Loading [MathJax]/jax/element/mml/optable/BasicLatin.js
Research article Special Issues

A constrained optimal control framework for vehicle platoons with delayed communication

  • Received: 22 November 2021 Revised: 02 May 2022 Accepted: 11 January 2023 Published: 21 March 2023
  • 58F15, 58F17, 53C35

  • Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging scenario. We present a single-level constrained optimal control framework that optimizes the fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution to the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.

    Citation: A M Ishtiaque Mahbub, Behdad Chalaki, Andreas A. Malikopoulos. A constrained optimal control framework for vehicle platoons with delayed communication[J]. Networks and Heterogeneous Media, 2023, 18(3): 982-1005. doi: 10.3934/nhm.2023043

    Related Papers:

    [1] Daniel Maxin, Fabio Augusto Milner . The effect of nonreproductive groups on persistent sexually transmitted diseases. Mathematical Biosciences and Engineering, 2007, 4(3): 505-522. doi: 10.3934/mbe.2007.4.505
    [2] Yansong Pei, Bing Liu, Haokun Qi . Extinction and stationary distribution of stochastic predator-prey model with group defense behavior. Mathematical Biosciences and Engineering, 2022, 19(12): 13062-13078. doi: 10.3934/mbe.2022610
    [3] Asma Alshehri, John Ford, Rachel Leander . The impact of maturation time distributions on the structure and growth of cellular populations. Mathematical Biosciences and Engineering, 2020, 17(2): 1855-1888. doi: 10.3934/mbe.2020098
    [4] Katarzyna Pichór, Ryszard Rudnicki . Stochastic models of population growth. Mathematical Biosciences and Engineering, 2025, 22(1): 1-22. doi: 10.3934/mbe.2025001
    [5] Brandy Rapatski, James Yorke . Modeling HIV outbreaks: The male to female prevalence ratio in the core population. Mathematical Biosciences and Engineering, 2009, 6(1): 135-143. doi: 10.3934/mbe.2009.6.135
    [6] Ping Yan, Gerardo Chowell . Modeling sub-exponential epidemic growth dynamics through unobserved individual heterogeneity: a frailty model approach. Mathematical Biosciences and Engineering, 2024, 21(10): 7278-7296. doi: 10.3934/mbe.2024321
    [7] Hao Wang, Yang Kuang . Alternative models for cyclic lemming dynamics. Mathematical Biosciences and Engineering, 2007, 4(1): 85-99. doi: 10.3934/mbe.2007.4.85
    [8] Hisashi Inaba . The Malthusian parameter and R0 for heterogeneous populations in periodic environments. Mathematical Biosciences and Engineering, 2012, 9(2): 313-346. doi: 10.3934/mbe.2012.9.313
    [9] Jim M. Cushing . The evolutionarydynamics of a population model with a strong Allee effect. Mathematical Biosciences and Engineering, 2015, 12(4): 643-660. doi: 10.3934/mbe.2015.12.643
    [10] Jie Bai, Xiunan Wang, Jin Wang . An epidemic-economic model for COVID-19. Mathematical Biosciences and Engineering, 2022, 19(9): 9658-9696. doi: 10.3934/mbe.2022449
  • Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging scenario. We present a single-level constrained optimal control framework that optimizes the fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution to the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.



    In this paper, we study the coupled chemotaxis-fluid models with the initial-bounary conditions

    {nt+un=Δn(nc)+γnμn2,in Q(0,T)×Ω,ct+uc=Δcc+n+f,in Q,ut+uu=Δuπ+nφ,in Q,u=0,in Q,nν=cν=0,u=0,on (0,T)×Ω,n(x,0)=n0(x),c(x,0)=c0(x),u(x,0)=u0(x),in Ω, (1.1)

    where ΩR2 is a bounded domain with smooth boundary Ω. ν is the outward normal vector to Ω, and γ, μ are positive constants. n, c denote the bacterial density, the oxygen concentration, respectively. u, π are the fluid velocity and the associated pressure. Here, the function f denotes a control that acts on chemical concentration, which lies in a closed convex set U. We observe that in the subdomains where f0 we inject oxygen, and conversely where f0 we extract oxygen.

    In order to understand the development of system (1.1), let us mention some previous contributions in this direction. Jin [11] dealed with the time periodic problem of (1.1) in spatial dimension n=2,3. Jin [12] also obtained the existence of large time periodic solution in ΩR3 without the term uu.

    Espejo and Suzuki [6] discussed the chemotaxis-fluid model

    nt+un=Δn(nc)+n(γμn), (1.2)
    ct+uc=Δcc+n, (1.3)
    ut=Δuπ+nφ, (1.4)
    u=0, (1.5)
    nν=cν=0,u=0. (1.6)

    They proved the global existence of weak solution. Tao and Winkler [17] proved the existence of global classical solution and the uniform boundedness. Tao and Winkler [18] also obtained the global classical solution and uniform boundedness under the condition of μ>23.

    The optimal control problems governed by the coupled partial differential equations is important. Colli et al. [4] studied the distributed control problem for a phase-field system of conserved type with a possibly singular potential. Liu and Zhang [14] considered the optimal control of a new mechanochemical model with state constraint. Chen et al. [3] studied the distributed optimal control problem for the coupled Allen-Cahn/Cahn-Hilliard equations. Recently, Guillén-González et al. [9] studied a bilinear optimal control problem for the chemo-repulsion model with the linear production term. The existence, uniqueness and regularity of strong solutions of this model are deduced. They also derived the first-order optimality conditions by using a Lagrange multipliers theorem. Frigeri et al. [8] studied an optimal control problem for two-dimensional nonlocal Cahn-Hilliard-Navier-Stokes systems with degenerate mobility and singular potential. Some other results can be found in [2,5,13,15,19].

    In this paper, we discuss the optimal control problem for (1.1). We adjust the external source f, so that the bacterial density n, oxygen concentration c and fluid velocity u are as close as possible to a desired state nd, cd and ud, and at the final moment T is as close as possible to a desired state nΩ, cΩ and uΩ. The main difficulties for treating the problem are caused by the nonlinearity of uu. Our method is based on fixed point method and Simon's compactness results. We overcome the above difficulties and derive first-order optimality conditions by using a Lagrange multipliers theorem.

    In this section, we will construct the existence and some priori estimates of the linearized problem for the chemotaxis-Navier-Stokes system in a bounded domain ΩR2. The proofs in this section will be established for a detailed framework.

    In the following lemmas we will state the Gagliardo-Nirenberg interpolation inequality [7].

    Lemma 2.1. Let l and k be two integers satisfying 0l<k. Suppose that 1q, r, p>0 and lka1 such that

    1plN=a(1qkN)+(1a)1r. (2.1)

    Then, for any uWk,q(Ω)Lr(Ω), there exist two positive constants C1 and C2 depending only on Ω, q, k, r and N such that the following inequality holds

    DluLpc1DkuaLqu1aLr+c2uLr

    with the following exception: If 1<q< and klNq is a non-negative integer, the (2.1) holds only for a satisfying lka<1.

    The following log-interpolation inequality has been proved by [1].

