnts)α[saG(s,τ)dτ]dss.

    Interchanging the order of integration and applying Dirichlet formula, we obtain

    Dαa[saG(s,τ) dτ](t)=1Γ(1α)tddtta(lnts)α[saG(s,τ)dτ]dss=tddtta(1Γ(1α)tτ(lnts)αG(s,τ)dss)dτ=tddttaJ1ατ[G(s,τ)](t)dτ=tattJ1ατ[G(s,τ)](t)dτ+tlimτtaJ1ατ[G(s,τ)](t)=taDατ[G(s,τ)](t)dτ+tlimτta J1ατ[G(s,τ)](t).

    In particular, we get

    Dαa[saG(s,τ)h(τ)dτ](t)=taDατ[G(s,τ)](t)h(τ)dτ+tlimτta(h(τ)J1ατ[G(s,τ)](t)). (2.14)

    To simplify the presentation, we let

    fx(t)=f(t,x(t),Dα1[x](t)),g(ts)=sinλ(ts). (2.15)

    In virtue of equation (2.14), we deduce that

    Dα1(s1g(sτ)Jα1[fx](τ)dτ)(t)=t1Dατ[g(sτ)](t)Jα1[fx](τ)dτ+tlimτt1(Jα1[fx](τ)J1ατ[G(s,τ)](t)).

    Applying a suitable shift in the fractional operators with lower terminal τ, we deduce the next property [23,24].

    Property 2.1. Let 0<α<1, Dα1[g]L1(1,e) and Jα1[fx]C(1,e). Then we have

    Dα1(t1g(ts)Jα1[fx](s)ds)(t)=t1Dα1[g(s1)](τ)Jα1[fx](tτ+1)dτ+tJα1[fx](t)limτ1+(J1α1[g(s1)](τ)).

    In the literature, we can read the following Schaefer fixed point theorem.

    Lemma 2.7. [21,55] Let E be a Banach space and assume that Ψ:EE is a completely continuous operator. If the set

    Λ={xE:x=μΨx:0<μ<1},

    is bounded, then Ψ has a fixed point in E.

    The next result is a generalization of Gronwall inequality due to Pachpatte ([46]).

    Lemma 2.8. Let uC(I,R+), ˜a(t,s), ˜b(t,s)C(D,R+) and ˜a(t,s), ˜b(t,s) are nondecreasing in t for each sI, where I=[˜α,˜β], R+=[0,),

    D={(t,s)I×I:˜αst˜β},

    and suppose that

    u(t)k+t˜α˜a(t,s)u(s)ds+˜β˜α˜b(t,s)u(s)ds,

    for tI, where k0 is a constant. If

    p(t)=˜β˜α˜b(t,s)exp(s˜α˜a(s,τ)dτ)ds<1,

    for tI, then

    u(t)k1p(t)exp(t˜α˜a(t,s)ds).

    The following hypotheses will be used in the sequel:

    H1: There exist a constant Ni>0 (i=1,2) such that

    |f(t,x1,˜x1)f(t,x2,˜x2)|N1|x1x2|+N2|˜x1˜x2|,

    for each t[1,e] and all xi,˜xiR.

    H2: There exists a constant L>0 such that |f(t,x,˜x)|L, for each t[1,e] and all x,˜xR.

    In order to study the nonlinear problem (1.1), we first consider the associated linear problem and obtain its solution:

    Dα1(D2+λ2)[x](t)=h(t),

    for 0<α1, where h is a continuous function on [1,e].

    Lemma 3.1. The general solution of the linear differential equation

    (D2+λ2)x(t)=˜x(t), (3.1)

    for t[1,e], is given by

    x(t)=1λt1sinλ(ts)˜x(s)ds+c1cosλt+c2sinλt,

    where c1,c2 are unknown arbitrary constants.

    Proof. Assume that x(t) satisfies (3.1), then the method of variation of parameters implies the desired results.

    Lemma 3.2. Let 0<α<1, hC([1,e],R). Then the unique solution of the linear problem

    {Dα1[˜x](t)=h(t),˜x(1)=0, (3.2)

    for t(1,e), is equivalent to the integral equation

    ˜x(t)=Jα1[h](t)=1Γ(α)t1(lntτ)α1h(τ)dττ. (3.3)

    Proof. Applying Lemma 2.1, we may reduce (3.2)-a to an equivalent integral equation

    ˜x(t)=Jα1[h](t)+c0(lnt)α1,

    where c0R. In view of the boundary condition ˜x(1)=0, we have c0=0, thus (3.3) holds.

    Lemma 3.3. Let hC([1,e],R),α(0,1] and 1<ξ<e. Then the fractional problem

    {Dα1(D2+λ2)[x](t)=h(t),x(1)=D2[x](1)=0,x(e)=βx(ξ), (3.4)

    has a unique solution given by

    x(t)=1λt1sinλ(ts)[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds+βΔsinλ(t1)ξ1sinλ(ξs)×[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds1Δsinλ(t1)e1sinλ(es)[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds, (3.5)

    where

    Δ=λ(sinλ(e1)βsinλ(ξ1))0. (3.6)

    Proof. Assuming

    (D2+λ2)[x](t)=˜x(t)

    and then applying Lemma 3.1 when 0<α<1, we get

    x(t)=1λt1sinλ(ts)˜x(s)ds+c1cosλt+c2sinλt.

    By the boundary condition x(1)=0 and privous equation, we conclude that

    c1cosλ=c2sinλ. (3.7)

    On the other hand, x(e)=βx(ξ), combining with

    x(e)=1λe1sinλ(es)˜x(s)ds+c1cosλe+c2sinλe,

    and

    x(ξ)=1λξ1sinλ(ξs)˜x(s)ds+c1cosλξ+c2sinλξ,

    yield

    c2=cosλΔ(βξ1sinλ(ξs)˜x(s)dse1sinλ(es)˜x(s)ds),

    where Δ is given by (3.6). If λ=(2k+1)π2, k=0,1,, then c2=0, and by (3.7), we get

    c1=2(2k+1)π[cos(2k+1)πe2βcos(2k+1)πξ2]1×[βξ1sin(2k+1)π2(ξs)˜x(s)dse1sin(2k+1)π2(es)˜x(s)ds],

    otherwise, we find

    c1=sinλΔ(βξ1sinλ(ξs)˜x(s)dse1sinλ(es)˜x(s)ds).

    The above two expressions of c1 are equivalent for the particular choice of λ. Substituting these values of c1 and c2 in (3.7) and applying Lemma 3.2, we finally obtain (3.5). So, the unique solution of problem (3.4) is given by (3.5). Conversely, let x(t) be given by formula (3.5), operating D2 on both sides and using (2.13), we get

    D2x(t)=λt1sinλ(ts)[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds+1Γ(α)t1(lntτ)α1h(τ)dττβλ2Δsinλ(t1)ξ1sinλ(ξs)[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds+λ2Δsinλ(t1)e1sinλ(es)[1Γ(α)s1(lnsτ)α1h(τ)dττ]ds.

    Hence

    (D2+λ2)[x](t)=1Γ(α)t1(lntτ)α1h(τ)dττ.

    Operating Dα1 on the above relation and using (2.8), we obtain the first equation of (3.4). Further, it is easy to get that all conditions in (3.4) are satisfied. The proof is completed.

    By virtue of Lemma 3.3, we get the following result.

    Lemma 3.4. Let 0<α<1, λ>0. Then the problem (1.1) is equivalent to the integral equation

    x(t)=1λt1g(ts)Jα1[fx](s)ds+1Δg(t1)[βξ1g(ξs)Jα1[fx](s)dse1g(es)Jα1[fx](s)ds]. (3.8)

    For convenience, we define the following functions

    ϕx(t)=t1g(ts)Jα1[fx](s)ds (3.9)

    and

    Hx(ξ,β)=1Δ(βϕx(ξ)ϕx(e)). (3.10)

    Then, the integral equation (3.8) can be written as

    x(t)=1λϕx(t)+Hx(ξ,β)g(t1). (3.11)

    From the expressions of (3.5) and (3.8), we can see that if all conditions in Lemmas 3.3 and 3.4 are satisfied, then the solution is a continuous solution of the boundary value problem (1.1). Let C=C([1,e],R) be a Banach space of all continuous functions defined on [1,e] endowed with the usual supremum norm. Consider the space defined by

    E={x:xC,Dα1[x]C},

    equipped with the norm xE=x+Dα1[x], then (E,.E) is a Banach space. On this space, by virtue of Lemma 3.4, we may define the operator Ψ:EE by

    Ψx(t)=1λϕx(t)+Hx(ξ,β)g(t1),

    where g(t1), ϕx(t) and Hx(ξ,β) defined by (2.15), (3.9) and (3.10) respectively. Then

    Dα1[Ψx](t)=1λDα1[ϕx](t)+Hx(ξ,β)Dα1[g(t1)]. (3.12)

    By virtue of Property 2.1 and Eq (2.10) in Remark 2.5, we get the following

    Dα1[ϕx](t)=t1Dα1[g(τ1)](s)Jα1[fx](ts+1)ds. (3.13)

    The continuity of the functional f would imply the continuity of Ψx and Dα1[Ψx]. Hence the operator Ψ maps the Banach space E into itself. This operator will be used to prove our main results. Next section, we employ fixed point theorems to prove the main results of this paper. In view of Lemma 3.4, we transform problem (1.1) as

    x=Ψx,xE. (3.14)

    Observe that problem (1.1) or (3.8) has solutions if the operator Ψ in (3.14) has fixed points. For computational convenience, we set the notations:

    0ρ(t):=1λρα(t)+1|Δ|(βρα(ξ)+ρα(e))Mρ, (3.15)

    and

    0σα(t):=t1s(lns)1α(ln(ts+1))αdsMσ, (3.16)

    where

    Mρ:=1λ+1|Δ|(β+1),Mσ:=max{t1s2α(ts+1)αds:t[1,e]}, (3.17)

    and

    Q1Γ(1+α)[Mρ+1Γ(2α)(Mσ+λβρα(ξ)+ρα(e)|Δ|e2α)]. (3.18)

    In this section, we establish the existence and uniqueness results via fixed point theorems.

    Theorem 4.1. Assume that f:[1,e]×C×CC is a continuous function that satisfies (H1). If we suppose

    N=max{N1,N2},NQ<1, (4.1)

    where Q is defined in (3.18), then problem (3.14) has a unique solution in E.

    Proof. To prove this theorem, we need to prove that the operator Ψ has a fixed point in E. So, we shall prove that Ψ is a contraction mapping on E. For any x,˜xE and for each t[1,e], we have

    |Ψ˜x(t)Ψx(t)|1λ|ϕ˜x(t)ϕx(t)|+|H˜x(ξ,β)Hx(ξ,β)||g(t1)|, (4.2)

    where x(t) and ˜x(t) are defined in Lemma 3.4. From assumption (H1) and Eqs (3.9) and (4.1), we obtain

    |ϕ˜x(t)ϕx(t)|=|t1g(ts)Jα1[f˜x(τ)fx(τ)](s)ds|supt[1,e]|f˜x(t)fx(t)||t1Jα1[1]ds|supt[1,e]N(|˜x(t)x(t)|+|Dα1˜x(t)Dα1x(t)|)Γ(1+α)t1(lns)αdsρα(t)Γ(1+α)N(M1+M2), (4.3)

    where ρα(t) is given by (2.11) and

    M1=supt[1,e]|˜x(t)x(t)|,M2=supt[1,e]|Dα1˜x(t)Dα1x(t)|.

    Similarly, we can obtain |ϕ˜x(ξ)ϕx(ξ)| and |ϕ˜x(e)ϕx(e)|. Then

    |H˜x(ξ,β)Hx(ξ,β)|1|Δ|[β|ϕ˜x(ξ)ϕx(ξ)|+|ϕ˜x(e)ϕx(e)|]βρα(ξ)+ρα(e)|Δ|Γ(1+α)N(M1+M2). (4.4)

    Linking (4.2), (4.3) and (4.4), for every x, ˜xE, we get

    |Ψ˜x(t)Ψx(t)|ρ(t)Γ(1+α)N(M1+M2),

    where ρ(t) is given by (3.15). Consequently, it yields that

    Ψ˜xΨxQ1N(˜xx+Dα1˜xDα1x), (4.5)

    with

    Q1max{ρ(t)Γ(1+α):t[1,e]}. (4.6)

    On the other hand, we observe that

    |Dα1[Ψ˜x](t)Dα1[Ψx](t)|1λ|Dα1[ϕ˜x](t)Dα1[ϕx](t)|+|H˜x(ξ,β)Hx(ξ,β)||Dα1[g(t1)]|. (4.7)

    By (3.13), we have

    |Dα1[ϕ˜x](t)Dα1[ϕx](t)|=|t1[Dα1[g(t1)](s)][(Jα1[f˜xfx])(ts+1)]ds|supt[1,e]|f˜x(t)fx(t)|t1|Dα1[g(t1)](s)|Jα1[1](ts+1)ds. (4.8)

    Taking into account that

    R11(t)=t1|Dα1[g(t1)](s)|Jα1[1](ts+1)dst1λ|γ(1α,lns)Γ(1α)s|(ln(ts+1))αΓ(1+α)dsλΓ(2α)Γ(1+α)t1s(lns)1α(ln(ts+1))αdsλΓ(2α)Γ(1+α)σα(t), (4.9)

    we have,

    |Dα1[ϕ˜x](t)Dα1[ϕx](t)|λσα(t)Γ(2α)Γ(1+α)N(M1+M2), (4.10)

    where σα(t) is given by (3.16). Therefore, from (4.7), (4.7) and (4.10), we have

    |Dα1[Ψ˜x](t)Dα1[Ψx](t)|N(M1+M2)Γ(1+α)(σα(t)Γ(2α)+R12(t)), (4.11)

    where

    R12(t)=βρα(ξ)+ρα(e)|Δ|λt|γ(1α,lnt)|Γ(1α)λ(βρα(ξ)+ρα(e))|Δ|Γ(2α)(lnt)1αt. (4.12)

    This gives

    Dα1[Ψ˜x]Dα1[Ψx]Q2N(˜xx+Dα1˜xDα1x), (4.13)

    with

    Q21Γ(1+α)Γ(2α)max{σα(t)+λβρα(ξ)+ρα(e)|Δ|(lnt)1αt:t[1,e]}. (4.14)

    By (4.5) and (4.13), we can write

    Ψ˜xΨxEQN˜xxE, (4.15)

    with QQ1+Q2. Combining (4.1) with (4.15), we conclude that Ψ is contractive on E. As a consequence of Banach fixed point theorem, we deduce that Ψ has a unique fixed point which is a solution of our problem in E.