    Lemma 2.2. Let ΩR2 be a bounded domain with smooth boundary. Then for all non-negative uH1(Ω), there holds

    u3L3(Ω)δu2H1(Ω)(u+1)log(u+1)L1(Ω)+p(δ1)uL1(Ω),

    where δ is any positive number, and p() is an increasing function.

    We first consider the existence of solutions to the linear problem of system (1.1). Assume functions u0H1(Ω), ˆuL4(0,T;L4(Ω)),ˆnL2(0,T;L2(Ω)), and consider

    {utΔu+ˆuu=π+ˆnφ,in Q,u=0,in Q,u=0,on Ω,u(x,0)=u0(x),in Ω. (2.2)

    By using fixed point method, the existence of solutions can be easily obtained. Therefore, we ignore the process of proof and just give the regularity estimate.

    Lemma 2.3. Let u0H1(Ω), ˆuL4(0,T;L4(Ω)), ˆnL2(0,T;H1(Ω)),φL(Q), and u be the solution of the problem (2.2), then uL(0,T;H1(Ω))L2(0,T;H2(Ω)) and utL2(0,T;L2(Ω)).

    Proof. Multiplying the first equation of (2.2) by u, and integrating it over Ω, we get

    12ddtΩu2dx+Ω|u|2dx+Ωu2dx=Ωˆnφudx+Ωu2dxˆnL2uL2+u2L2C(ˆn2L2+u2L2).

    By Gronwall's inequality, we have

    u2L2+T0u2H1dτC(T0ˆn2L2dτ+u02L2).

    Operating the Helmholtz projection operator P to the first equation of (2.2), we know

    ut+Au+P(ˆuu)=P(ˆnφ),

    where A:=PΔ is called Stokes operator, which is an unbounded self-adjoint positive operator in L2 with compact inverse, for more properties of Stokes operator, we refer to [10]. Note that u=0, that is Pu=u, PΔu=Δu, Put=ut. So, in following calculations, we ignore the projection operator P. Multiplying this equation by Δu, and integrating it over Ω, we get

    12ddtΩ|u|2dx+Ω|Δu|2dx+Ω|u|2dx=ΩP(ˆuu)ΔudxΩP(ˆnφ)Δudx+Ω|u|2dx.

    For the terms on the right, we have

    ΩP(ˆuu)ΔudxΩP(ˆnφ)Δudx+Ω|u|2dxˆuL4uL4ΔuL2+ˆnL2ΔuL2+u2L2ˆuL4u1/2L2Δu3/2L2+ˆuL4uL2ΔuL2+ˆnL2ΔuL2+u2L212Δu2L2+C(ˆu4L4+ˆu2L4+1)u2L2+ˆn2L2.

    Therefore, we get

    ddtu2L2+u2H1C(ˆu4L4+ˆu2L4+1)u2L2+Cˆn2L2+C.

    By Gronwall's inequality, we derive

    u2L2+T0u2H1dτC.

    Multiplying the first equation of (2.2) by ut, and combining with above inequality, we have

    T0Ω|ut|2dxdtC.

    Summing up, we complete the proof.

    For the above solution u, we consider the following linear problem

    {ctΔc+uc+c=ˆn++f,in Q,cν=0,on (0,T)×Ω,c(x,0)=c0(x),in Ω. (2.3)

    Along with fixed point method, the existence of solutions can be easily obtained. Thus we omit the proof and only give the regularity estimate.

    Lemma 2.4. Let c0H2(Ω), ˆnL2(0,T;H1(Ω)), fL2(0,T;H1(Ω)), u be the solution of the problem (2.2), and c be the solution of (2.3). Then cL((0,T),H2(Ω))L2((0,T),H3(Ω)) and ctL2(0,T;L2(Ω)).

    Proof. Multiplying the first equation of (2.3) by c, and integrating it over Ω, we infer from Ωc(uc)=12Ωc2udx=0 that

    12ddtΩc2dx+Ω|c|2dx+Ωc2dxˆnL2cL2+fL2cL2.

    Therefore, we have

    c2L2+c2H1C(c02L2+t0(ˆn2L2+f2L2)dτ).

    Multiplying the first equation of (2.3) by Δc, and integrating it over Ω, we get

    12ddtΩ|c|2dx+Ω|Δc|2dx+Ω|c|2dx=ΩucΔcdxΩΔcˆndxΩΔcfdx.

    Using the Young inequality and the Hölder inequality, we obtain

    ΩucΔcdxΩΔcˆndxΩΔcfdxuL4cL4ΔcL2+ˆnL2ΔcL2+fL2ΔcL2CuH1(c12L2Δc12L2+cL2)ΔcL2+ˆnL2ΔcL2+fL2ΔcL2=CuH1c12L2Δc32L2+CcL2ΔcL2+ˆnL2ΔcL2+fL2ΔcL212Δc2L2+Cu4H1c2L2+C(ˆn2L2+f2L2).

    Combining this and above inequalities, we conclude

    ddtc2L2+c2H1Cu4H1c2L2+C(ˆn2L2+f2L2).

    We therefore verify that

    c2L2+t0c2H1C(t0ˆn2L2dτ+t0f2L2dτ).

    Applying to the first equation of (2.3), multiplying it by Δc, and integrating over Ω give

    12ddtΩ|Δc|2dx+Ω|Δc|2dx+Ω|Δc|2dx=Ω(uc)ΔcdxΩˆn+ΔcdxΩfΔcdx.

    For the terms on the right, we obtain

    Ω(uc)ΔcdxΩˆn+ΔcdxΩfΔcdxΔcL2(uL4ΔcL4+uL4cL4)+ˆnL2ΔcL2+fL2ΔcL2ΔcL2(uL4Δc12L2Δc12L2+uL4ΔcL2+u12L2Δu12L2c12L2Δc12L2+uL2c12L2Δc12L2+u12L2Δu12L2cL2+uL2cL2)+ˆnL2ΔcL2+fL2ΔcL212Δc2L2+C(1+Δc2L2+Δu2L2+ˆn2L2+f2L2).

    Straightforward calculations yield

    Δc2L2+t0Δc2H1dτC(1+t0ˆn2H1dτ+t0f2H1dτ).

    Multiplying the first equation of (2.3) by ct, and combining with above inequality, we have

    T0Ω|ct|2dxdtC,

    and thereby precisely arrive at the conclusion.

    With above solutions u and c in hand, we deal with the following linear problem.

    {ntΔn+un+n=(nc)+(1+γ)ˆn+μˆn+n,in Q,nν|Ω=0,n(x,0)=n0(x),in Ω. (2.4)

    By a similar argument as the above two problems, the existence of solutions can be easily obtained. Therefore, we only give the regularity estimate.

    Lemma 2.5. Suppose 0n0H1(Ω), ˆnL2(0,T;H1(Ω))L4(0,T;L4(Ω)), and u, c, n are the solutions of the problem (2.2), (2.3) and (2.4), respectively. Then n0, nL(0,T;H1(Ω))L2(0,T;H2(Ω)) and ntL2(0,T;L2(Ω)).

    Proof. Firstly, we verify the nonnegativity of n. We examine the set A(t)={x:n(x,t)<0}. Along with (2.4), we get

    ddtA(t)ndxA(t)nνds+A(t)ndx=(1+γ)A(t)ˆn+dxμA(t)ˆn+ndx.