    Corollary 4.2. Let the assumptions of the Theorem 4.1 be fulfilled. If we suppose that (4.1) holds, with Q is defined as

    Q=1Γ(1+α)[Mρ+1Γ(2α)(Mσ+λβ+1|Δ|e)], (4.16)

    then, problem (3.14) has a unique solution in E.

    Let BrE be bounded, i.e., there exists a positive constant r>0 such that xE<r for all xBr. then Br is a closed ball in the Banach space E, hence it is also a Banach space. The restriction of Ψ on Br is still a contraction by Theorem 4.1. Then, problem (3.14) has a unique solution in Br if Ψ(Br) Br.

    Theorem 4.3. Assume that f:[1,e]×C×CC is a continuous function that satisfies (H1). If we suppose that (4.1) holds, with Q is defined in (3.18), then problem (3.14) has a unique solution in Br.

    Proof. Now we show that Ψ(Br)Br, that is ΨxEr whenever xEr. Denoting

    Lb=N1supt[1,e]|x(t)|+N2supt[1,e]|Dα1x(t)|+L0,

    where L0=max{|f(t,0,0|:t[1,e]}. Observe that

    |fx(t)|=|fx(t)f0(t)+f0(t)||fx(t)f0(t)|+|f0(t)|Lb.

    So, we have

    |ϕx(t)|=|t1g(ts)Jα1[fxf0+f0](s)ds|supt[1,e](|fx(t)f0(t)|+|f0(t)|)t1Jα1[1](s)dsρα(t)Γ(1+α)Lb

    and

    |Hx(ξ,β)|βρα(ξ)+ρα(e)Γ(1+α)|Δ|Lb. (4.17)

    Then |Ψx(t)|ρ(t)Γ(1+α)Lb. Therefore,

    ΨxQ1(N(x+Dα1x)+L0), (4.18)

    where Q1 is given by (4.6). On the other hand, we have

    |Dα1[Ψx](t)||Dα1[ϕx](t)|λ+β|ϕx(ξ)|+|ϕx(e)||Δ||Dα1(g(t1))|. (4.19)

    Thanks to (H1), it yields that

    |Dα1[ϕx](t)||t1Dα1[g(t1)](s)Jα1[fxf0+f0](ts+1)ds|Lb[t1[Dα1[g(t1)](s)]Jα1[1](ts+1)ds].

    This gives

    |Dα1[ϕx](t)|λσα(t)Γ(1+α)Γ(2α)Lb, (4.20)

    where σα(t) is given by (3.16). Consequently, by (4.17), (4.19) and (4.20), we have

    Dα1[Ψx]Q2(N(x+Dα1x)+L0), (4.21)

    where Q2 is given by (4.14). Using (4.18) and (4.21), we obtain

    ΨxEQ(N(x+Dα1x)+L0),

    and we find that ΨxEQ(Nr+L0)r, where we choose rL0(Q1N)1. Hence, the operator Ψ maps bounded sets into bounded sets in Br, therefore Ψ is a contraction. Thus, the conclusion of the theorem follows by the contraction mapping principle.

    Corollary 4.4. Assume that f:[1,e]×C×CC is a continuous function that satisfies (H1). If we suppose N=max{N1,N2} and

    L0(Q1N)1<r,

    where Q is defined in (4.16). Then, problem (3.14) has a unique solution in Br.

    Our second result will use the Scheafer fixed point theorem.

    Theorem 4.5. The problem (3.14) has at least one solution defined on E, whenever assumption (H2) be hold.

    Proof. The proof will be given in several steps.

    Step 1: We show that Ψ is continuous. Let us consider a sequence {xn}E converging to x. For each t[1,e], we have

    |Ψxn(t)Ψx(t)|1λ|ϕxn(t)ϕx(t)|+|Hxn(ξ,β)Hx(ξ,β)||g(t1)|,

    where

    |ϕxn(t)ϕx(t)|=|t1g(ts)Jα1[fxnfx](s)ds|ρα(t)Γ(1+α)|fxn(t)fx(t)|. (4.22)

    Similarly, we can obtain

    |Hxn(ξ,β)Hx(ξ,β)|βρα(ξ)+ρα(e)|Δ|Γ(1+α)|fxn(t)fx(t)|. (4.23)

    Thus, from (4.19), (4.22) and (4.23), we have

    |Ψxn(t)Ψx(t)|ρ(t)Γ(1+α)|fxn(t)fx(t)|. (4.24)

    If (t,x)[1,e]×E, xnx as n and f is continuous, then (4.24) gives

    ΨxnΨx0, (4.25)

    as n. On the other hand, from (4.11) we observe that

    |Dα1[Ψxn](t)Dα1[Ψx](t)|1Γ(1+α)(σα(t)Γ(2α)+R12)|fxn(τ)fx(τ)|,

    where R12 is given by (4.12). Thus

    Dα1[Ψxn]Dα1[Ψx]0, (4.26)

    as n. Since the convergence of a sequence implies its boundedness, therefore, there exists r>0 such that xnr,xr and hence f is uniformly continuous on the compact set

    {(t,x(t),Dα1[x](t)):t[1,e],xr1,Dα1[x]r2}.

    By (4.25) and (4.26), we can write [Ψxn][Ψx]E0 as n. This shows that Ψ is continuous.

    Step 2: Now we show that the operator Ψ:EE maps bounded sets into bounded sets in E. Let BrE be bounded, i.e., there exists a positive constant r>0 such that xEr for all xBr. Let

    L=max{|f(t,x(t),Dα1[x](t))|:t[1,e]0<xr,Dα1[x]r},

    then, for xBr, we have

    |ϕx(t)|=|t1g(ts)Jα1[fx](s)ds|ρα(t)Γ(1+α)L (4.27)

    and

    |Hx(ξ,β)|βρα(ξ)+ρα(e)|Δ|Γ(1+α)L. (4.28)

    Then from (4.27) and (4.28), we get |Ψx(t)|ρ(t)Γ(1+α)L. Therefore,

    ΨxQ1L. (4.29)

    According to Property 2.1, we should have

    |Dα1[ϕx](t)|=|t1Dα1[g(t1)](s)Jα1[fx](ts+1)ds|λσα(t)Γ(1+α)Γ(2α)L. (4.30)

    Consequently, by (3.12), (4.28) and (4.30), we have

    Dα1ΨxQ2L. (4.31)

    Using (4.29) and (4.31), we obtain ΨxEQL. Hence, the operator Ψ maps bounded sets into bounded sets in E. Next we show that Ψ maps bounded sets into equicontinuous sets of Br.

    Step 3: In this step, we show that Ψ(Br) is equicontinuity. Let t1,t2[1,e] such that t1<t2. Then we obtain

    |Ψx(t2)Ψx(t1)|1λ|ϕx(t2)ϕx(t1)|+|Hx(ξ,β)||g(t21)g(t11)|. (4.32)

    We can show that

    |ϕx(t2)ϕx(t1)|=|t21g(t2s)Jα1[fx](s)dst11g(t1s)Jα1[fx](s)ds|t11|g(t2s)g(t1s)||Jα1[fx](s)|ds+t2t1|g(t2s)||Jα1[fx](s)|dsLt11|g(t2s)g(t1s)|Jα1[1](s)ds+Lt2t1|g(t2s)|Jα1[1](s)dsLt11|λt2t1cosλ(τs)dτ|Jα1[1](s)ds+Lt2t1Jα1[1](s)dsLΓ(1+α)[λ|t2t1|t11(lns)αds+t2t1(lns)αds]. (4.33)

    Hence

    |ϕx(t2)ϕx(t1)|LΓ(2+α)[λ|t2t1||tα+111|+|tα+12tα+11|]. (4.34)

    It is easy to find that

    |g(t21)g(t11)|=|λt2t1cosλ(τ1)dτ|λ|t2t1|.

    Therefore by (4.28), (4.32), (4.33) and (4.34) we have

    |Ψx(t2)Ψx(t1)|1λ|ϕx(t2)ϕx(t1)|+|Hx(ξ,β)||g(t21)g(t11)|LΓ(2+α)[|t2t1||tα+111|+1λ|tα+12tα+11|]+λLβρα(ξ)+ρα(e)|Δ|Γ(1+α)|t2t1|. (4.35)

    We have also,

    |Dα1[Ψx](t2)Dα1[Ψx](t1)|1λ|Dα1[ϕx](t2)Dα1[ϕx](t1)|+|Hx(ξ,β)||Dα1[g(t21)]Dα1[g(t11)]|. (4.36)

    Thus, we obtain

    |Dα1[ϕx](t2)Dα1[ϕx](t1)||t21Dα1[g(τ1)](s)Jα1[fx](t2s+1)dst11Dα1[g(τ1)](s)Jα1[fx](t1s+1)ds|t11|Dα1[g(τ1)](s)||Jα1[fx](t2s+1)Jα1[fx](t1s+1)|ds+t2t1|Dα1[g(τ1)](s)|Jα1[fx](t2s+1)ds.

    We find that

    |Jα1[fx](t2s+1)Jα1[fx](t1s+1)|L|Jα1[1](t2s+1)Jα1[1](t1s+1)|=LΓ(α)|t2s+11(lnt2s+1τ)α1dττt1s+11(lnt1s+1τ)α1dττ|=LΓ(α)t1s+11|(lnt2s+1τ)α1(lnt1s+1τ)α1|dττ+LΓ(α)t2s+1t1s+1(lnt2s+1τ)α1dττLΓ(1+α)[2(lnt2s+1t1s+1)α+(lnt2s+1)α(lnt1s+1)α]. (4.37)

    Note that

    |Jα1[fx](t2s+1)Jα1[fx](t1s+1)|,

    is independent of x. Therefore

    |Dα1[ϕx](t2)Dα1[ϕx](t1)|LΓ(1+α)t11[2(lnt2s+1t1s+1)α+(ln(t2s+1))α(ln(t1s+1))α]ds+λL(σα(t2)σα(t1))Γ(2α)Γ(1+α). (4.38)

    In accordance with (4.35), (4.36), (4.37) and (4.38), we deduce that

    Ψx(t2)Ψx(t1)+Dα1[Ψx](t2)Dα1[Ψx](t1)0,

    as |t2t1|0. Hence the sets of functions {Ψx(t):xBr} and

    {Dα1[Ψx](t):xBr},

    are bounded in Br and equicontinuous on [1,e]. Thus, by the Arzelá–Ascoli Theorem, the mapping Ψ is completely continuous on E.

    Step 4: In the last step, it remains to show that the set defined by

    Λ={xE:x=μΨx for some 0<μ<1},

    is bounded. Let x be a solution. Then, for t[1,e] and using the computations in proving that Ψ is bounded, we have |x(t)|=|μ(Ψx)(t)|. Let xΛ, x=μΨx for some 0<μ<1. Thus, by (4.29), for each t[1,e], we have

    x=μΨxΨxQ1L, (4.39)

    for μ(0,1). On the other hand, by (4.31), we have

    Dα1[x]=μDα1[Ψx]Q2L. (4.40)

    It follows from (4.39) and (4.40) that xE=x+Dα1[x]QL<, where Q defined by (3.18). This implies that the set Λ is bounded independently of μ(0,1). Therefore, Λ is bounded. As a conclusion of Schaefer fixed point theorem, we deduce that Ψ has at least one fixed point, which is a solution of (3.14). The proof is completed.

    Remark 4.6. Let ε>0 and choose a number η>0 such that

    ε>2LλΓ(2+α)min{κ1(η),κ2(η)}+2λLβρα(ξ)+ρα(e)|Δ|Γ(1+α)η, (4.41)

    where

    κ1(η)=λη(eα+11)+e(α+1)ηα,κ2(η)=λη(ηα+11)+((2η)α+11).

    Assume t1,t2[1,e]; t1<t2 such that t2t1η. It obvious that t2>η and there are two possibilities for t1 and η.

    Case 1: For ηt1<t2e, by means of mean value theorem of differentiation implies that there exists t(t1,t2), such that

    tα+12tα+11=(α+1)(t2t1)tαη(α+1)ttα1e(α+1)ηα,

    whence, we obtain

    |ϕx(t2)ϕx(t1)|LΓ(2+α)[λη(eα+11)+e(α+1)ηα]=LΓ(2+α)κ1(η).

    Case 2: For 1t1<η<t2e and so t2<2η. These imply that

    |ϕx(t2)ϕx(t1)|LΓ(2+α)[λη(ηα+11)+((2η)α+11)]=LΓ(2+α)κ2(η).

    Combining Case 1 and 2, we obtain

    |ϕx(t2)ϕx(t1)|LΓ(2+α)min{κ1(η),κ2(η)}.

    Now, it is obvious by (4.35) and (4.41) that |Ψx(t2)Ψx(t1)|ε2. A similar argument can be applied to obtain

    |Dα1[Ψx](t2)Dα1[Ψx](t1)|ε2.

    Corollary 4.7. Suppose that the conditions of Theorem 4.5 hold, with Q is defined as (4.16). Then, problem (3.14) has at least one solution defined on [1,e].

    For the study of Hyers-Ulam-Rassias and generalized Ulam-Hyers-Rassias stabilities of problem (1.1) on a compact interval [1,e], we adopt the following definitions [22,35,49,58,59].

    Definition 5.1. Problem (1.1) is Ulam-Hyers-Rassias stable with respect to φC([1,e],R+) if for each ϵ>0 and each solution ˜x of the inequality

    |Dα1(D2+λ2)˜x(t)f(t,˜x(t),Dα1[˜x](t))|ϵφ(t), (5.1)

    for t[1,e], there exists a real number cφ>0 and a solution x of problem (1.1) such that

    |˜x(t)x(t)|ϵcφφ(t),

    for t[1,e]. Particularly, in the case that φ is identity function on [1,e], problem (1.1) is called Ulam-Hyers stable. Moreover, if there exists ψC(R+,R+),ψ(0)=0, such that |˜x(t)x(t)|ψ(ϵ), for t[1,e], then problem (1.1) is called generalized Ulam-Hyers stable.

    Definition 5.2. Problem (1.1) is generalized Ulam-Hyers-Rassias stable with respect to a function φC([1,e],R+) if for each solution ˜x of the inequality

    |Dα1(D2+λ2)˜x(t)f(t,˜x(t),Dα1[˜x](t))|φ(t), (5.2)

    for t[1,e], there exist a real number cφ>0 and a solution x of problem (1.1) such that |˜x(t)x(t)|cφφ(t), for t[1,e].

    Remark 5.3. A function ˜xC is a solution of the inequality (5.1) if and only if there exists a function hC (which depends on ˜x) such that for all t[1,e],

    (i) |h(t)|ϵφ(t).

    (ii) Dα1(D2+λ2)˜x(t)=f(t,˜x(t),Dα1[˜x](t))+h(t).

    Lemma 5.4. Let 0<α<1, if ˜xC is a solution of the inequality (5.1)(or (5.2)) then ˜x is a solution of the following integral inequality

    |˜x(t)1λϕ˜x(t)H˜x(ξ,β)g(t1)|ϵω(t), (5.3)

    for t\in \left[1, e\right] , where

    \begin{equation} \tilde{x}(1) = 0 = \mathrm{D}^{2}\tilde{x}\left( 1\right), \qquad \tilde{x}(e) = \beta \tilde{x}(\xi ), \end{equation} (5.4)

    for \xi \in (1, e] and

    \begin{align*} \omega (t) & = \frac{1}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[\varphi \right] \left( s\right) \mathrm{d}s + \frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s +\frac{1}{\left\vert \Delta \right\vert } \int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s. \end{align*}

    Proof. By using Remark 5.3-ii, we have

    \mathcal{D}_{1}^{\alpha }\left( \mathrm{D}^{2}+\lambda^{2}\right) \tilde{x} (t) = f(t, \tilde{x}(t), \mathcal{D}_{1}^{\alpha }\left[ \tilde{x}\right] (t))+h\left( t\right).

    In accordance with Lemma 3.4, we deduce \tilde{x}(t) = \frac{1}{\lambda }\phi_{\tilde{x}, h}\left(t\right) +H_{\tilde{x}, h}\left(\xi, \beta \right) g\left(t-1\right) , where

    \phi_{\tilde{x}, h}\left( t\right) = \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{\tilde{x}}+h\right] \left( s\right) \mathrm{d}s

    and H_{\tilde{x}, h}\left(\xi, \beta \right) = \frac{1}{\Delta }\left(\beta \phi_{\tilde{x}, h}\left(\xi \right) -\phi_{\tilde{x}, h}\left(e\right) \right) . Hence

    \begin{align*} \tilde{x}(t) & = \frac{1}{\lambda }\phi_{\tilde{x}}\left( t\right) + H_{\tilde{x}}\left( \xi, \beta \right) g\left( t-1\right) + \frac{1}{ \lambda}\int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha } \left[ h\right] \left( s\right) \mathrm{d}s\\ &\quad +g\left( t-1\right) \frac{\beta }{\Delta }\int_{1}^{\xi }g\left( \xi -s\right) \mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s -\frac{1}{\Delta }g\left( t-1\right) \int_{1}^{e}g\left( e-s\right) \mathcal{J}_{1}^{\alpha } \left[ h\right] \left( s\right) \mathrm{d}s. \end{align*}

    Accordingly, we easily deduce equation (5.3).

    By virtue of Remark 5.3-i, it can be easily seen that

    \begin{align*} \left\vert \phi_{\tilde{x}, h}\left( t\right) -\phi_{x}\left( t\right) \right\vert &\leq \left\vert \phi_{\tilde{x}}\left( t\right) -\phi _{x}\left( t\right) \right\vert +\left\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d} s\right\vert \\ &\leq \left\vert \phi_{\tilde{x}}\left( t\right) -\phi_{x}\left( t\right) \right\vert +\epsilon \int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s. \end{align*}

    Similar arguments can be applied as in (4.3) to deduce that

    \begin{equation*} \left\vert \phi_{\tilde{x}}\left( t\right) -\phi_{x}\left( t\right) \right\vert \leq \frac{N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) }\left\Vert \tilde{x}-x\right\Vert_{E}. \end{equation*}

    Now, it is obvious that

    \begin{equation} \left\vert \phi_{\tilde{x}, h}\left( t\right) -\phi_{x}\left( t\right)\right\vert \leq \frac{N\rho_{\alpha }\left( t\right) }{\Gamma \left(1+\alpha \right) }\left\Vert \tilde{x}-x\right\Vert_{E}+\epsilon \int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right)\mathrm{d}s, \end{equation} (5.5)

    and

    \begin{align} \left\vert H_{\tilde{x}, h}\left( \xi, \beta \right) -H_{x}\left( \xi , \beta \right) \right\vert &\leq \frac{1}{\left\vert \Delta \right\vert }\left( \beta \left\vert \phi_{\tilde{x}, h}\left( \xi \right) -\phi_{x}\left( \xi \right) \right\vert +\left\vert \phi_{x}\left( e\right) -\phi_{\tilde{x}, h}\left( e\right) \right\vert \right) \\ &\leq \frac{N}{\left\vert \Delta \right\vert \Gamma \left( 1+\alpha \right) }\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) \left\Vert \tilde{x}-x\right\Vert_{E} \\ &\quad +\frac{\epsilon }{\left\vert \Delta \right\vert }\left( \beta \int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s + \int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s\right). \end{align} (5.6)

    Theorem 5.5. Assume that the conditions of Theorem 4.1 and (5.1), (5.4) hold. Then, problem (1.1) is Ulam-Hyers-Rassias stable with respect to positive constant functions. Particularly, problem (1.1) is Ulam-Hyers stable and generalized Ulam-Hyers stable.

    Proof. Using Theorem 4.1, there exists a unique solution x\in \mathcal{C} of problem (1.1) that is given by integral equation (3.11). Let \tilde{x}\in\mathcal{C} be any solution of the inequality (5.1), then by (5.5) and (5.6), we have

    \begin{align*} \left\vert \tilde{x}(t)-x\left( t\right) \right\vert & = \left\vert \frac{1}{\lambda }\phi_{\tilde{x}, h}\left( t\right) +H_{\tilde{x}, h}\left( \xi, \beta \right) g\left( t-1\right) -\frac{1}{\lambda } \phi_{x}\left( t\right) -H_{x}\left( \xi, \beta \right) g\left( t-1\right) \right\vert \\ &\leq \frac{1}{\lambda }\left\vert \phi_{\tilde{x}, h}\left( t\right) -\phi_{x}\left( t\right) \right\vert +\left\vert H_{\tilde{x}, h}\left( \xi, \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert \left\vert g\left( t-1\right) \right\vert \\ &\leq \frac{N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) } \left\Vert \tilde{x}-x \right\Vert_{E} +\epsilon \bigg[ \frac{1}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha } \left[ \varphi \right] \left( s\right) \mathrm{d}s \\ & \quad +\frac{\beta }{\left\vert \Delta \right\vert } \int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s + \frac{1}{\left\vert \Delta \right\vert } \int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d} s \bigg], \end{align*}

    where x\left(t\right) and \rho \left(t\right) are as in (3.8) and (3.15). Thus

    \begin{align*} \left\Vert \tilde{x}-x\right\Vert_{E} &\leq \frac{\epsilon \Gamma \left( 1+\alpha \right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}\bigg[ \frac{1 }{\lambda}\int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right]\left( s\right) \mathrm{d}s \\ &\quad +\frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi } \mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s+ \frac{1}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J}_{1}^{\alpha } \left[ \varphi \right] \left( s\right) \mathrm{d}s\bigg], \end{align*}

    since by (3.18), we get 0 < \frac{NM_{\rho }}{ \Gamma \left(1+\alpha \right) } < 1 . Then, for each t\in \left[1, e\right] ,

    \begin{align*} \left\vert \tilde{x}(t)-x\left( t\right) \right\vert &\leq \epsilon \bigg[ \frac{1}{\lambda } \int_{1}^{t} \mathcal{J}_{1}^{\alpha} \left[ \varphi \right] \left( s\right) \mathrm{d}s + \frac{\beta }{\left\vert \Delta \right\vert }\left( 1 + \frac{N \rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}\right) \int_{1}^{\xi }\mathcal{J}_{1}^{ \alpha } \left[ \varphi \right] \left( s\right) \mathrm{d}s\\ &\quad + \left( \frac{1}{\left\vert \Delta \right\vert }+\frac{1}{\lambda }\frac{ N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} +\frac{1}{\left\vert \Delta \right\vert }\frac{N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}\right) \int_{1}^{e} \mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s\bigg]. \end{align*}

    Accordingly, to satisfy the inequality \left\vert \tilde{x}(t)-x\left(t\right) \right\vert \leq \epsilon c_{\varphi } \varphi \left(t\right) , we have to pose that \varphi is a constant function on [1, e] . Hence, if \varphi (t) = c > 0, t\in [1, e] , then any finite positive constant

    \begin{align*} c_{\varphi } &\geq \frac{c\rho_{\alpha }\left( e\right) }{\lambda \Gamma \left( 1+\alpha \right) }+\frac{c\beta \rho_{\alpha }\left( \xi \right) }{ \left\vert \Delta \right\vert \Gamma \left( 1+\alpha \right) }\left[ 1+\frac{ N\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} \right] \\ &\quad + \frac{c\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) } \left[ \frac{1}{\left\vert \Delta \right\vert }+\frac{1}{\lambda }\frac{ N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} +\frac{1}{\left\vert \Delta \right\vert }\frac{N\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}\right], \end{align*}

    will satisfy the problem. Thus, the fractional boundary value problem (1.1) is Ulam-Hyers-Rassias with respect to a constant function. The Ulam-Hyers stability can be obtained by putting \varphi = 1, and hence generalized Ulam-Hyers stable with \psi as identity function.

    In the next result, we prove the (generalized) Ulam-Hyers-Rassias stability in terms of a function.

    Theorem 5.6. Assume that the conditions of Theorem 4.1 and (5.1) hold. Then, problem (1.1) is Ulam-Hyers-Rassias stable with respect to \varphi provided that

    \begin{align} \varphi (t) &\geq \frac{\beta }{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\int_{1}^{\xi }\rho_{\alpha -1} \left( \frac{\xi }{s}\right) \varphi \left( s\right) \mathrm{d}s + \frac{1}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\int_{1}^{e}\rho_{\alpha -1}\left( \frac{e}{s} \right) \varphi \left( s\right) \mathrm{d}s \\ &\quad +\frac{1}{\lambda \Gamma \left( \alpha \right) }\int_{1}^{t}\rho_{\alpha -1}\left( \frac{t}{s}\right) \varphi \left( s\right) \mathrm{d}s, \end{align} (5.7)

    and \sup_{t\in \lbrack 1, e]}p(t) < 1, where

    \begin{align*} p(t) & = \int_{1}^{e}\left( \frac{\beta N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{\xi }{s}\right) + \frac{\beta N_{2}}{\left\vert \Delta \right\vert }+\frac{N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{e}{s}\right) +\frac{N_{2}}{\left\vert \Delta \right\vert }\right) \\ &\quad \times \exp \left( \frac{N_{1}}{\lambda \Gamma \left( \alpha \right) } \int_{\alpha }^{s}\rho_{\alpha -1}\left( \frac{s}{\tau }\right) d\tau + \frac{N_{2}}{\lambda }\left( s-1\right) \right) ds. \end{align*}

    Proof. Let us denote by x\in \mathcal{C} the unique solution of the problem (1.1). Let \tilde{x}\in \mathcal{C} be a solution of the inequality (5.1), with

    \begin{equation} \tilde{x}(1) = x(1), \ \tilde{x}\left( e\right) = x\left( e\right). \end{equation} (5.8)

    By modifying the estimate (5.5), we have

    \begin{align} \left\vert \phi_{\tilde{x}, h}\left( t\right) -\phi_{x}\left( t\right) \right\vert &\leq N_{1} \int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[\left\vert \tilde{x}-x\right\vert \right] \left( s\right) \mathrm{d}s \\ & \quad + N_{2} \int_{1}^{t} \mathcal{J}_{1}^{ \alpha} \mathcal{D}_{1}^{ \alpha }\left[ \left\vert \tilde{x}-x\right\vert \right] \left( s\right) \mathrm{d}s + \int_{1}^{t} \mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s. \end{align} (5.9)

    The above inequality implies

    \begin{align} \left\vert H_{\tilde{x}, h}\left( \xi, \beta \right) - H_{x}\left( \xi , \beta \right) \right\vert & \leq \frac{1}{\left\vert \Delta \right\vert }\left[ \beta \left\vert \phi_{\tilde{x}, h}\left( \xi \right) -\phi_{x}\left( \xi \right) \right\vert + \left\vert \phi_{\tilde{x}, h}\left( e\right) -\phi_{x}\left( e\right) \right\vert \right] \\ &\leq \frac{\beta N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{\xi } \mathcal{J}_{1}^{ \alpha }\left[ \left\vert \tilde{x} - x \right\vert \right] \left( s\right) \mathrm{d}s +\frac{\beta N_{2}}{\left\vert \Delta \right\vert} \int_{1}^{\xi }\left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad +\frac{N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J}_{1}^{ \alpha }\left[ \left\vert \tilde{x} - x\right\vert \right] \left( s \right) \mathrm{d}s +\frac{N_{2}}{\left\vert \Delta \right\vert } \int_{1}^{e}\left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad +\frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J} _{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s+\frac{1}{ \left\vert \Delta \right\vert } \int_{1}^{e} \mathcal{J}_{1}^{ \alpha }\left[ h \right] \left( s\right) \mathrm{d}s. \end{align} (5.10)