    Since nν0 on {n<0}, from this we deduce that the right hand side is nonnegative. Integrating this equality on [0,t] gives

    A(t)ndxdτ+t0A(t)ndxdτ=0.

    Then, we get n0.

    Next, multiplying the first equation of (2.4) by n, and integrating it over Ω, we get

    12ddtΩn2dx+Ω(n2+|n|2)dx+μΩˆn+n2dx=Ωncndx+(1+γ)Ωnˆn+dxnL4cL4nL2+(1+γ)ˆnL2nL2C(n12L2n12L2+nL2)cH2nL2+(1+γ)ˆnL2nL2C(n2L2c4H2+n2L2c2H2+ˆnL2)+12n2H1.

    So, we derive that

    n2L2+T0n2H1dtC(1+T0ˆn2L2dt).

    Multiplying the first equation of (2.4) by Δn, and integrating it over Ω, we get

    12ddtΩ|n|2dx+Ω|Δn|2dx+Ω|n|2dx=ΩunΔndx+Ω((nc)Δn(1+γ)ˆn+Δn+μˆn+nΔn)dxuL4nL4ΔnL2+nL4ΔcL4ΔnL2+nL4cL4ΔnL2+(1+γ)ˆnL2ΔnL2+μnL4ˆnL4ΔnL2CuH1(n12L2Δn12L2+nL2)ΔnL2+nL4(Δc12L2Δc12L2+ΔcL2)ΔnL2+μnL4ˆnL4ΔnL2+(n12L2Δn12L2+nL2)cH1ΔnL2+(1+γ)ˆnL2ΔnL212Δn2L2+C(n2L2+n4L4+Δc4L2+Δc2L2+ˆn2L2+ˆn4L4)12Δn2L2+C(1+n2L2+n4L2+n2L2n2L2+Δc2L2+ˆn2L2+ˆn4L4).

    Straightforward calculations yield

    n2L2+T0Ω(|Δn|2+|n|2+ˆn+|n|2)dxdtC.

    Multiplying the first equation of (2.4) by nt, and combining with above inequality, we have

    T0Ω|nt|2dxdtC.

    The proof is complete.

    Introduce the spaces

    Xu=L4(0,T;L4(Ω)),Xn=L4(0,T;L4(Ω))L2(0,T;H1(Ω)),Yu=L(0,T;H1(Ω))L2(0,T;H2(Ω)),Yn=L(0,T;H1(Ω))L2(0,T;H2(Ω)).

    Define a map

    F:Xu×XnXu×Xn,F(ˆu,ˆn)=(u,n),

    where the (u,n) is the solution of the decoupled linear problem

    {ntΔn+un+n=(nc)+(1+γ)ˆn+μˆn+n,in (0,T)×ΩQ,ctΔc+uc+c=ˆn++f,in (0,T)×ΩQ,utΔu+ˆuu=π+ˆnφ,in (0,T)×ΩQ,u=0,in (0,T)×ΩQ,nν=cν=0,u=0,on (0,T)×Ω,n(x,0)=n0(x),c(x,0)=c0(x),u(x,0)=u0(x),in Ω.

    Next, we use fixed point method to prove the local existence of solutions of the problem (1.1).

    Lemma 2.6. The map F:Xu×XnXu×Xn is well defined and compact.

    Proof. Let (ˆn,ˆu)Xu×Xn, by Lemmas 2.3, 2.4, 2.5 we deduce that (n,u)=F(ˆn,ˆu) is bounded in Yu×Yn. Note that the embeddings H2(Ω)H1(Ω) is compact and interpolating between L(0,T;H1(Ω)) and L2(0,T;H2(Ω)). It is easy to get that u is bounded in L4(0,T;L4(Ω)) and n is bounded in L4(0,T;L4(Ω))L2(0,T;H1(Ω)). Therefore, the operator F:Xu×XnXu×Xn is a compact operator.

    From Lemma 2.6, (n,u)Yn×Yu satisfies pointwisely a.e. in Q the following problem

    {ntΔn+un+n=(nc)+α(1+γ)nμn2,in Q,ctΔc+uc+c=n+αf,in Q,utΔu+uu=π+αnφ,in Q,u=0,in Q,nν=cν=0,u=0,on (0,T)×Ω,n(x,0)=n0(x),c(x,0)=c0(x),u(x,0)=u0(x),in Ω. (3.1)

    In order to prove the existence of solution, we first give some a priori estimates.

    Lemma 3.1. Let (n,c,u) be a local solution to (3.1). Then, it holds that

    nL1+t0(nL1+nL2)dτC, (3.2)
    u2L2+t0u2H1dτC, (3.3)
    c2L2+t0c2H1dτC. (3.4)

    Proof. With Lemma 2.5 in hand, we get n0. Integrating the first equation of (3.1) over Ω, we see that

    ddtΩndx+Ωndx+μΩn2dx=α(1+γ)Ωndxμ2Ωn2dx+C.

    Solving this differential inequality, we obtain that

    nL1+t0(nL1+nL2)dτC.

    Multiplying the third equation of (3.1) by u, and integrating it over Ω, we get

    12ddtΩu2dx+Ω|u|2dx+Ωu2dx=αΩnφudx+Ωu2dxnL2uL2+u2L2C(n2L2+u2L2).

    Therefore, we see that

    u2L2+t0uH1dτC.

    By the Gagliardo-Nirenberg interpolation inequality, we deduce that

    t0u4L4dτCt0(u2L2u2L2d+u2L2)τu2L2t0u2L2dτ+t0u2L2dτC.

    Multiplying the third equation of (3.1) by Δu, and integrating it over Ω, we get

    ddtu2L2+u2H1C(u4L4+u2L4+1)u2L2+Cn2L2+C.

    Thus, we know

    u2L2+t0u2H1dτC.

    Multiplying the second equation of (3.1) by c, and integrating it over Ω, we have

    12ddtΩc2dx+Ω|c|2dx+Ωc2dxnL2cL2+αfL2cL2.

    Then, we have

    cL2+t0cH1dτC.

    Multiplying the second equation of (3.1) by Δc, and integrating it over Ω, we get

    ddtc2L2+c2H1Cu4H1c2L2+C(n2L2+f2L2).

    Further, we have

    c2L2+t0c2H1dτC.

    The proof is complete.

    Lemma 3.2. Let (n,c,u) be a local solution to (3.1). Then, it holds that

    (n+1)ln(n+1)L1+c2L2+c2H1C. (3.5)

    Proof. We rewrite the first equation of (3.1) as

    ddt(n+1)+u(n+1)Δ(n+1)=((n+1)c)+Δc+α(1+γ)nμn2.

    Multiplying the above equation by ln(n+1) and integrating the equation, we have

    ddtΩ(n+1)ln(n+1)dx+4Ω|n+1|2dxΩ(n+1)cdx+ΩΔcln(n+1)dx+α(1+γ)Ωnln(n+1)dx=I1+I2+I3.

    For I1, integrating by parts and using Young's inequality with small δ, we get

    I1=ΩnΔcdxnL2ΔcL2δΔc2L2+Cn2L2.

    For the term I2, we have

    I2=ΩΔcln(n+1)dxδΔc2L2+Cln(n+1)2L2δΔc2L2+CΩ(n+1)ln(n+1)dx.

    For the rest term I3, straightforward calculations yield

    I3=α(1+γ)Ωnln(n+1)dx(1+γ)Ω(n+1)ln(n+1)dx.

    Combining I1, I2 with I3, we conduct that

    ddtΩ(n+1)ln(n+1)dx+4Ω|n+1|2dxδΔc2L2+CΩ(n+1)ln(n+1)dx+Cn2L2. (3.6)

    Multiplying the second equation of (3.1) by Δc, and integrating it over Ω, we get

    12ddtΩ|c|2dx+Ω|Δc|2dx+Ω|c|2dx=ΩucΔcdxΩΔcndxαΩΔcfdx.

    Straightforward calculations yield

    ddtc2L2+c2H1Cc2L2+C(n2L2+f2L2). (3.7)

    Combing (3.6) and (3.7), it follows that

    ddtΩ(n+1)ln(n+1)dx+ddtc2L2+(1δ)c2H1+4Ω|n+1|2dxCΩ(n+1)ln(n+1)dx+C(f2L2+n2L2).

    Taking δ small enough, and solving this differential inequality, we obtain that

    (n+1)ln(n+1)L1+c2L2+c2H1C.

    The proof is complete.

    Lemma 3.3. Assume fL2(0,T;H1(Ω)), let (n,c,u) be a local solution to (3.1). Then, it holds that

    n2L2+Δc2L2+t0nH1dτ+t0ΔcH1dτC. (3.8)

    Proof. Taking the L2-inner product with n for the first equation of (3.1) implies

    12ddtΩn2dx+Ω(n2+|n|2)dx+μΩn3dx=Ωncndx+α(1+γ)Ωn2dx=12Ωn2Δcdx+α(1+γ)Ωn2dx.

    Here, we note that

    |Ωn2Δcdx|n2L3ΔcL3Cn2L3(Δc23L2c13L2+cL2)Cn2L3(Δc23L2+1).

    From Lemma 2.2 and (3.2), it follows that

    χ2Ωn2ΔcdxC(δn2H1(n+1)log(n+1)L1+p(δ1)nL1)23(Δc23L2+1)C(δn2H1+p(δ1))23(Δc23L2+1)C(δn43H1Δc23L2+δn43H1+p23(δ1)Δc23L2+p23(δ1))δΔc2L2+Cδ12n2H1+C1/2δp(δ1).

    As an immediate consequence

    ddtn2L2+n2H1δΔc2L2+Cδ12n2H1+Cn2L2. (3.9)

    Applying to the first equation of (3.1), multiplying it by Δc, and integrating over Ω give

    12ddtΩ|Δc|2dx+Ω|Δc|2dx+Ω|Δc|2dx=Ω(uc)ΔcdxΩnΔcdxΩfΔcdx=I4+I5.

    For I4, by using the Gagliardo-Nirenberg interpolation inequality, we get

    I4=Ω(uc)ΔcdxΔcL2(uL4ΔcL4+uL4cL4)ΔcL2(uL4Δc12L2Δc12L2+uL4ΔcL2+u12L2Δu12L2c12L2Δc12L2+uL2c12L2Δc12L2+u12L2Δu12L2cL2+uL2cL2)14Δc2L2+C(1+Δc2L2+Δu2L2).

    For the term I5, we have

    I5=ΩnΔcdxΩfΔcdxC(n2L2+f2L2)+14Δc2L2.

    Along with I4 and I5, we conclude

    ddtΔc2L2+Δc2L2+Δc2L2C(1+Δc2L2+Δu2L2+n2L2+f2L2). (3.10)

    Combining (3.9) and (3.10), it follows that

    ddt(n2L2+Δc2L2)+Δc2L2+(1Cδ12)n2H1+(1δ)Δc2L2C(1+Δc2L2+Δu2L2+n2L2+f2L2).

    By choosing δ small enough and using (3.3) and (3.5), we have

    n2L2+Δc2L2+t0nH1dτ+t0ΔcH1dτC.

    The proof is complete.

    Lemma 3.4. Assume fL2(0,T;H1(Ω)), let (n,c,u) be a local solution to (3.1). Then, it holds that

    n2L2+t0n2H2dτC. (3.11)

    Proof. Taking the L2-inner product with Δn for the first equation of (3.1) implies

    12ddtΩ|n|2dx+Ω|Δn|2dx+Ω|n|2dx=ΩunΔndx+Ω(nc)Δndx+(1+γ)Ω|n|2dx+μΩn2Δndx=I6+I7+I8.

    For the term I6, with the estimate (3.3), we have

    I6=ΩunΔndx=12Ωu(n)2dxuL2n2L4uL2(n12L2Δn12L2+nL2)2δΔn2L2+Cn2L2.

    For the term I7, taking (3.8) into considering, we conduct that

    I7=Ω(nc)Δndx=Ω(nc+nΔc)ΔndxΔnL2(nL3cL6+nCΔcL2)CΔnL2(nH13cH1+nH43ΔcL2)CnH2nH43cH2Cn53H2n13L2cH2δn2H2+C(δ)n2L2c6H2δn2H2+C.

    For the term I8, thanks to the nonnegativity of n, we see that

    I8=(1+γ)Ω|n|2dx+μΩn2Δndx=(1+γ)Ω|n|2dx2μΩ|n|2ndx(1+γ)n2L2.

    Combine the estimates about I6, I7 and I8, it follows that

    ddtn2L2+(14δ)n2H2Cn2L2+C.

    By taking δ small enough, we get

    n2L2+t0n2H2dτC.

    Therefore, this proof is complete.

    Lemma 3.5. The operator F:Xu×XnXu×Xn, is continuous.

    Proof. Let {(ˆnm,ˆum)}mN be a sequence of Xu×Xn, Then, with Lemmas 2.3, 2.4 and 2.5 in hand, we conduct that {(nm,um)=F(ˆnm,ˆum)}mN is bounded in Yu×Yn. Taking the compactness of Yu×Yn in Xu×Xn into consider, we see that F is a compact operator, which means there exists a subsequence of {F(ˆnm,ˆum)}mN, for convenience, still denoted as {F(ˆnm,ˆum)}mN, and exists an element (ˆn,ˆu) in Yu×Yn such that

    F(ˆnm,ˆum)(ˆn,ˆu) weakly in Yu×Yn and strongly in Xu×Xn.

    Let m and take the limit, it is clear that (n,u)=F(ˆnm,ˆum) and (ˆnm,ˆum)=(ˆn,ˆu), this means that F(ˆnm,ˆum)=(ˆnm,ˆum). Since uniqueness of limit, the map F is continuous.