    Taking into account (2.12), (5.9) and (5.10), lead to

    \begin{align*} \left\vert \tilde{x}(t)- x\left( t\right) \right\vert & \leq \frac{1}{\lambda }\left\vert \phi_{\tilde{x}, h}\left( t\right) -\phi_{x} \left( t\right) \right\vert +\left\vert H_{\tilde{x}, h} \left( \xi, \beta \right) - H_{x}\left( \xi, \beta \right) \right\vert \left\vert g\left(t-1\right) \right\vert \\ &\leq \frac{N_{1}}{\lambda } \int_{1}^{t} \mathcal{J}_{1}^{\alpha }\left[ \left\vert \tilde{x}-x\right\vert \right] \left( s\right) \mathrm{d}s+\frac{ \beta N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J} _{1}^{\alpha }\left[ \left\vert \tilde{x}-x\right\vert \right] \left( s\right) \mathrm{d}s \\ &\quad +\frac{N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J} _{1}^{\alpha }\left[ \left\vert \tilde{x}-x\right\vert \right] \left( s\right) \mathrm{d}s +\frac{N_{2}}{\lambda }\int_{1}^{t}\left\vert \tilde{x}\left( s\right) -x \left( s\right) \right\vert \mathrm{d}s\\ &\quad + \frac{\beta N_{2}}{\left\vert \Delta \right\vert }\int_{1}^{\xi }\left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s +\frac{N_{2}}{\left\vert \Delta \right\vert }\int_{1}^{e}\left\vert \tilde{ x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad +\frac{1}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s+\frac{\beta }{\left\vert \Delta \right\vert } \int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s +\frac{1}{\left\vert \Delta \right\vert }\int_{1}^{e} \mathcal{J}_{1}^{\alpha }\left[ h\right] \left( s\right) \mathrm{d}s \\ &\leq \int_{1}^{t}\left( \frac{N_{1}}{\lambda \Gamma \left( \alpha \right) } \rho_{\alpha -1}\left( \frac{t}{s}\right) +\frac{N_{2}}{\lambda }\right) \left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad +\int\limits_{1}^{\xi }\left( \frac{\beta N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{\xi }{s}\right) +\frac{\beta N_{2}}{\left\vert \Delta \right\vert }\right) \left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad +\int\limits_{1}^{e}\left( \frac{N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{e}{s}\right) + \frac{N_{2}}{\left\vert \Delta \right\vert }\right) \left\vert \tilde{x} \left( s\right) -x\left( s\right) \right\vert \mathrm{d}s \\ &\quad + \frac{1}{\Gamma \left( \alpha \right) } \int_{1}^{e} \left( \frac{\beta }{ \left\vert \Delta \right\vert }\rho_{\alpha -1}\left( \frac{\xi }{s}\right) +\frac{1}{\left\vert \Delta \right\vert }\rho_{\alpha -1}\left( \frac{e}{s} \right) +\frac{1}{\lambda }\rho_{\alpha -1}\left( \frac{t}{s}\right) \right) h\left( s\right) \mathrm{d}s. \end{align*}

    Hence

    \begin{equation*} \left\vert \tilde{x}(t)-x\left( t\right) \right\vert \leq k+\int_{1}^{t}\tilde{a}(t, s)\left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s+\int\limits_{1}^{e}\tilde{b}(t, s)\left\vert \tilde{x}\left( s\right) -x\left( s\right) \right\vert \mathrm{d}s, \end{equation*}

    where

    \begin{align*} k & = \frac{1}{\Gamma \left( \alpha \right) }\int_{1}^{e} \bigg( \frac{\beta }{ \left\vert \Delta \right\vert }\rho_{\alpha -1}\left( \frac{\xi }{s}\right) + \frac{1}{\left\vert \Delta \right\vert }\rho_{\alpha -1} \left( \frac{e}{s} \right) +\frac{1}{\lambda }\rho_{\alpha -1}\left( \frac{e}{s}\right) \bigg) h\left( s\right)\, \mathrm{d}s, \end{align*}

    and

    \begin{align*} \tilde{a}(t, s) & = \left( \frac{N_{1}}{\lambda \Gamma \left( \alpha \right) }\rho _{\alpha -1}\left( \frac{t}{s}\right) +\frac{N_{2}}{\lambda }\right), \\ \tilde{b}(t, s) & = \frac{\beta N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{\xi }{s}\right) +\frac{\beta N_{2}}{\left\vert \Delta \right\vert }+\frac{N_{1}}{\left\vert \Delta \right\vert \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{e}{s} \right) +\frac{N_{2}}{\left\vert \Delta \right\vert }. \end{align*}

    In virtue of Lemma 2.8, we deduce that

    \begin{equation*} \left\vert \tilde{x}(t)-x\left( t\right) \right\vert \leq \frac{k}{1-p(t)} \exp \left( \int_{1}^{t}a(t, s)ds\right), \end{equation*}

    for t\in \lbrack 1, e] . Problem (1.1) is Ulam-Hyers-Rassias stable with respect to \varphi \geq \frac{1}{\epsilon }\left\vert h\right\vert , \varphi must satisfy the inequality (5.7). In this case, we get \left\vert \tilde{x}(t)-x\left(t\right) \right\vert \leq c_{\varphi }\epsilon \varphi (t) , where

    \begin{equation*} c_{\varphi } = \max\limits_{t\in \lbrack 1, e]} \left[ \frac{1}{1-p(t)} \exp \left( \int_{1}^{t}\left( \frac{N_{1}}{\lambda \Gamma \left( \alpha \right) }\rho_{\alpha -1}\left( \frac{t}{s}\right) +\frac{N_{2}}{\lambda }\right) ds\right)\right]. \end{equation*}

    This completes the proof.

    Theorem 5.7. Assume the conditions of Theorem 4.1 and (5.2) hold. Then, problem (1.1) is generalized Ulam-Hyers-Rassias stable with respect to \varphi provided for any t \in \left[1, e\right] that

    \begin{align} \varphi (t) &\geq \frac{1}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s + \frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s +\frac{1}{\left\vert \Delta \right\vert } \int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s \\ & \quad +\frac{1}{\Gamma \left( 1+\alpha \right) -NM_{\rho }} \left[ N\left\Vert \varphi \right\Vert \rho \left( t\right) \left( \frac{\rho_{\alpha }\left( e\right) }{ \lambda }+\frac{\beta \rho_{\alpha }\left( \xi \right) }{\left\vert \Delta \right\vert } + \frac{\rho_{\alpha }\left( e\right) }{\left\vert \Delta \right\vert }\right) \right] \\ &\quad + \frac{1}{\left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) } \bigg[ \Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) \\ & \quad - N \left( M_{\sigma }+\lambda \frac{\left( \beta \rho _{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{ \left\vert \Delta \right\vert }e^{2-\alpha }\right) \bigg]^{-1}\\ & \quad \times N^{2} \left\Vert \varphi \right\Vert \left( \frac{\rho_{\alpha }\left( e\right) }{\lambda }+\frac{\beta \rho_{\alpha }\left( \xi \right) }{ \left\vert \Delta \right\vert }+\frac{\rho_{\alpha }\left( e\right) }{ \left\vert \Delta \right\vert }\right) \left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left(e\right) \right) }{\left\vert \Delta \right\vert }e^{2-\alpha }\right) \rho \left( t\right) \\ & \quad + N \rho \left( t\right) \bigg[ \Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) - N \left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) + \rho_{\alpha } \left( e\right) \right) }{\left\vert \Delta \right\vert } e^{2-\alpha }\right) \bigg]^{-1} \\ &\quad \times \bigg[ \int_{1}^{t}s\left( \ln s\right)^{1-\alpha }\mathcal{J} _{1}^{\alpha }\left[ \varphi \right] \left( t-s+1\right) \mathrm{d}s \\ & \quad + \frac{ \lambda \beta e^{2-\alpha }}{\left\vert \Delta \right\vert }\int_{1}^{\xi } \mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s +\frac{\lambda e^{2-\alpha }}{\left\vert \Delta \right\vert }\int_{1}^{e} \mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right)\, \mathrm{d} s \bigg]. \end{align} (5.11)

    Proof. Let us denote by x\in C\left(\left[1, e\right], \mathbb{R} \right) the unique solution of the problem (1.1). Let \bar{x}\in \mathcal{C} be a solution of the inequality (5.2), with (5.8). It follows

    \begin{align*} \left\vert \phi_{\bar{x}}\left( t\right) -\phi_{x}\left( t\right) \right\vert & = \left\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J} _{1}^{\alpha }\left[ f_{\bar{x}}-f_{x}\right] \left( s\right) \mathrm{d}s\right\vert \\ &\leq N_{1} \int_{1}^{t} \mathcal{J}_{1}^{\alpha }\left[ \left\vert \bar{x} - x \right\vert \right] \left( s\right) \mathrm{d}s + N_{2} \int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ \left\vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x} \right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\vert \right] \left( s\right) \mathrm{d}s \\ &\leq N_{1} \int_{1}^{t} \mathcal{J}_{1}^{\alpha }\left[ \left\vert \bar{x} - x \right\vert \right] \left( s\right) \mathrm{d}s + \frac{N_{2}\rho_{\alpha }\left( t\right) }{\Gamma \left( 1 +\alpha \right) }\left\Vert \mathcal{D} _{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert. \end{align*}

    On the other hand, we have, for each t\in \left[1, e\right] ,

    \begin{align*} \left\vert H_{\tilde{x}}\left( \xi, \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert & \leq \frac{1}{\left\vert \Delta \right\vert }\left[ \beta \left\vert \phi_{\tilde{x}}\left( \xi \right) -\phi_{x}\left( \xi \right) \right\vert +\left\vert \phi_{\tilde{x}} \left( e\right) -\phi_{x}\left( e \right) \right\vert \right] \\ &\leq \frac{N_{2}\left( \rho_{\alpha }\left( e\right) +\beta \rho_{\alpha }\left( \xi \right) \right) }{\left\vert \Delta \right\vert \Gamma \left( 1+\alpha \right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] - \mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\quad +\frac{\beta N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{ J}_{1}^{\alpha }\left[ \left\vert \bar{x}-x\right\vert \right] \left( s\right) \mathrm{d}s + \frac{N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{e} \mathcal{J}_{1}^{\alpha }\left[ \left\vert \bar{x}-x\right\vert \right] \left( s\right) \mathrm{d}s. \end{align*}

    Hence by Lemma 5.4, for each t\in \left[1, e\right] , we get

    \begin{align*} \left\vert \bar{x}(t)-x\left( t\right) \right\vert &\leq \left\vert \bar{x}(t)-\frac{1}{\lambda }\phi_{\bar{x}}\left( t\right) - H_{\bar{x}}\left( \xi, \beta \right) g\left( t-1\right) \right\vert +\frac{1}{\lambda }\left\vert \phi_{\bar{x}}\left( t\right) -\phi_{x}\left( t\right) \right\vert \\ &\quad +\left\vert H_{\bar{x}}\left( \xi, \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert \left\vert g\left( t-1\right) \right\vert \\ &\leq \omega (t)+\frac{N_{1}}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha } \left[ \left\vert \bar{x}-x\right\vert \right] \left( s\right) \mathrm{d}s+ \frac{\beta N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J} _{1}^{\alpha }\left[ \left\vert \bar{x}-x\right\vert \right] \left( s\right) \mathrm{d}s \\ &\quad +\frac{N_{1}}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J} _{1}^{\alpha }\left[ \left\vert \bar{x}-x\right\vert \right] \left( s\right) \mathrm{d}s +\frac{N_{2}\rho_{\alpha }\left( t\right) }{\lambda \Gamma \left( 1+\alpha \right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] - \mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\quad +\frac{N_{2}\left( \rho_{\alpha }\left( e\right) +\beta \rho_{\alpha }\left( \xi \right) \right) }{\left\vert \Delta \right\vert \Gamma \left( 1+\alpha \right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] - \mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\leq \omega (t)+\frac{N_{1}\rho \left( t\right) }{\Gamma \left( 1+\alpha \right) }\left\Vert \bar{x}-x\right\Vert +\frac{N_{2}\rho \left( t\right) }{ \Gamma \left( 1+\alpha \right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\leq \Omega +\frac{N_{1}M_{\rho }}{\Gamma \left( 1+\alpha \right) } \left\Vert \bar{x}-x\right\Vert +\frac{N_{2}M_{\rho }}{\Gamma \left( 1+\alpha \right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] - \mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert, \end{align*}

    where

    \Omega = \frac{\left\Vert \varphi \right\Vert \rho_{\alpha }\left( e\right) }{\lambda }+\frac{\beta \left\Vert \varphi \right\Vert \rho _{\alpha }\left( \xi \right) }{\left\vert \Delta \right\vert }+\frac{ \left\Vert \varphi \right\Vert \rho_{\alpha }\left( e\right) }{\left\vert \Delta \right\vert }.

    Then (see (3.17))

    \begin{equation*} \left\Vert \bar{x}-x\right\Vert \leq \frac{\Omega \Gamma \left( 1+\alpha \right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}+\frac{NM_{\rho }}{ \Gamma \left( 1+\alpha \right) -NM_{\rho }}\left\Vert \mathcal{D} _{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert. \end{equation*}

    Accordingly, we get

    \begin{align*} \left\vert \bar{x}(t)-x\left( t\right) \right\vert &\leq \omega (t)+\frac{\Omega N\rho \left( t\right) }{\Gamma \left(1+\alpha \right) -NM_{\rho }} + \frac{N\rho \left( t\right) }{\Gamma \left(1 + \alpha \right)- NM_{\rho }} \left\Vert \mathcal{D}_{1}^{ \alpha }\left[ \bar{x} \right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert. \end{align*}

    We are going now to get an estimate for \left\Vert \mathcal{D}_{1}^{\alpha } \left[\bar{x}\right] -\mathcal{D}_{1}^{\alpha }\left[x\right] \right\Vert. It is obvious that

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] (t)-\mathcal{D} _{1}^{\alpha }\left[ x\right] (t)\right\vert &\leq \frac{1}{\lambda } \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{\bar{x}, h}\right] \left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left( t\right) \right\vert \\ &\quad +\left\vert H_{\bar{x}, h}\left( \xi, \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert, \end{align*}

    where

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{\bar{x}, h}\right] \left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left( t\right)\right\vert& = \left\vert \int_{1}^{t}\mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{\bar{x}}-f_{x}+h \right] \left( t-s+1\right) \mathrm{d}s\right\vert \\ &\leq N_{1}\int_{1}^{t}\left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \mathcal{J}_{1}^{\alpha } \left[ \left\vert \bar{x}-x\right\vert \right] \left( t-s+1\right) \mathrm{d} s \\ &\quad +N_{2}\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D }_{1}^{\alpha }\left[ x\right] \right\Vert \int_{1}^{t}\left\vert \mathcal{D} _{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \mathcal{J}_{1}^{\alpha }\left[ 1\right] \left( t-s+1\right) \mathrm{d}s \\ &\quad +\int_{1}^{t}\left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( t-s+1\right) \mathrm{d}s \\ &\leq \frac{\lambda N\Omega \sigma_{\alpha }\left( t\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) } \\ &\quad +\frac{\lambda N\sigma_{\alpha }\left( t\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha } \left[ x\right] \right\Vert \\ &\quad +\frac{\lambda }{\Gamma \left( 2-\alpha \right) }\int_{1}^{t}s\left( \ln s\right)^{1-\alpha }\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( t-s+1\right) \mathrm{d}s. \end{align*}