    Theorem 3.1. Let u0H1(Ω), n0H1(Ω), c0H2(Ω) with n00 in Ω, and fL2(0,T;H1(Ω)), then (1.1) exists unique strong solution (n,c,u). Moreover, there exists a positive C constant such that

    nL(0,T;H1(Ω))+nL2(0,T;H2(Ω))+ntL2(0,T;L2(Ω))+cL(0,T;H2(Ω))+cL2(0,T;H3(Ω))+ctL2(0,T;L2(Ω))+uL(0,T;H1(Ω))+uL2(0,T;H2(Ω))+utL2(0,T;L2(Ω))C. (3.12)

    Proof. From Lemmas 3.1, 3.3 and 3.4, it is easy to verify the existence of solution and (3.11). Therefore, we will prove the uniqueness of the solution in the following part. For convenience, we set n=n1n2, c=c1c2 and u=u1u2, where (ni,ci,ui) is the strong solution of the system, where i=1,2. Thus, we obtain the following system

    ntΔn+u1n+un2=(n1c)(nc2)+γnμn(n1+n2),in (0,T)×ΩQ, (3.13)
    ctΔc+u1c+uc2+c=n,in (0,T)×ΩQ, (3.14)
    utΔu+u1u+uu2=nφ,in (0,T)×ΩQ, (3.15)
    u=0,in (0,T)×ΩQ, (3.16)
    nν=cν=0,u=0,on (0,T)×Ω, (3.17)
    n0(x)=c0(x)=u0(x)=0,in Ω. (3.18)

    Taking the L2-inner product with n for the (3.13) implies

    12ddtΩn2dx+Ω|n|2dx+Ωn2dxΩun2ndx+Ωn1cndx+Ωnc2ndx+(1+γ)Ωn2dx=I9+I10+I11+I12.

    For the term I9, due to the estimates (3.3) and (3.8), we have

    I9=Ωun2ndxn2L2uL4nL4Cn2L2uH1(n12L2n12L2+nL2)δ3n2L2+Cn2L2.

    For the term I10, with the estimate (3.8) and (3.11), we get

    I10=Ωn1cndxnL2n1L4cL4CnL2n1H1cH1δ3n2L2+C.

    For the term I11,

    I11=Ωnc2ndxnL2c2L4nL4nL2c2H1nH1δ3n2L2+C.

    With the use of estimates Ii(i=9,10,11,12), we have

    ddtn2L2+nH1δn2L2+Cn2L2+C. (3.19)

    Taking the L2-inner product with c for the (3.14) implies

    12ddtΩc2dx+Ω|c|2dx+Ωc2dx=Ωu1ccdxΩuc2cdx+Ωncdxc2L4u1L2+uL2c2L4cL4+nL2cL2C(c12L2c12L2+cL2)2u1L2+(c12L2c12L2+cL2)uL2c2H1+nL2cL2δc2L2+Cc2L2.

    Then, we get

    ddtc2L2+cH1δc2L2+Cc2L2. (3.20)

    Taking the L2-inner product with c for the (3.15) implies

    12Ωu2dx+Ω|u|2dx=Ωnφudx.

    Straightforward calculations yield

    ddtu2L2+uH1C(u2L2+n2L2). (3.21)

    Then, a combination of (3.19), (3.20) and (3.21) yields

    ddt(n2L2+c2L2+u2L2)+(nH1+cH1+uH1)δ(n2L2+c2L2+u2L2)+(n2L2+c2L2+u2L2)+C.

    By choosing δ small enough, we get

    ddt(n2L2+c2L2+u2L2)C(n2L2+c2L2+u2L2)+C.

    Applying Gronwall's lemma to the resulting differential inequality, we finally obtain the uniqueness of the solution.

    In this section, we will prove the existence of the optimal solution of control problem. The method we use for treating this problem was inspired by some ideas of Guillén-González et al [9]. Assume UL2(0,T;H1(Ωc)) is a nonempty, closed and convex set, where control domain ΩcΩ, and ΩdΩ is the observability domain. We adjust the external source f, so that the bacterial density n, oxygen concentration c and fluid velocity u are as close as possible to a desired state nd, cd and ud, and at the final moment T is as close as possible to a desired state nΩ, cΩ and uΩ. We consider the optimal control problem as follows

    Minimize the cost functional

    J(n,c,u,f)=β12nnd2L2(Qd)+β22ccd2L2(Qd)+β32uud2L2(Qd)+β42n(T)nΩ2L2(Ωd)+β52c(T)cΩ2L2(Ωd)+β62u(T)uΩ2L2(Ωd)+β72f(x,t)2L2(Qc), (4.1)

    subject to the system (1.1). Moreover, the nonnegative constants βi,i=1,2,,7 are given but not all zero, the functions nd, cd, ud represents the desired states satisfying

    ndL2(Qd),cdL2(Qd),udL2(Qd),nΩL2(Ωc),cΩL2(Ωc),uΩL2(Ωc),fU.

    The set of admissible solutions of optimal control problem (4.1) is defined by

    Sad={s=(n,c,u,f)H:s is a strong solution of (1.1)}.

    The function space H is given by

    H=Yn×Yc×Yu×U,

    where Yc=L(0,T;H2(Ω))L2(0,T;H3(Ω)).

    Now, we prove the existence of a global optimal control for problem (1.1).

    Theorem 4.1. Suppose fU is satisfied, and n00, then the optimal control problem (4.1) admits a solution (ˉn,ˉc,ˉu,ˉf)Sad.

    Proof. Along with Theorem 3.1, we conduct that Sad, then there exists the minimizing sequence {(nm,cm,um,fm)}mNSad such that

    limm+J(nm,cm,um,fm)=inf(n,c,u,f)SadJ(n,c,u,f). (4.2)

    According to the definition of Sad, for each mN there exists (nm,cm,um,fm) satisfying

    {nmt+umnm=Δnm(nmcm)+γnmμn2m,in Q,cmt+umcm=Δcmcm+nm+fm,in Q,umt+umum=Δumπ+nmφ,in Q,um=0,in Q,nmν|Ω=cmν|Ω=0,um|Ω=0,nm(0)=n0,cm(0)=c0,um(0)=u0,in Ω. (4.3)

    Observing that U is a closed convex subset of L2(0,T;H1(Ωc)). According to the definition of Sad, we deduce that there exists (ˉn,ˉc,ˉu,ˉf) bounded in H such that, for subsequence of (nm,cm,um,fm)mN, for convenience, still denoted by (nm,cm,um,fm), as m+

    nmˉn, weakly in L2(0,T;H2(Ω)) and weakly*  in L(0,T;H1(Ω)),cmˉc, weakly in L2(0,T;H3(Ω)) and weakly*  in L(0,T;H2(Ω)),umˉu, weakly in L2(0,T;H2(Ω)) and weakly*  in L(0,T;H1(Ω)),fmˉf, weakly in L2(0,T;H1(Ωc)), and ˜fU.

    According to the Aubin-Lions lemma [16] and the compact embedding theorems, we obtain

    nmˉn, strongly in C([0,T];L2(Ω))L2(0,T;H1(Ω)),cmˉc, strongly in C([0,T];H1(Ω))L2(0,T;H2(Ω)),umˉu, strongly in C([0,T];L2(Ω))L2(0,T;H1(Ω)).

    Since (nmcm)=nmcm+nmΔcm is bounded in L2(0,T;L2(Ω)), then

    (nmcm)χ, weakly in L2(0,T;L2(Ω)).