    Also, we get

    \begin{align*} \left\vert H_{\tilde{x}, h}\left( \xi, \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert &\leq \frac{\beta }{\left\vert \Delta \right\vert }\left( \left\vert \phi_{ \tilde{x}}\left( \xi \right) -\phi_{x}\left( \xi \right) \right\vert +\int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s\right) \\ &\quad +\frac{1}{\left\vert \Delta \right\vert }\left( \left\vert \phi_{\tilde{x} }\left( e\right) -\phi_{x}\left( e\right) \right\vert +\int_{1}^{e}\mathcal{ J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s\right) \\ &\leq \frac{\Omega N\left( \beta \rho_{\alpha }\left( \xi \right) +\rho _{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) } +\frac{N\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }\left\Vert \mathcal{D} _{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\quad +\frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J} _{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s+\frac{1}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J} _{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{ d}s. \end{align*}

    Hence, we deduce that

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] (t)-\mathcal{D} _{1}^{\alpha }\left[ x\right] (t)\right\vert &\leq \frac{1}{\lambda }\left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{ \bar{x}, h}\right] \left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x} \right] \left( t\right) \right\vert\\ &\quad +\left\vert H_{\bar{x}, h}\left( \xi , \beta \right) -H_{x}\left( \xi, \beta \right) \right\vert \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \\ &\leq \frac{N\Omega \left( \sigma_{\alpha }\left( t\right) +\lambda t\left( \ln t\right)^{1-\alpha }\frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1 +\alpha \right) -NM_{\rho }\right) } \\ &\quad +\frac{N\left( \sigma_{\alpha }\left( t\right) +\lambda t\left( \ln t\right)^{1-\alpha }\frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert } \right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x }\right] -\mathcal{D}_{1}^{\alpha }\left[ x\right] \right\Vert \\ &\quad +\frac{1}{\Gamma \left( 2-\alpha \right) }\int_{1}^{t}s\left( \ln s\right) ^{1-\alpha }\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( t-s+1\right) \mathrm{d}s \\ &\quad +\frac{\lambda t}{\Gamma \left( 2-\alpha \right) }\left( \ln t\right) ^{1-\alpha }\frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi } \mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s \\ &\quad +\frac{\lambda t}{\Gamma \left( 2-\alpha \right) }\left( \ln t\right) ^{1-\alpha }\frac{1}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J} _{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{ d}s \\ &\leq \frac{N\Omega \left( M_{\sigma }+\lambda \frac{\left( \beta \rho _{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{ \left\vert \Delta \right\vert }e^{2-\alpha }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) } \\ &\quad +\frac{N\left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert }e^{2-\alpha }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }\left\Vert \mathcal{D}_{1}^{\alpha }\left[ \bar{x}\right] -\mathcal{D}_{1}^{\alpha } \left[ x\right] \right\Vert \\ &\quad +\frac{1}{\Gamma \left( 2-\alpha \right) }\int_{1}^{t}s\left( \ln s\right) ^{1-\alpha }\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( t-s+1\right) \mathrm{d}s \\ &\quad +\frac{\lambda \beta t}{\left\vert \Delta \right\vert \Gamma \left( 2-\alpha \right) }\left( \ln t\right)^{1-\alpha }\int_{1}^{\xi }\mathcal{J} _{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{ d}s \\ &\quad +\frac{\lambda t}{\left\vert \Delta \right\vert \Gamma \left( 2-\alpha \right) }\left( \ln t\right)^{1-\alpha }\int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s. \end{align*}

    Then

    \begin{align*} \left\Vert \mathcal{D}_{1}^{ \alpha }\left[ \bar{x}\right] - \mathcal{D} _{1}^{\alpha }\left[ x\right] \right\Vert &\leq \frac{N\Omega \left( M_{\sigma }+\lambda \frac{\left( \beta \rho _{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{ \left\vert \Delta \right\vert }e^{2-\alpha }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) -N\left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert } e^{2-\alpha }\right) } \\ &\quad +\frac{\left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) -N\left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert }e^{2-\alpha }\right) } \\ &\quad \times \left( \int_{1}^{e}s\left( \ln s\right)^{1-\alpha }\mathcal{J} _{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( e-s+1\right) \mathrm{d}s\right. \\ &\quad \left. +\frac{\lambda \beta e^{2-\alpha }}{\left\vert \Delta \right\vert } \int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s+\frac{\lambda e^{2-\alpha }}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \left\vert h\right\vert \right] \left( s\right) \mathrm{d}s\right). \end{align*}

    If \left\vert h\right\vert \leq \varphi , we get

    \begin{align*} \left\vert \bar{x}(t)-x\left( t\right) \right\vert &\leq \frac{1}{\lambda }\int_{1}^{t}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s+\frac{\beta }{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s+\frac{1}{\left\vert \Delta \right\vert } \int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s \\ &\quad +\frac{\Omega N\rho \left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}+\frac{N\rho \left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} \\ &\quad \times \left[ \frac{N\Omega \left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{ \left\vert \Delta \right\vert }e^{2-\alpha }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) -N\left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert } e^{2-\alpha }\right) }\right. \\ &\quad \left. + \frac{\left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) }{\Gamma \left( 2-\alpha \right) \left( \Gamma \left( 1+\alpha \right) -NM_{\rho }\right) -N\left( M_{\sigma }+\lambda \frac{\left( \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right) }{\left\vert \Delta \right\vert }e^{2-\alpha }\right) } \right] \\ &\quad \times \left[ \int_{1}^{t}s\left( \ln s\right)^{1-\alpha }\mathcal{J} _{1}^{\alpha }\left[ \varphi \right] \left( t-s+1\right) \mathrm{d}s \right. \\ &\quad \left. +\frac{\lambda \beta e^{2-\alpha }}{\left\vert \Delta \right\vert }\int_{1}^{\xi }\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s+\frac{\lambda e^{2-\alpha }}{\left\vert \Delta \right\vert }\int_{1}^{e}\mathcal{J}_{1}^{\alpha }\left[ \varphi \right] \left( s\right) \mathrm{d}s\right]. \end{align*}

    Hence by the given condition (5.11), the equation (1.1) is generalized Ulam-Hyers-Rassias stable with respect to \varphi .

    For f Lipschitz in the second and the third variables, the solution's dependence on the order of the differential operator, the boundary values and the nonlinear term f are discussed in this section. We show that the solutions of two equations with neighbouring orders will (under suitable conditions on their right hand sides f ) lie close to one another.

    Theorem 6.1. Suppose that the conditions of Theorem 4.1 hold. Let x\left(t\right) , x_{\epsilon }\left(t\right) be the solutions, respectively, of problems (1.1) and

    \begin{equation} \mathcal{D}_{1}^{\alpha -\epsilon }\left( \mathrm{D}^{2}+\lambda^{2}\right) x(t) = f \left(t, x(t), \mathcal{D}_{1}^{\alpha }\left[ x\right] (t) \right), \end{equation} (6.1)

    for t\in \left(0, 1\right) and \epsilon > 0 , with the boundary conditions (1.1)-b, where 0 < \alpha -\epsilon < \alpha < 1 . Then there exists a constant k_{\epsilon } > 0 such that

    \begin{equation} \left\Vert x-x_{\epsilon }\right\Vert_{E}\leq k_{\epsilon } \left\| f\right\|_*, \end{equation} (6.2)

    where \left\| f\right\|_* = \sup_{ \epsilon } \left\Vert f_{x_{\epsilon }}\right\Vert and f_{x_{\epsilon }} (t): = f \left(t, x_{\epsilon }(t), \mathcal{D}_{1}^{ \alpha }\left[x_{ \epsilon }\right] (t) \right) .

    Proof. By Lemma 3.4 and equation (3.11), we can obtain

    x_{\epsilon }\left( t\right) = \frac{1}{ \lambda }\phi_{x_{\epsilon }} \left(t\right) + H_{x_{\epsilon }}\left( \xi, \beta \right) g\left( t-1\right),

    is the solution of (6.1) with the boundary conditions in (1.1), where

    \phi_{x_{\epsilon }}\left( t\right) = \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha -\epsilon }\left[ f_{ x_{\epsilon }}\right] \left(s\right) \mathrm{d}s

    and H_{x_{\epsilon }}\left(\xi, \beta \right) = \frac{1}{ \Delta }\left(\beta \phi_{x_{\epsilon }} \left(\xi \right) +\phi_{x_{\epsilon }} (e) \right) . Then

    \begin{align*} \left\vert \phi_{x_{\epsilon }}\left( t\right) -\phi_{x}\left( t\right) \right\vert & = \bigg\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha -\epsilon }\left[ f_{x_{\epsilon }}\right] \left( s\right) \mathrm{d}s -\int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x}\right] \left( s\right) \mathrm{d}s \bigg\vert \\ &\leq \left\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha } \left[ f_{x_{\epsilon }}-f_{x}\right] \left( s\right) \mathrm{d}s\right\vert\\ &\quad +\left\vert \int_{1}^{t}g\left( t-s\right) \left[ \mathcal{J}_{1}^{\alpha -\epsilon }\left[ f_{x_{\epsilon }}\right] \left( s\right) -\mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}\right] \left( s\right) \right] \mathrm{d}s\right\vert \\ &\leq \left\Vert f_{x_{\epsilon }}-f_{x} \right\Vert \int_{1}^{t}\left\vert g\left( t-s\right) \right\vert \mathcal{J}_{1}^{ \alpha }\left[ 1\right] \left( s\right) \mathrm{d}s \\ &\quad +\left\Vert f_{x_{\epsilon }} \right\Vert \int_{1}^{t}\left\vert g\left( t-s\right) \right\vert \left\vert \mathcal{J}_{1}^{\alpha -\epsilon }\left[ 1 \right] \left( s\right) -\mathcal{J}_{1}^{ \alpha }\left[ 1\right] \left( s\right) \right\vert \mathrm{d}s \\ &\leq \frac{N\left\Vert x-x_{\epsilon }\right\Vert_{E}}{\Gamma \left( 1+\alpha \right) }\int_{1}^{t}\left( \ln s\right)^{\alpha }\mathrm{d}s+\left\Vert f_{x_{\epsilon }}\right\Vert \int_{1}^{t}\left\vert \frac{ \left( \ln s\right)^{\alpha -\epsilon }}{\Gamma \left( 1+\alpha -\epsilon \right) }-\frac{\left( \ln s\right)^{\alpha }}{\Gamma \left( 1+\alpha \right) }\right\vert \mathrm{d}s. \end{align*}

    This leads to

    \left\vert \phi_{x_{\epsilon }}\left( t\right) -\phi_{x}\left( t\right) \right\vert \leq \frac{N}{\Gamma \left( 1+\alpha \right) }\rho_{\alpha }\left( t\right) \left\Vert x-x_{\epsilon }\right\Vert_{E}+\varrho _{\epsilon }\left( t\right) \left\Vert f_{x_{\epsilon }}\right\Vert,

    with

    \varrho_{\epsilon }\left( t\right) = \int_{1}^{t}\left\vert \frac{\left( \ln s\right)^{\alpha -\epsilon }}{\Gamma \left( 1+\alpha -\epsilon \right) }- \frac{\left( \ln s\right)^{\alpha }}{\Gamma \left( 1+\alpha \right) } \right\vert \mathrm{d}s.

    In a similar manner, we can get

    \begin{align*} \left\vert H_{x_{\epsilon }}\left( \xi, \beta \right) -H_{x}\left( \xi , \beta \right) \right\vert &\leq \frac{N}{\Gamma \left( 1+\alpha \right) } \frac{1}{\left\vert \Delta \right\vert }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\Vert x-x_{\epsilon }\right\Vert_{E} \\ &\quad +\frac{1}{\left\vert \Delta \right\vert }\left[ \beta \varrho_{\epsilon }\left( \xi \right) +\varrho_{\epsilon }\left( e\right) \right] \left\Vert f_{x_{\epsilon }}\right\Vert. \end{align*}

    Then

    \begin{equation} \left\vert x\left( t\right) -x_{\epsilon }\left( t\right) \right\vert \leq \frac{N}{\Gamma \left( 1+\alpha \right) }\rho \left( t\right) \left\Vert x-x_{\epsilon }\right\Vert_{E}+\varrho \left( t\right) \left\Vert f_{x_{\epsilon }}\right\Vert, \end{equation} (6.3)

    with \varrho \left(t\right) = \frac{1}{\lambda }\varrho_{\epsilon }\left(t\right) +\frac{1}{\left\vert \Delta \right\vert }\left[\beta \varrho_{\epsilon }\left(\xi \right) + \varrho_{\epsilon }\left(e\right) \right] . On the other hand,

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t)-\mathcal{ D}_{1}^{\alpha }\left[ x\right] (t)\right\vert &\leq \frac{1}{\lambda } \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right] \left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left( t\right) \right\vert \\ &\quad +\left\vert H_{x_{\epsilon }}\left( \xi, \beta \right) -H_{x}\left( \xi , \beta \right) \right\vert \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert. \end{align*}

    By (4.8), we have

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right] \left( t\right) - \mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left(t\right) \right\vert & = \left\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \left[ \mathcal{J}_{1}^{\alpha -\epsilon }\left[ f_{x_{\epsilon }}\right] - \mathcal{J}_{1}^{ \alpha }\left[ f_{x}\right] \right] \left( t-s+1\right) \mathrm{d}s\right\vert \\ & = \bigg\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \\ & \quad \times \left[\mathcal{J}_{1}^{\alpha -\epsilon }\left[f_{x_{\epsilon }}\right] -\mathcal{J}_{1}^{\alpha }\left[ f_{x} \right] + \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}\right] -\mathcal{J} _{1}^{\alpha }\left[ f_{x_{\epsilon }}\right] \right] \left( t-s+1\right) \mathrm{d}s\bigg\vert.\\ \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right] ( t) - \mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] (t) \right\vert \notag &\leq \left\vert \int_{1}^{t} \mathcal{D}_{1}^{ \alpha }\left[ g\left(t-1\right) \right] \left( s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}-f_{x}\right] \left( t-s+1\right) \mathrm{d}s\right\vert\\ &\quad +\left\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \left( \mathcal{J}_{1}^{\alpha -\epsilon }\left[ f_{x_{\epsilon }}\right] - \mathcal{J}_{1}^{ \alpha }\left[ f_{x_{\epsilon }} \right] \right) \left( t-s+1\right) \mathrm{d}s\right\vert \\ &\leq N\left\Vert x-x_{\epsilon }\right\Vert_{E}\int_{1}^{t}\left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \mathcal{J}_{1}^{\alpha }\left[ 1\right] \left( t-s+1\right) \mathrm{d}s \\ &\quad +\left\Vert f_{x_{\epsilon }}\right\Vert \int_{1}^{t}\left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \left\vert \left( \mathcal{J}_{1}^{\alpha -\epsilon }\left[ 1\right] - \mathcal{J}_{1}^{\alpha }\left[ 1\right] \right) \left( t-s+1\right) \right\vert \mathrm{d}s. \end{align*}

    Then

    \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right]\left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x} \right] \left(t\right) \right\vert \leq N\sigma_{\alpha }\left( t\right) \left\Vert x-x_{\epsilon } \right\Vert_{E} + \upsilon_{\epsilon }\left( t\right) \left\Vert f_{x_{\epsilon }}\right\Vert,

    with

    \begin{equation} \upsilon_{\epsilon }\left( t\right) = \int_{1}^{t}\left\vert \mathcal{D} _{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \left\vert \mathcal{J}_{1}^{\alpha -\epsilon }\left[ 1\right] -\mathcal{J} _{1}^{\alpha }\left[ 1\right] \left( t-s+1\right) \right\vert \mathrm{d}s. \end{equation} (6.4)

    Then the expression above becomes

    \begin{align} \label{Eq152} \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t)- \right. & \left. \mathcal{D}_{1}^{\alpha }\left[ x\right] (t) \right\vert \leq \frac{1}{\lambda }N\sigma_{\alpha }\left( t\right) \left\Vert x-x_{\epsilon }\right\Vert_{E}+\upsilon_{\epsilon }\left( t\right) \left\Vert f_{x_{\epsilon }}\right\Vert \\ &\quad +\frac{N}{\Gamma \left( 1+\alpha \right) }\frac{1}{\left\vert \Delta \right\vert }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\Vert x-x_{\epsilon }\right\Vert_{E} \\ &\quad +\frac{1}{\left\vert \Delta \right\vert }\left[ \beta \varrho_{\epsilon }\left( \xi \right) +\varrho_{\epsilon }\left( e\right) \right] \left\Vert f_{x_{\epsilon }}\right\Vert \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \\ &\leq N\left[ \frac{1}{\lambda }\sigma_{\alpha }\left( t\right) +\frac{1}{ \Gamma \left( 1+\alpha \right) }\frac{1}{\left\vert \Delta \right\vert } \left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \right] \left\Vert x-x_{\epsilon }\right\Vert _{E} \\ &\quad +\left[ \frac{1}{\lambda }\upsilon_{\epsilon }\left( t\right) +\frac{1}{ \left\vert \Delta \right\vert }\left[ \beta \varrho_{\epsilon }\left( \xi \right) +\varrho_{\epsilon }\left( e\right) \right] \left\vert \mathcal{D} _{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \right] \left\Vert f_{x_{\epsilon }}\right\Vert. \end{align}

    Then

    \begin{equation} \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t)-\mathcal{ D}_{1}^{\alpha }\left[ x\right] (t)\right\vert \leq NC_{22}\left( t\right) \left\Vert x-x_{\epsilon }\right\Vert_{E}+C_{33}\left( t\right) \left\Vert f_{x_{\epsilon }}\right\Vert, \end{equation} (6.5)

    with

    \begin{equation} \begin{split} C_{22}\left( t\right) & = \left[ \frac{1}{\lambda }\sigma_{\alpha }\left( t\right) +\frac{1}{\Gamma \left( 1+\alpha \right) }\frac{1}{\left\vert \Delta \right\vert }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho _{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \right], \\ C_{33}\left( t\right) & = \left[ \frac{1}{\lambda }\upsilon_{\epsilon }\left( t\right) +\frac{1}{\left\vert \Delta \right\vert }\left[ \beta \varrho_{\epsilon }\left( \xi \right) +\varrho_{\epsilon }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert \right]. \end{split} \end{equation} (6.6)

    Moreover, from (6.3), (6.5), we deduce that

    \begin{align*} \left\vert x\left( t\right) -x_{\epsilon }\left( t\right) \right\vert &+ \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t)- \mathcal{D}_{1}^{\alpha }\left[ x\right] (t)\right\vert \\ &\leq N\left[ \frac{1}{\Gamma \left( 1+\alpha \right) }\rho \left( t\right) +C_{22}\left( t\right) \right] \left\Vert x-x_{\epsilon }\right\Vert_{E}+ \left[ \varrho \left( t\right) +C_{33}\left( t\right) \right] \left\Vert f_{x_{\epsilon }}\right\Vert. \end{align*}

    Finally, we get the inequality

    \left\Vert x-x_{\epsilon }\right\Vert_{E}\leq \frac{\sup_{t\in \lbrack 1, e]}\left[ \varrho \left( t\right) +C_{33}\left( t\right) \right] }{ 1-N\sup_{t\in \lbrack 1, e]}\left[ \frac{1}{\Gamma \left( 1+\alpha \right) } \rho \left( t\right) +C_{22}\left( t\right) \right] }\left\| f\right\|_*,

    which is exactly the required inequality (6.2), where

    \begin{equation} k_{\epsilon } = \frac{\sup_{t\in \lbrack 1, e]}\left[ \varrho \left( t\right) +C_{33}\left( t\right) \right] }{1-N\sup_{t\in \lbrack 1, e]}\left[ \frac{1}{ \Gamma \left( 1+\alpha \right) }\rho \left( t\right) +C_{22}\left( t\right) \right] }. \end{equation} (6.7)

    Theorem 6.2. Suppose that the conditions of Theorem 4.1 hold. Let x\left(t\right), x_{\epsilon }\left(t\right) be the solutions, respectively, of the problems (1.1) and

    \mathcal{D}_{1}^{\alpha }\left( \mathrm{D}^{2}+\lambda^{2}\right) x(t) = f(t, x(t), \mathcal{D}_{1}^{\alpha }\left[ x\right] (t)+\epsilon h_{\epsilon }\left( t\right),

    for t\in \left(1, e\right) and h_{\epsilon }\in \mathcal{C} , with boundary conditions (1.1)-b, where \epsilon < 0. Then \left\Vert x-x_{\epsilon }\right\Vert_{E} = O\left(\epsilon \right) .

    Proof. In accordance with Lemma 3.4, we have

    \phi_{x_{\epsilon }}\left( t\right) = \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}+\epsilon h_{\epsilon } \right] \left( s\right) \mathrm{d}s

    and

    \begin{align} \left\vert \phi_{x_{\epsilon }}\left( t\right) -\phi_{x}\left( t\right) \right\vert & = \left\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}+\epsilon h_{\epsilon } \right] \left( s\right) \mathrm{d}s -\int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x}\right] \left( s\right) \mathrm{d}s\right\vert \\ &\leq \left\vert \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{\alpha } \left[ f_{x_{\epsilon }}-f_{x}\right] \left( s\right) \mathrm{d}s\right\vert +\epsilon \left\vert \int_{1}^{t}g \left( t-s\right) \mathcal{J}_{1}^{\alpha } \left[ h_{\epsilon }\right] \left( s\right) \mathrm{d}s\right\vert \\ &\leq \frac{1}{\Gamma \left( 1+\alpha \right) }\left( \left\Vert f_{x_{\epsilon }}-f_{x}\right\Vert +\epsilon \left\Vert h_{\epsilon }\right\Vert \right) \int_{1}^{t}\left( \ln s\right)^{\alpha }\mathrm{d}s \\ &\leq \frac{N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert }{\Gamma \left( 1+\alpha \right) }\rho_{\alpha }\left( t\right). \end{align} (6.8)

    and

    \begin{align} \left\vert H_{x}\left( \xi, \beta \right) -H_{x_{\epsilon }}\left( \xi , \beta \right) \right\vert &\leq \frac{1}{\left\vert \Delta \right\vert }\left[ \beta \left\vert \phi _{x_{\epsilon }}\left( \xi \right) -\phi_{x}\left( \xi \right) \right\vert +\left\vert \phi_{x_{\epsilon }}\left( e\right) -\phi_{x}\left( e\right) \right\vert \right] \\ &\leq \frac{1}{\left\vert \Delta \right\vert }\frac{N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert }{\Gamma \left( 1+\alpha \right) }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right]. \end{align} (6.9)

    From (6.8) and (6.9), we derive

    \left\vert x\left( t\right) -x_{\epsilon }\left( t\right) \right\vert \leq \frac{\rho \left( t\right) }{\Gamma \left( 1+\alpha \right) }\left( N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert \right).

    On the other hand,

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right] \left( t\right) \right. & \left. -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left( t\right) \right\vert \\ & = \left\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \left( \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}+\epsilon h_{\epsilon }\right] -\mathcal{J}_{1}^{\alpha } \left[ f_{x}\right] \right) \left(t - s + 1\right) \mathrm{d}s\right\vert\\ &\leq \left\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{ \epsilon }}-f_{x}\right] \left( t-s+1\right) \mathrm{d}s\right\vert\\ &\quad +\epsilon \left\vert \int_{1}^{t} \mathcal{D}_{1}^{\alpha }g\left( t-1\right) \left( s\right) \mathcal{J}_{1}^{ \alpha }\left[ h_{\epsilon } \right] \left( t-s+1\right) \mathrm{d}s \right\vert \\ &\leq \left( N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert \right) \int_{1}^{t}\left\vert \mathcal{D }_{1}^{\alpha }\left[ g\left( t-1\right) \right] \left( s\right) \right\vert \mathcal{J}_{1}^{\alpha }\left[ 1\right] \left( t-s+1\right) \mathrm{d}s \end{align*}

    and

    \left\vert \mathcal{D}_{1}^{\alpha }\left[ \phi_{x_{\epsilon }}\right] \left( t\right) -\mathcal{D}_{1}^{\alpha }\left[ \phi_{x}\right] \left( t\right) \right\vert \leq \frac{R_{11}(t)}{\Gamma \left( 1+\alpha \right) }\left( N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert \right),

    where R_{11} is given by (4.9). Hence, we obtain

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t) \right. &\left. - \mathcal{D}_{1}^{ \alpha }\left[ x\right] (t)\right\vert \leq \frac{R_{11}(t)}{\lambda \Gamma \left( 1+\alpha \right) }\left( N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert \right) \\ &\quad +\frac{1}{\left\vert \Delta \right\vert }\frac{N\left\Vert x-x_{\epsilon }\right\Vert_{E}+\epsilon \left\Vert h_{\epsilon }\right\Vert }{\Gamma \left( 1+\alpha \right) }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha } \left[ g\left( t-1\right) \right] \right\vert, \end{align*}

    and

    \begin{align*} \left\vert x\left( t\right) - x_{\epsilon }\left( t\right) \right\vert & +\left\vert \mathcal{D}_{1}^{ \alpha }\left[ x_{\epsilon }\right] (t)- \mathcal{D}_{1}^{\alpha }\left[ x\right] (t)\right\vert \\ &\leq \frac{N}{\Gamma \left( 1+\alpha \right) }\left( \rho \left( t\right) + \frac{\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left(e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left(t-1\right) \right] \right\vert }{ \left\vert \Delta \right\vert }+\frac{R_{11}(t)}{\lambda }\right) \left\Vert x-x_{\epsilon }\right\Vert_{E} \\ &\quad +\frac{\epsilon \left\Vert h_{\epsilon }\right\Vert }{\Gamma \left( 1+\alpha \right) }\left( \rho \left( t\right) +\frac{\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{ \alpha }\left[ g\left( t-1\right) \right] \right\vert }{\left\vert \Delta \right\vert } + \frac{R_{11}(t)}{ \lambda }\right). \end{align*}

    Consequently

    \left\Vert x-x_{\epsilon }\right\Vert_{E}\leq \epsilon \frac{NQ}{1-NQ} \left\| h\right\|_*,

    where Q is given by (3.18) and \left\| h\right\|_* = \sup_{0 < \epsilon }\left\Vert h_{\epsilon }\right\Vert . It is obvious that \left\Vert x-x_{\epsilon }\right\Vert_{E} = O\left(\epsilon \right) .

    Let us introduce small perturbation in the boundary conditions of (1.1) such that

    \begin{equation} x(1) = 0 = \mathrm{D}^{2}x\left( 1\right), \qquad x(e) = \beta x(\xi )+\epsilon, \end{equation} (6.10)

    for \xi \in (1, e] .

    Theorem 6.3. Assume the conditions of Theorem 4.1 hold. Let x\left(t\right) , x_{\epsilon }\left(t\right) be respective solutions, of the problems (1.1) and the boundary conditions (1.1)-a with (6.10). Then

    \left\Vert x-x_{\epsilon} \right\Vert_{E} = O\left(\epsilon \right).