    Recalling that

    nmcmˉnˉc, weakly in L(0,T;L2(Ω)).

    Therefore, we get that χ=(ˉnˉc). Owing to (ˉn,ˉc,ˉu,ˉf)H, we see that (ˉn,ˉc,ˉu,ˉf) is solution of the system (1.1), along with (4.2) implies that

    limm+J(nm,cm,um,fm)=inf(u,c,u,f)SadJ(u,c,u,f)J(ˉn,ˉc,ˉu,ˉf).

    On the other hand, we deduce from the weak lower semicontinuity of the cost functional

    J(ˉn,ˉc,ˉu,ˉf)lim infm+J(nm,cm,um,fm).

    Therefore, this implies that (ˉn,ˉc,ˉu,ˉf) is an optimal pair for problem (1.1).

    In order to derive the first-order necessary optimality conditions for a local optimal solution of problem (4.1). To this end, we will use a result on existence of Lagrange multipliers in Banach spaces ([20]). First, we discuss the following problem

    minJ(s) subject to sS={sH:G(s)N}, (5.1)

    where J:XR is a functional, G:XY is an operator, X and Y are Banach spaces, and nonempty closed convex set H is subset of X and nonempty closed convex cone N with vertex at the origin in Y.

    A+ denotes its polar cone

    A+={ρX:ρ,aX0,aA}.

    We consider the following Banach spaces

    X=Vn×Vc×Vu×L2(0,T;H1(Ωc)),Y=L2(Q)×L2(0,T;H1(Ω))×L2(Q)×H1(Ω)×H2(Ω)×H1(Ω),

    where

    Vn={nYn:nν on (0,T)×Ω},Vc={nYc:cν on (0,T)×Ω},Vu={nYu:u=0 on (0,T)×Ω and u=0 in (0,T)×Ω}

    and the operator G=(G1,G2,G3,G4,G5,G6):XY, where

    G1:XL2(Q),G2:XL2(0,T;H1(Ω)),G3:XL2(Q),G4:XH1(Ω),G5:XH2(Ω),G6:XH1(Ω),

    which are defined at each point s=(n,c,u,f)X by

    {G1=nt+unΔn+(nc)γn+μn2,G2=ct+ucΔc+cnf,G3=ut+uuΔu+πnφ,G4=n(0)n0,G5=c(0)c0,G6=u(0)u0. (5.2)

    The function spaces are given as follows

    H=Vn×Vc×Vu×U.

    We see that H is a closed convex subset of X and N={0}, and rewrite the optimal control problem

    minJ(s) subject to sSad={sH:G(s)=0}. (5.3)

    Taking the differentiability of J and G into consider, it follows that

    Lemma 5.1. The functional J:XR is Fréchet differentiable and the Fréchet derivative of J in ˉs=(ˉn,ˉc,ˉu,ˉf)X in the direction r=(˜n,˜c,˜u,˜f) is given by

    J(ˉs)[r]=β1T0Ωd(ˉnnd)˜ndxdt+β2T0Ωd(ˉccd)˜cdxdt+β3T0Ωd(ˉuud)˜u(T)dxdt+β4Ωd(ˉn(T)nΩ)˜n(T)dx+β5Ωd(ˉc(T)cΩ)˜cdx+β6Ωd(ˉu(T)uΩ)˜u(T)dx+β7T0Ωdˉf˜fdxdt. (5.4)

    Lemma 5.2. The operator G:XY is continuous-Fréchet differentiable and the Fréchet derivative of J in ˉs=(ˉn,ˉc,ˉu,ˉf)X in the direction r=(˜n,˜c,˜u,˜f), is the linear operator

    G(ˉs)[r]=(G1(ˉs)[r],G2(ˉs)[r],G3(ˉs)[r],G4(ˉs)[r],G5(ˉs)[r],G6(ˉs)[r])

    defined by

    {G1(ˉs)[r]=˜ntΔ˜n+ˉu˜n+˜uˉn+(ˉn˜c)+(˜nˉc)γ˜n+2μ˜nˉn,inQ,G2(ˉs)[r]=˜ctΔ˜c+ˉu˜c+˜uˉc+˜c˜n˜f,inQ,G3(ˉs)[r]=˜utΔ˜u+ˉu˜u+˜uˉu˜nφ,inQ,˜u=0,inQ,˜nν=˜cν=0,˜u=0,on(0,T)×Ω,˜n(0)=˜n0,˜c(0)=˜c0,˜u(0)=˜u0,inΩ.

    Lemma 5.3. Let ˉs=(ˉn,ˉc,ˉu,ˉf)Sad, then ˉs is a regular point.

    Proof. For any fixed (ˉn,ˉc,ˉu,ˉf)Sad, we set (gn,gc,gu,˜n0,˜c0,˜u0)Y. Since 0C(ˉf), it suffices to show the existence of (˜n,˜c,˜u)Yn×Yc×Yu such that

    {˜ntΔ˜n+ˉu˜n+˜uˉn+(ˉn˜c)+(˜nˉc)γ˜n+2μ˜nˉn=gn,in Q,˜ctΔ˜c+ˉu˜c+˜uˉc+˜c˜n=gc,in Q,˜utΔ˜u+ˉu˜u+˜uˉu˜nφ=gu,in Q,˜u=0,in Q,˜nν=˜cν=0,˜u=0,on (0,T)×Ω,˜n(0)=˜n0,˜c(0)=˜c0,˜u(0)=˜u0,in Ω. (5.5)

    Next, we use Leray-Schauder's fixed point method to prove the existence of solutions of the problem (5.5), the operator T:(˙n,˙u)Xn×Xu(˜n,˜u)Yn×Yu with (˜n,˜c,˜u) solving the decoupled problem:

    {˜ntΔ˜n+ˉu˜n+˜uˉn+(ˉn˜c)+(˜nˉc)γ˜n+2μ˙nˉn=gn,in Q,˜ctΔ˜c+ˉu˜c+˜uˉc+˜c˙n=gc,in Q,˜utΔ˜u+ˉu˜u+˙uˉu˙nφ=gu,in Q. (5.6)

    The system (5.6) is complemented by the corresponding Neumann boundary and initial conditions. Similar to the proof of Lemmas 2.3, 2.4, 2.5 and 2.6, we conduct that operator T:Xn×XuXn×Xu is well-defined and compact.

    Similar to the proof of Theorem 3.1, (˜n,˜u) solves the coupled problem (ˉn,ˉc,ˉu,ˉf)Sad, and we set (gn,gc,gu,˜n0,˜c0,˜u0)Y. Since 0C(ˉf), it suffices to show the existence of (˜n,˜c,˜u)Yn×Yc×Yu such that

    {˜ntΔ˜n+˜n=ˉu˜n˜uˉn(ˉn˜c)(˜nˉc)+α(γ+1)˜n2μ˜nˉn+αgn,in Q,˜ctΔ˜c+˜c=ˉu˜c˜uˉc+α˜n+αgc,in Q,˜utΔ˜u=ˉu˜u˜uˉu+α˜nφ+αgu,in Q, (5.7)

    complemented by the corresponding Neumann boundary and initial conditions.