    Proof. Similar arguments as in the proof of Lemma 3.4, may lead to the solution of equations (1.1)-a and (6.10) that has the following form

    \begin{align*} x_{\epsilon }(t) & = \frac{1}{\lambda }\int_{1}^{t}\sin \lambda \left( t-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}\right] \mathrm{d } s \\ &\quad +\frac{\beta }{\Delta }\sin \lambda \left( t-1\right) \int_{1}^{\xi }\sin \lambda \left( \xi -s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon}}\right] \mathrm{d}s\\ &\quad -\frac{1}{\Delta }\sin \lambda \left( t-1\right) \int_{1}^{e}\sin \lambda \left( e-s\right) \mathcal{J}_{1}^{\alpha }\left[ f_{x_{\epsilon }}\right] \mathrm{d}s +\epsilon \frac{\lambda \sin \lambda \left( t-1\right) }{\Delta \cos \lambda }. \end{align*}

    Therefore

    x_{\epsilon }\left( t\right) = \frac{1}{\lambda }\phi_{x_{\epsilon }}\left( t\right) +H_{x_{\epsilon }}\left( \xi, \beta \right) g\left( t-1\right) +\epsilon \frac{\lambda \sin \lambda \left( t-1\right) }{\Delta \cos \lambda },

    and \Delta \cos \lambda \neq 0 , where

    \phi_{x_{\epsilon }}\left( t\right) = \int_{1}^{t}g\left( t-s\right) \mathcal{J}_{1}^{ \alpha }\left[ f_{x_{\epsilon }}\right] \left( s\right) \mathrm{d}s

    and H_{x_{\epsilon }}\left(\xi, \beta \right) = \frac{1}{\Delta }\left(\beta \phi_{x_{\epsilon }}\left(\xi \right) +\phi_{x_{\epsilon }} \left(e\right) \right) . As before, we find that

    \left\vert x\left( t\right) -x_{\epsilon }\left( t\right) \right\vert \leq \frac{N\rho \left( t\right) }{\Gamma \left( 1+\alpha \right) }\left\Vert x-x_{\epsilon } \right\Vert_{E} + \frac{\epsilon \lambda }{\left\vert \Delta \right\vert }\left\vert \frac{\sin \lambda \left( t-1\right) }{\cos \lambda } \right\vert,

    and

    \begin{align*} \left\vert \mathcal{D}_{1}^{\alpha }\left[ x_{\epsilon }\right] (t)- \mathcal{D}_{1}^{\alpha }\left[ x\right] (t)\right\vert & \leq \frac{R_{11}(t)}{ \lambda \Gamma \left( 1+\alpha \right) } N\left\Vert x-x_{\epsilon } \right\Vert_{E} + \epsilon \left\vert \frac{\lambda \mathcal{D}_{1}^{\alpha }\sin \lambda \left( t-1\right) }{ \Delta \cos \lambda } \right\vert \\ &\quad +\frac{1}{\left\vert \Delta \right\vert }\frac{N\left\Vert x-x_{\epsilon }\right\Vert_{E}}{\Gamma \left( 1+\alpha \right) }\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert. \end{align*}

    Hence

    \begin{align*} \left\vert x\left( t\right) \right. & \left. -x_{\epsilon } \left( t\right) \right\vert + \left\vert \mathcal{D}_{1}^{\alpha} \left[ x_{\epsilon }\right] (t)- \mathcal{D}_1^{ \alpha }\left[ x\right] (t)\right\vert \\ &\leq \frac{N}{ \Gamma \left( 1+\alpha \right)} \bigg[ \rho \left( t\right) + \frac{\left[ \beta \rho_{\alpha }\left( \xi \right) +\rho_{\alpha }\left( e\right) \right] \left\vert \mathcal{D}_{1}^{\alpha }\left[ g\left( t-1\right) \right] \right\vert }{\left\vert \Delta \right\vert }+\frac{ R_{11}(t)}{\lambda }\bigg] \left\Vert x-x_{\epsilon }\right\Vert_{E} \\ &\quad +\frac{\epsilon \lambda }{\left\vert \Delta \cos \lambda \right\vert } \bigg[ \left\vert \sin \lambda \left( t-1\right) \right\vert +\frac{\lambda }{\Gamma \left( 2-\alpha \right) }t\left( \ln t\right)^{1-\alpha }\bigg]. \end{align*}

    Consequently

    \left\Vert x - x_{\epsilon } \right\Vert_{E}\leq \frac{\epsilon \lambda }{\left\vert \Delta \cos \lambda \right\vert \left( 1-NQ\right) } \left[1+\frac{\lambda e^{2-\alpha }}{\Gamma \left( 2-\alpha \right) }\right].

    It is obvious that \left\Vert x-x_{\epsilon }\right\Vert_{E} = O\left(\epsilon \right) .

    In this section, we present some examples to illustrate the validity and applicability of the main results.

    Example 7.1. Consider problem (1.1) with

    \begin{equation} f(t, x, \tilde{x}) = \frac{1/6}{ 1 + \left\vert x\right\vert + \left\vert \tilde{x} \right\vert}. \end{equation} (7.1)

    Then f fulfills the Lipschitz condition (H1) such that \lambda = 2 , \beta = 2 , \xi = \frac{3}{2} ,

    N = \max \left\{ N_{1}, N_{2} \right\} = \frac{1}{6}.

    In Table 1, we show values of \alpha , t and Q . Thus NQ < 1 . Hence, by Theorem 4.1, the problem (1.1) with (7.1) has a unique solution on \left[1, e\right] .

    Table 1.  Some values of \alpha , t and Q .
    \alpha 1 1 1 0.75 0.25 0.5 0.5 0.5
    t 1 2 e e e e 2 1
    Q 1.38 1.35 2.94 3.58 4.58 4.15 1.90 0.20

     | Show Table
    DownLoad: CSV

    Example 7.2. Consider problem (1.1) with

    \begin{equation} f(t, x, \tilde{x}) = \frac{5}{8}\left( \sin x+\cos x\right) + \tilde{x} \end{equation} (7.2)

    or

    \begin{equation} f(t, x, \tilde{x}) = \frac{1}{4}\left( \sin x+\cos x\right) +\frac{7}{6}\tilde{x}. \end{equation} (7.3)

    Then f fulfills the Lipschitz condition (H1), where \lambda = 4 , \beta = 2 , \xi = \frac{3}{2} , N = \max \left\{ N_{1}, N_{2} \right\} = \frac{5}{4} or \frac{7}{6} . In Table 2, we show values of \alpha , t , Q , \frac{5}{4}Q and \frac{7}{6}Q . Thus, the condition (4.1) holds. Again, taking N = \max \left\{N_{1}, N_{2} \right\} = \frac{5}{4} or \frac{7}{6} , we have NQ < 1 . Note that all the assumptions of the Theorem 4.3 holds. Therefore problem (1.1) has a unique solution on E .

    Table 2.  Some values of \alpha , t , Q , \frac{5}{4}Q and \frac{7}{6}Q .
    \alpha 1.000 1.000 0.75 0.50 0.25
    t 1.030 1.002 1.03 1.03 1.03
    Q 0.860 0.780 0.57 0.44 0.39
    5Q/4 1.008 > 1 0.980 0.72 0.56 0.49
    7Q/6 0.940 < 1 0.910 0.67 0.52 0.46

     | Show Table
    DownLoad: CSV

    Example 7.3. Consider problem (1.1) with

    \begin{equation} f(t, x, \tilde{x}) = d_1( t) \sin \left[ d_2 ( t) \left( x+\tilde{x}\right) \right] + d_3( t) \cos \left[ d_4 ( t) \left( x+ \tilde{x} \right) \right], \end{equation} (7.4)

    for d_{i} \in C\left[1, e\right] with i = 1, 2, 3, 4 , that fulfils (H1) with

    N_{1} = N_{2} = \left\vert d_1( t) d_2( t) \right\vert +\left\vert d_3( t) d_4( t) \right\vert = 1,

    for example d_1 (t) = d_3 (t) = \frac{1}{4} , d_2(t) = d_4 (t) = 2 . Thus, we can put \beta = 2 , \xi = \frac{3}{2} , N = 1 , L_0 = \frac{1}{4} . In Table 3, one can find some values of \alpha , \lambda , t , Q and r , where r and Q are as defined in Theorem 4.3.

    Table 3.  Some values of \alpha , \lambda , t , Q and r .
    \alpha 1.00 1.00 0.750 0.750 0.500 0.500 0.25 0.250
    \lambda 1.35 7.76 7.760 9.600 7.760 9.600 7.80 9.600
    t 1.00 1.50 1.500 1.000 1.500 1.000 2.00 1.000
    Q 0.89 0.78 0.250 0.120 0.270 0.150 0.79 0.180
    r\geq 2.03 0.89 0.084 0.035 0.093 0.045 0.94 0.054

     | Show Table
    DownLoad: CSV

    Hence, by Theorem 4.3, problem (1.1) with (7.4) has a unique solution on B_{r} .

    Example 7.4. (Illustrative example on stability) Consider the FLE problem (1.1) with Hadamard fractional derivatives involving nonlocal boundary conditions:

    \begin{equation} \left\{ \begin{array}{ll} \mathcal{D}_{1}^{\frac{3}{8}} \left( \mathrm{D}^2 + \pi^{2}\right) x(t) = f\left( t, x(t), \mathcal{D}_1^{ \frac{3}{8}} \left[ x\right] (t) \right), & t\in \left( 1, \frac{15}{4}\right), \\ \mathrm{D}^{2} x\left(1 \right) = x(1) = 0, & \\ x ( \frac{15}{4}) = 2 x(\frac{11}{5}), & \end{array}\right. \end{equation} (7.5)

    with

    \begin{equation} f(t, x, \tilde{x}) = \frac{x^2}{5(t+1)^2(|x|+3)} + \frac{|x|}{5(t+1)^2} + \frac{\sin \tilde{x}}{4(t+3)^2} -2, \end{equation} (7.6)

    where \alpha = \frac{3}{8} , \lambda = \pi , \beta = 2 and \xi = \frac{11}{5} . It is obvious that

    \sin \pi \left(\frac{15}{4} -1 \right) = 0.7071 \neq -1.1756 = 2 \sin \pi \left( \frac{11}{5} - 1\right),

    So

    \Delta = \lambda \left( \sin \lambda \left( e-1\right) -\beta \sin \lambda \left( \xi -1\right) \right) = 5.9146 \neq 0.

    See the Figure 1. Also, we have

    \left\vert f(t, x_{1}, \tilde{x}_{1}) - f(t, x_{2}, \tilde{x}_{2}) \right\vert \leq \frac{16}{135} \left\vert x_{1} - x_{2} \right\vert + \frac{1}{49} \left\vert \tilde{x}_{1} - \tilde{ x}_{2} \right\vert,
    Figure 1.  Numerical results of \Delta where \beta = 1, \dots, 20 in Example 7.4.

    for each t\in \left[1, \frac{15}{4} \right] and all x_{i}, \tilde{x}_{i} \in \mathbb{R} , here N_{1} = \frac{16}{135} and N_{2} = \frac{1}{49} . Put \varphi (t) = \frac{t^2}{t^2+1} and

    N = \max \left\{ \frac{16}{135}, \frac{1}{49} \right\} = \frac{16}{135}.

    By using Eq (2.11), we obtain

    \rho_{\alpha } (t) \leq \frac{1}{\frac{3}{8} +1} \left(\frac{15}{4}\right)^{\frac{3}{8} +1} = 4.4770

    and using MatLab program,

    \begin{align*} \rho_{\alpha} \left( \frac{15}{4} \right) & = \int_1^\frac{15}{4} \left( \ln s \right)^{\alpha } \mathrm{d}s = 2.4371, \\ \rho_{\alpha} \left( \frac{11}{5} \right) & = \int_1^\frac{11}{5} \left( \ln s \right)^{\alpha } \mathrm{d}s = 0.8463. \end{align*}

    Also, by applying Eq (3.17), we obtain

    M_{\rho} = \frac{1}{\pi} + \frac{3}{\left\vert 5.9146 \right\vert} = 0.8255,

    Then we get

    \begin{align*} c_{\varphi } &\geq \frac{c\rho_{\alpha }\left( e\right) }{\lambda \Gamma \left( 1+\alpha \right) }+\frac{c\beta \rho_{\alpha }\left( \xi \right) }{ \left\vert \Delta \right\vert \Gamma \left( 1+\alpha \right) }\left[ 1+\frac{ N\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} \right] \\ &\quad + \frac{c\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) } \left[ \frac{1}{\left\vert \Delta \right\vert }+\frac{1}{\lambda }\frac{ N\rho_{\alpha }\left( t\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }} +\frac{1}{\left\vert \Delta \right\vert }\frac{N\rho_{\alpha }\left( e\right) }{\Gamma \left( 1+\alpha \right) -NM_{\rho }}\right]. \end{align*}

    Then the assumptions of Theorem 5.5 are satisfied. Then, problem (1.1) is Ulam-Hyers stable and generalized Ulam-Hyers stable.

    Example 7.5. (Illustrative example on solution dependence) Consider the FLE problem (1.1) with Hadamard fractional derivatives involving nonlocal boundary conditions:

    \begin{equation} \left\{ \begin{array}{ll} \mathcal{D}_{1}^{\frac{7}{9}} \left( \mathrm{D}^2 + \left(\frac{4\pi}{3} \right)^{2}\right) x(t) = f\left( t, x(t), \mathcal{D}_1^{\frac{7}{9}} \left[ x\right] (t) \right), & t\in \left( 1, \frac{12}{5}\right), \\ \mathrm{D}^{2} x\left(1 \right) = x(1) = 0, & \\ x(\frac{12}{5}) = \frac{7}{3} x(\frac{9}{4}), & \end{array}\right. \end{equation} (7.7)

    and

    \begin{equation} \mathcal{D}_{1}^{\frac{7}{9} -\epsilon} \bigg( \mathrm{D}^2 + \bigg(\frac{4\pi}{3} \bigg)^{2}\bigg) x(t) = f\left( t, x(t), \mathcal{D}_1^{\frac{7}{9}} \left[ x\right] (t) \right), \end{equation} (7.8)

    for t\in \left(0, 1\right) and \epsilon > 0 with

    \begin{equation} \begin{split} f(t, x, \tilde{x}) & = \frac{|x+4|(\sin^2(\pi t)+14)}{5+t} + \frac{(\tilde{x}^2+4)(\sin^2 (3\pi t)+6)}{(t+2)(|\tilde{x}+1|)} \\ & \quad + \frac{|\tilde{x}|(\cos^2 (3\pi t) +2)}{(t+0.5)^2} + \frac{13}{4}, \end{split} \end{equation} (7.9)

    where \alpha = \frac{7}{9} , \lambda = \frac{4\pi}{3} , \beta = \frac{7}{3} , \xi = \frac{9}{4} and 0 < \frac{7}{9} -\epsilon < \alpha < 1 . It is obvious that

    \sin \pi \left(\frac{15}{4} -1 \right) = 0.7071 \neq -1.1756 = 2 \sin \pi \left( \frac{11}{5} - 1\right),

    So

    \Delta = \lambda \left( \sin \lambda \left( e-1\right) -\beta \sin \lambda \left( \xi -1\right) \right) = 6.7606 \neq 0.

    See Figure 2. Also, we have

    \left\vert f(t, x_{1}, \tilde{x}_{1}) - f(t, x_{2}, \tilde{x}_{2}) \right\vert \leq \frac{4}{121} \left\vert x_{1} - x_{2} \right\vert + \frac{2}{75} \left\vert \tilde{x}_{1} - \tilde{ x}_{2} \right\vert,
    Figure 2.  Numerical results of \Delta where \beta = 1, \dots, 20 in Example 7.5.
    Figure 3.  Numerical results of \mathcal{D}_{1}^{\alpha} \left(\mathrm{D}^{2} + \lambda^{2}\right) x(t) and f\left(t, x(t), \mathcal{D}_1^{ \alpha} \left[x\right] (t) \right) where x(t) = \ln t and \alpha = \frac{1}{7} , \frac{1}{2} , \frac{7}{9} in Example 7.5, respectively.
    Figure 4.  Numerical results of (a) = \mathcal{D}_{1}^{\alpha} \left(\mathrm{D}^{2} + \lambda^{2}\right) x(t) and (b) = f(t, x(t), \mathcal{D}_1^{\alpha} \left[x\right] (t)) where x(t) = \ln t in Example 7.5, respectively.

    for each t\in \left[1, \frac{12}{5} \right] and all x_{i}, \tilde{x}_{i} \in \mathbb{R} , here N_{1} = \frac{4}{121} and N_{2} = \frac{2}{75} . Put \varphi (t) = \frac{t^2}{t^2 + 1} ,

    N = \max \left\{ \frac{4}{121}, \frac{2}{75} \right\}.