    Taking the L2-inner product with ˜u for the third equation of (5.7) implies

    12Ω˜u2dx+Ω|˜u|2dx=αΩ˜nφ˜udx+αΩ˜ugudx.

    By the Poincaré inequality and Young's inequality, we have

    ddt˜u2L2+˜u2H1C(˜n2L2+gu2L2)+C˜u2L2. (5.8)

    Taking the L2-inner product with ˜c for the second equation of (5.7) implies

    12Ω˜c2dx+Ω|˜c|2dx+Ω˜c2dx=Ω˜uˉc˜cdx+αΩ˜n˜cdx+αΩgc˜cdx.

    With the Poincaré inequality and Young's inequality in hand, we see that

    ddt˜c2L2+˜c2H1C(˜n2L2+gc2L2)+C˜c2L2. (5.9)

    Taking the L2-inner product with Δ˜c for the second equation of (5.7) implies

    12Ω|˜c|2dx+Ω|Δ˜c|2dx+Ω|˜c|2dx=Ω˜uˉcΔ˜cdx+Ωˉu˜cΔ˜cdxαΩ˜nΔ˜cdxαΩgcΔ˜cdx=J1+J2+J3.

    For the term J1

    J1=Ω˜uˉcΔ˜cdxΔ˜cL2ˉcL4˜uL416Δ˜c2L2+Cˉc2H1˜u2H1.

    For the term J2, we see that

    J2=Ωˉu˜cΔ˜cdx=12Ωˉu|˜c|2dxˉuL2˜c2L4ˉuL2(˜c12L2Δ˜c12L2+˜cL2)16Δ˜c2L2+C˜c2L2.

    For the term J3, we get

    J3=αΩ˜nΔ˜cdxαΩgcΔ˜cdx16Δ˜c2L2+C(˜n2L2+gc2L2).

    Therefore, combining J1, J2 and J3, we have

    ddt˜c2L2+˜c2H1C˜c2L2+C(˜n2L2+gc2L2). (5.10)

    Taking the L2-inner product with ˜n for the first equation of (5.7) implies

    ddtΩ˜n2dx+Ω|˜n|2dx+Ω˜n2dx=Ω˜uˉn˜ndx+Ω˜nˉn˜cdx+Ω˜n˜nˉcdx+α(γ+1)Ω˜n2dx+2μΩˉn˜n2dx+αΩ˜ngndx=J4+J5+J6+J7.

    For the term J4, by Gagliardo-Nirenberg interpolation inequality, we have

    J4=Ω˜uˉn˜ndx˜uL4ˉnL2˜nL4C(˜u12L2˜u12L2+˜uL2)ˉnL2˜nH1δ˜n2H1+C˜uL2˜uL2+C˜u2L2δ˜n2H1+δ˜u2L2+C˜u2L2.

    For the term J5,

    J5=Ω˜nˉn˜cdx˜nL2ˉnL4˜cL4˜nL2ˉnH1(˜c12L2Δ˜c12L2+˜cL2)δ˜n2L2+˜cL2Δ˜cL2+C˜c2L2δ˜n2L2+δΔ˜cL2+C˜c2L2.

    For the term J6,

    J6=Ω˜n˜nˉcdx˜n2L4ΔˉcL2(˜n12L2˜n12L2+˜nL2)ΔˉcL2δ˜n2L2+C˜n2L2+C.

    For the term J7,

    J7=α(γ+1)Ω˜n2dx+2μΩˉn˜n2dx+αΩ˜ngndx(γ+1)˜n2L2+gnL2˜nL2+ˉnL2˜n2L4(γ+1)˜n2L2+gnL2˜nL2+ˉnL2(˜n12L2˜n12L2+˜nL2)δ˜nL2+C˜n2L2+Cgn2L2.

    Therefore, by choosing δ small enough, from J4, J5, J6 and J7, it follows that

    ddt˜n2L2+˜n2H1C(˜n2L2+˜c2L2+˜u2L2)+δΔ˜cL2+δ˜u2L2+Cgn2L2. (5.11)

    By choosing δ small enough and combining (5.8)-(5.11), we get

    ddt(˜n2L2+˜c2H1+˜u2L2)+˜n2H1+˜c2H2+˜u2H1C(gn2L2+gc2L2+gu2L2)+C(˜n2L2+˜c2H1+˜u2L2).

    Applying Gronwall's lemma to the resulting differential inequality, we obatin

    ˜n2L2+˜c2H1+˜u2L2+t0˜n2H1dτ+t0˜c2H2dτ+t0˜u2H1dτC. (5.12)

    Taking the L2-inner product with Δ˜u for the third equation of (5.7) implies

    12ddtΩ|˜u|2dx+Ω|Δ˜u|2dx=Ωˉu˜uΔ˜udx+Ω˜uˉuΔ˜udxαΩ˜nφΔ˜udxαΩguΔ˜udx=J8+J9+J10.

    With the use of the Gagliardo-Nirenberg interpolation inequality, we derive

    J8=Ωˉu˜uΔ˜udxˉuL4˜uL4Δ˜uL2ˉuH1(˜u12L2Δ˜u12L2+˜uL2)Δ˜uL2δΔ˜u2L2+C˜u2L2

    and

    J9=Ω˜uˉuΔ˜udxΔ˜uL2ˉuL4˜uL4CΔ˜uL2ˉuH1(˜u12L2˜u12L2+˜uL2)δΔ˜u2L2+C˜u2L2.

    For the term J10, we deduce

    J10=αΩ˜nφΔ˜udxαΩguΔ˜udxδΔ˜u2L2+C(˜n2L2+gu2L2).

    By choosing δ small enough, with the estimates J8, J9 and J10, we have

    ddt˜u2L2+Δ˜u2L2C˜u2L2+Cgu2L2. (5.13)

    Applying to the first equation of (5.7), multiplying it by Δ˜c, and integrating over Ω give

    12ddtΩ|Δc|2dx+Ω|Δc|2dx+Ω|Δc|2dx=Ω(ˉu˜c)Δ˜cdxΩ(˜uˉc)Δ˜cdx+αΩ˜nΔ˜cdx+αΩgcΔ˜cdx=J11+J12+J13.

    For the first term J11, we have

    J11=Ω(ˉu˜c)Δ˜cdx=Ωˉu˜cΔ˜cdxΩˉuΔ˜cΔ˜cdxΔ˜cL2ˉuL4˜cL4+Δ˜cL2ˉuL4Δ˜cL4Δ˜cL2(ˉu12L2Δˉu12L2+ˉuL2)(ˉc12L2Δˉc12L2+ˉcL2)+Δ˜cL2ˉuH1(Δ˜c12L2Δ˜c12L2+Δ˜cL2)δΔ˜c2L2+CΔˉu2L2+CΔ˜c2L2.

    Similarly, for the term J12,

    J12=Ω(˜uˉc)Δ˜cdx=Ω˜uˉcΔ˜cdxΩ˜uΔˉcΔ˜cdxΔ˜cL2˜uL4ˉcL4+˜uL4ΔˉcL4Δ˜cL2CΔ˜cL2(˜u12L2Δ˜u12L2+˜uL2)ˉcH1+(˜u12L2˜u12L2+˜uL2)(Δˉc12L2Δˉc12L2+ΔˉcL2)Δ˜cL2δΔ˜c2L2+δΔ˜u2L2+CΔˉc2L2+C˜u2L2.