    By using Eq (2.11), we obtain

    \rho_{\alpha } (t) \leq \frac{1}{\frac{7}{9} +1} \left(\frac{12}{5}\right)^{\frac{7}{9} +1} = 2.6671

    and

    \begin{align*} \rho_{\alpha} \left( \frac{12}{5} \right) & = \int_1^\frac{12}{5} \left( \ln s \right)^{\alpha} \mathrm{d}s = 0.7953, \\ \rho_{\alpha} \left( \frac{9}{4} \right) & = \int_1^\frac{9}{4} \left( \ln s \right)^{\alpha } \mathrm{d}s = 0.6639. \end{align*}

    Also, by applying Eq (3.17), we obtain

    M_{\rho} = \frac{1}{\pi} + \frac{3}{\left\vert 6.7606 \right\vert} = 0.7620,

    Then for \epsilon = 0.1 , by using MatLab program, we get

    \begin{align*} \varrho_{\epsilon} \left( e\right) = \varrho_{\epsilon} \left( \frac{12}{5}\right) & = \int_{1}^{t}\left\vert \frac{\left( \ln s\right)^{\frac{7}{9} -\epsilon }}{\Gamma \left( 1+\frac{7}{9} -\epsilon \right) }- \frac{\left( \ln s\right)^{\alpha }}{\Gamma \left( 1+\alpha \right) } \right\vert \mathrm{d}s = 0.1886, \\ \varrho_{\epsilon} \left( \xi\right) = \varrho_{\epsilon} \left( \frac{9}{4}\right) & = \int_{1}^{t}\left\vert \frac{\left( \ln s\right)^{\frac{7}{9} -\epsilon }}{\Gamma \left( 1+\frac{7}{9} -\epsilon \right) }- \frac{\left( \ln s\right)^{\alpha }}{\Gamma \left( 1+\alpha \right) } \right\vert \mathrm{d}s = 0.1761 \end{align*}

    and

    \varrho \left( t\right) = \frac{3}{4\pi} \varrho_{\epsilon }\left( t\right) +\frac{1}{\left\vert 6.7606 \right\vert }\left[ \frac{7\times 0.1761}{3} + \varrho_{\epsilon }\left(0.1886\right) \right].

    Then the assumptions of Theorem 6.1 are satisfied. In addition to, by applying Eqs (6.6) and (6.7), we can calculate C_{22}(t) , C_{33}(t) and k_\epsilon .

    Table 4.  Numerical results of \mathcal{D}_{1}^{\alpha} \left(\mathrm{D}^{2} + \lambda^{2}\right) x(t) = f(t, x(t), \mathcal{D}_1^{\alpha} \left[x\right] (t)) in Example 7.5 for \alpha = \frac{1}{7} , \frac{1}{2} , \frac{7}{9} , here x(t) = \ln (t) and (a) = \mathcal{D}_{1}^{\alpha} \left(\mathrm{D}^{2} + \lambda^{2}\right) x(t) , (b) = f\left(t, x(t), \mathcal{D}_1^{\alpha} \left[x\right] (t) \right) .
    \alpha = \frac{1}{7} \alpha = \frac{1}{2} \alpha = \frac{7}{9}
    n t_N x(t_N) (a) (b) (a) (b) (a) (b)
    0 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
    1 1.0296 0.0292 -0.6481 20.4865 -2.0421 20.3037 -5.7442 20.6579
    2 1.0601 0.0584 0.3210 20.6028 1.2297 20.4569 2.3563 20.9298
    3 1.0915 0.0875 1.1548 20.8158 3.1716 20.6972 5.8459 21.1952
    4 1.1238 0.1167 1.9214 21.0045 4.6113 20.9107 7.9468 21.4028
    5 1.1571 0.1459 2.6447 21.0604 5.7818 21.0022 9.4115 21.4832
    \vdots \vdots \vdots \vdots \vdots \vdots \vdots \vdots \vdots
    20 1.7926 0.5836 11.5237 19.1220 14.8965 19.3122 16.5953 19.4690
    21 1.8456 0.6128 12.0495 19.0026 15.3004 19.1885 16.8158 19.3276
    22 1.9003 0.642 12.5704 18.5841 15.6925 18.7749 17.0254 18.8997
    23 1.9566 0.6712 13.0867 18.0950 16.0739 18.2893 17.2254 18.3996
    24 2.0145 0.7004 13.5986 17.8788 16.4453 18.0647 17.4166 18.1580
    25 2.0742 0.7296 14.1063 18.0394 16.8076 18.2061 17.5999 18.2808
    26 2.1356 0.7587 14.6102 18.2910 17.1613 18.4376 17.7761 18.4949
    27 2.1988 0.7879 15.1104 18.2508 17.5070 18.3843 17.9457 18.4272
    28 2.2639 0.8171 15.6071 17.9090 17.8453 18.0343 18.1094 18.0647
    29 2.3310 0.8463 16.1005 17.6773 18.1766 17.7921 18.2675 17.8109
    30 2.4000 0.8755 16.5908 17.8398 18.5014 17.9399 18.4206 17.9478

     | Show Table
    DownLoad: CSV

    Now, we describ discretization method and use Theorem 2.3 for this example. Fix n \geq 1 for N \in \{1, \cdots, n \} , define

    t_N = a \exp (\Delta T) = \exp (\Delta T),

    with

    \Delta T = \frac{1}{n}\ln \frac{b}{a} = \frac{1}{n}\ln \frac{12}{5}

    and [a, b] = [1, \frac{12}{5}] .

    Also,

    \left(\tau_{k}^\alpha \right) = k^{1-\alpha} - (k-1)^{1- \alpha} = k^{1-\frac{7}{9}} - (k-1)^{1- \frac{7}{9}} = k^{\frac{2}{9}} - (k-1)^{\frac{2}{9}}

    and

    \begin{align*} \zeta & = \dfrac{ (\Delta T)^{1-\alpha } }{a[1- \exp(-\Delta T)]\Gamma(2-\alpha)} = \dfrac{\left( \frac{1}{n}\ln \frac{12}{5} \right)^{ \frac{2}{9}}}{ \left[ 1- \exp \left( \frac{-1}{n}\ln \frac{12}{5} \right) \right] \Gamma \left( \frac{2}{9} \right) }. \end{align*}

    Thus, for f\left(t, x(t), \mathcal{D}_1^{ \frac{7}{9}} \left[x\right] (t) \right) , we get

    \mathcal{D}_1^{\frac{7}{9}} \left[ x \right] (t_N) = \tilde{ \mathcal{D}}_1^{ \frac{7}{9}}\left[ x\right] (t_N) + O \left( \frac{1}{n}\ln \frac{12}{5} \right),

    where

    \begin{align*} \tilde{\mathcal{D}}_1^{\frac{7}{9}} \left[ x \right] (t_N) & = \frac{ x(a)}{ \Gamma( 1 - \alpha) } \left( \ln \frac{ t_N}{ a } \right)^{ - \alpha } + \zeta \sum\limits_{k = 1}^{N} \left(\tau_{N-k+1}^\alpha \right) \frac{ x(t_k) - x(t_{k-1}) }{ \exp(k\Delta T)}. t_k\\ & = \frac{ x(1)}{ \Gamma( 1 - \frac{7}{9}) } \left( \ln t_N \right)^{ -\frac{7}{9} } + \zeta \sum\limits_{k = 1}^{N} \left(\tau_{N-k+1}^\frac{7}{9} \right) \frac{ x(t_k) - x( t_{k-1})}{ \exp \left( \frac{k}{n}\ln \frac{12}{5} \right) }. t_k\\ & = \frac{ x(1)}{ \Gamma \left( \frac{2}{9} \right) } \left( \ln t_N \right)^{ -\frac{7}{9} } + \zeta \sum\limits_{k = 1}^{N} \left(\tau_{N-k+1 }^\frac{7}{9} \right) \frac{ x(t_k) - x( t_{k-1})}{ \exp \left( \frac{k}{n} \ln \frac{12}{5} \right)}. t_k, \end{align*}

    The Langevin equation has been proposed to describe dynamical processes in a fractal medium in which the fractal and memory properties with a dissipative memory kernel are incorporated. However, it has been realized that the classical Langevin equation failed to describe the complex systems. Thus, the consideration of LDE in frame of fractional derivatives becomes compulsory. As a result of this interest, several results have been revealed and different versions of LDE have been under study. In this paper, we have presented some results dealing with the existence and uniqueness of solutions for boundary value problem of nonlinear Langevin equation involving Hadamard fractional order. As a first step, the boundary value problem is transformed to a fixed point problem by applying the tools of Hadamard fractional calculus. Based on this, the existence results are established by means of the Schaefer's fixed point theorem and Banach contraction principle.

    We claim that the results of this paper is new and generalize some earlier results. For instance, by taking \alpha = 1 in the results of this paper which can be considered a special case of a simple Jerk Chaotic circuit equation see [33]. The paper presented a discuss on the Ulam-Hyers-Rassias and generalized Ulam-Hyers-Rassias stabilities of the solution of the FLD using the generalization for the Gronwall inequality. We present an example to demonstrate the consistency to the theoretical findings. We also analyze the continuous dependence of solutions all on its right side function, initial value condition and the fractional order for FDE. Using these results, the properties of the solution process can be discussed through numerical simulation. We hope to consider this problem in a future work.

    Data sharing not applicable to this article as no data sets were generated or analyzed during the current study.

    The authors declare that the study was realized in collaboration with equal responsibility. All authors read and approved the final manuscript.

    The second author was supported by Bu-Ali Sina University. J. Alzabut would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.

    The authors declare that they have no competing interests.

    Table Algorithm 1.  The proposed method for the FLE problem with Hadamard fractional derivatives involving nonlocal boundary conditions (7.7) in Example 7.5 which we use the conditions of Theorem 2.3 there in.
    1  function [ParamMatrix]= discretization_method3(alpha, a, e, lambda, n, x_t)
    2  [xalpha, yalpha]=size(alpha);
    3  DeltaT=log(e/a)/n;
    4  ParamMatrix(1, 1) = 0;
    5  ParamMatrix(1, 2) = a;
    6  for j=3:2+7*yalpha
    7  ParamMatrix(1, j) = 0;
    8  end;
    9  
    10  column=3;
    11  j=1;
    12  while j < =yalpha
    13  for N=1:n
    14  ParamMatrix(N+1, 1) = N;
    15  tN=a*exp(N*DeltaT);
    16  ParamMatrix(N+1, 2) = tN;
    17  end;
    18  for N=1:n
    19  zeta = round(DeltaT^(1-alpha(j))/(a * (1- exp((-1)*DeltaT))*gamma(2-alpha(j))), 6);
    20  ParamMatrix(N+1, column) = zeta;
    21  ParamMatrix(N+1, column+5) =round(eval(subs(x_t, ParamMatrix(N+1, 2))), 6);
    22  s=0;
    23  k=1;
    24  while k < =N
    25  taukalpha= (N-k+1)^(1-alpha(j)) - (N-k)^(1-alpha(j));
    26  y2=eval(subs(x_t, ParamMatrix(k+1, 2)));
    27  y1=eval(subs(x_t, ParamMatrix(k, 2)));
    28  s = s + taukalpha*(y2-y1)*ParamMatrix(k+1, 2)/exp(k*DeltaT);
    29  k=k+1;
    30  end;
    31  A=eval(subs(x_t, a))* (log(ParamMatrix(N+1, 2)/a))^((-1)*alpha(j));
    32  HadamardD_xtN= round(A + zeta*s, 6);
    33  ParamMatrix(N+1, column+1) = HadamardD_xtN;
    34  end;
    35  for N=1:n
    36  s=0;
    37  k=1;
    38  while k < =N
    39  taukalpha= (N-k+1)^(1-alpha(j)) - (N-k)^(1-alpha(j));
    40  y2=lambda^2* eval(subs(x_t, ParamMatrix(k+1, 2)));
    41  y1=lambda^2* eval(subs(x_t, ParamMatrix(k, 2)));
    42  s = s + taukalpha*(y2-y1)*ParamMatrix(k+1, 2)/exp(k*DeltaT);
    43  k=k+1;
    44  end;
    45  A=eval(subs(x_t, a))* (log(ParamMatrix(N+1, 2)/a))^((-1)*alpha(j));
    46  HadamardD_xtN = round(A + zeta*s, 6);
    47  ParamMatrix(N+1, column+2) = HadamardD_xtN;
    48  end;
    49  for N=1:n
    50  s=0;
    51  k=1;
    52  while k < =N
    53  taukalpha= (N-k+1)^(1-alpha(j)) - (N-k)^(1-alpha(j));
    54  y2=eval(subs(diff(x_t, 2), ParamMatrix(k+1, 2)));
    55  y1=eval(subs(diff(x_t, 2), ParamMatrix(k, 2)));
    56  s = s + taukalpha*(y2-y1)*ParamMatrix(k+1, 2)/exp(k*DeltaT);
    57  k=k+1;
    58  end;
    59  A=eval(subs(diff(x_t, 2), a))* (log(ParamMatrix(N+1, 2)/a))^((-1)*alpha(j));
    60  HadamardD_xtN= round(A + zeta*s, 5);
    61  ParamMatrix(N+1, column+3) = HadamardD_xtN;
    62  ParamMatrix(N+1, column+4) = ParamMatrix(N+1, column+2)+ParamMatrix(N+1, column+3);
    63  ParamMatrix(N+1, column+6) = abs(ParamMatrix(N+1, column+5) +4)*((sin(pi*ParamMatrix(N+1, 2)))^2+14)/(5+ParamMatrix(N+1, 2)) + ((ParamMatrix(N+1, column+1))^2+4)*((sin(3*pi*ParamMatrix(N+1, 2)))^2 +6) / ((ParamMatrix(N+1, 2) +2) * (abs(ParamMatrix(N+1, column+1)+1))) + abs(ParamMatrix(N+1, column+1)) * ((cos(pi*ParamMatrix(N+1, 2)))^2+10)/((ParamMatrix(N+1, 2)+0.5)^2) + 13/4;
    64  end;
    65  j=j+1;
    66  column=column+7;
    67  end;
    68  end

     | Show Table
    DownLoad: CSV


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