    For the rest term J13, we see

    J13=αΩ˜nΔ˜cdx+αΩgcΔ˜cdxδΔ˜c2L2+C(˜n2L2+gc2L2).

    By choosing δ small enough, we get

    ddtΔ˜c2L2+Δ˜c2H1C(˜n2L2+Δ˜c2L2+˜u2L2)+CΔˉu2L2+δΔ˜u2L2+CΔˉc2L2+Cgc2L2. (5.14)

    From (5.13) and (5.14), along with δ small enough, it follows that

    ddt(˜u2L2+Δ˜c2L2)+Δ˜u2L2+Δ˜c2H1C(˜u2L2+Δ˜c2L2)+(˜n2L2+Δˉu2L2+Δˉc2L2+gc2L2)+Cgu2L2.

    Applying Gronwall's lemma to the resulting differential inequality, we know

    ˜u2L2+Δ˜c2L2+t0Δ˜u2L2dτ+t0Δ˜c2H1dτC.

    Taking the L2-inner product with Δ˜n for the first equation of (5.7) implies

    12ddtΩ|˜n|2dx+Ω|Δ˜n|2dx+Ω|˜n|2dx=Ωˉu˜nΔ˜ndxΩ˜uˉnΔ˜ndxΩ(˜nˉc)Δ˜ndxΩ(ˉn˜c)Δ˜ndxα(1+γ)Ω˜nΔ˜ndx+2μΩ˜nˉnΔ˜ndxαΩgnΔ˜ndx=J14+J15+J16+J17+J18.

    With the Gagliardo-Nirenberg interpolation inequality in hand, we can estimate as follows

    Similar to above estimates, we see

    Similarly, we derive

    and

    For the rest terms, we know

    Therefore, Taking small enough and together with , we see that

    Applying Gronwall's lemma to the resulting differential inequality, we know

    Therefore, from Leray-Schauder theorem, we derive the existence of solution for (5.5). Along with the regularity of , the uniqueness of solution can easily get, so we omit the process.

    Theorem 5.1. Assume that be an optimal solution for the control problem (5.3). Then, there exist Lagrange multipliers such that for all has

    (5.15)

    where .

    Proof. With the Lemma 5.3 in hand, we get that is a regular point. Then, togather with Theorem 3.1 in [20], it follows that there exist Lagrange multipliers such that

    for all . Hence, the proof follows from Lemmas 5.1 and 5.2.

    Corollary 5.1. Assume that be an optimal solution for the control problem (5.3). Then, there exist Lagrange multipliers , satisfying

    (5.16)
    (5.17)
    (5.18)

    which corresponds to the linear system

    (5.19)

    subject to the following boundary and final conditions

    and the following identities hold

    (5.20)

    Proof. By taking in (5.15), then it follows that the equation (5.16) holds. In light of an analogous argument, and in light of the (5.15), it guarantees that (5.17) and (5.18) hold. On the other hand, let , as an immediate consequence we obtain

    By choosing for all , thus we achieve (5.20).

    Theorem 5.2. Under the assumptions of Theorem 5.1, system (5.19) has a unique weak solution such that

    Proof. For convenience, we set , , , in order to simplify notations, we still write , , instead of , , , then the adjoint system (5.19) can be written as follow

    (5.21)

    subject to the following boundary and final conditions

    Following an analogous reasoning as in the proof of Lemma 5.3, we omit the process and just give a number of a priori estimates as follows.

    Taking the -inner product with for the first equation of (5.21) implies

    Then, we have

    (5.22)

    Taking the -inner product with for the first equation of (5.21) implies

    Thus, we get

    (5.23)

    Taking the -inner product with for the second equation of (5.21) implies

    As an immediate consequence, we obtain

    (5.24)

    Taking the -inner product with for the third equation of (5.21) implies

    Therefore, we see that

    (5.25)

    Combining (5.22)-(5.25) and taking small enough, we have

    Applying Gronwall's lemma to the resulting differential inequality, we know

    The proof is complete.

    The authors would like to express their deep thanks to the referee's valuable suggestions for the revision and improvement of the manuscript.



    [1] A. Al Alam, A. Gattami, K. H. Johansson, An experimental study on the fuel reduction potential of heavy duty vehicle platooning, 13th international IEEE conference on intelligent transportation systems, IEEE, Funchal, Portugal, (2010), 306–311. https://doi.org/10.1109/ITSC.2010.5625054
    [2] J. Alam A. Martensson, K. H. Johansson, Experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning, Control Eng. Pract., 38 (2015), 11–25. https://doi.org/10.1016/j.conengprac.2014.12.009 doi: 10.1016/j.conengprac.2014.12.009
    [3] J. Alonso, V. Milanés, J. Pérez, E. Onieva, C. González, T. de Pedro, Autonomous vehicle control systems for safe crossroads, Transp. Res. Part C Emerg. Technol., 19 (2011), 1095–1110. https://doi.org/10.1016/j.trc.2011.06.002 doi: 10.1016/j.trc.2011.06.002
    [4] T. Ard, F. Ashtiani, A. Vahidi, H. Borhan, Optimizing gap tracking subject to dynamic losses via connected and anticipative mpc in truck platooning, American Control Conference (ACC), IEEE, Denver, CO, USA, (2020), 2300–2305. https://doi.org/10.23919/ACC45564.2020.9147849
    [5] M. Athans, A unified approach to the vehicle-merging problem, Transp. Res., 3 (1969), 123–133. https://doi.org/10.1016/0041-1647(69)90109-9 doi: 10.1016/0041-1647(69)90109-9
    [6] T. C. Au, P. Stone, Motion planning algorithms for autonomous intersection management, Bridging the gap between task and motion planning, AAAI press, (2010), 2–9. https://dl.acm.org/doi/abs/10.5555/2908515.2908516
    [7] H. Bang, B. Chalaki, A. A. Malikopoulos, Combined optimal routing and coordination of connected and automated vehicles, IEEE Control Syst. Lett., 6 (2022), 2749–2754. https://doi.org/10.1109/LCSYS.2022.3176594 doi: 10.1109/LCSYS.2022.3176594
    [8] L. E. Beaver, B. Chalaki, A. M. Mahbub, L. Zhao, R. Zayas, A. A. Malikopoulos, Demonstration of a time-efficient mobility system using a scaled smart city, Veh. Syst. Dyn., 58 (2020), 787–804. https://doi.org/10.1080/00423114.2020.1730412 doi: 10.1080/00423114.2020.1730412
    [9] L. E. Beaver, A. A. Malikopoulos, Constraint-driven optimal control of multi-agent systems: A highway platooning case study, IEEE Control Syst. Lett., 6 (2022), 1754–1759. https://doi.org/10.1109/LCSYS.2021.3133801 doi: 10.1109/LCSYS.2021.3133801
    [10] C. Bergenhem, S. Shladover, E. Coelingh, C. Englund, S. Tsugawa, Overview of platooning systems, Proceedings of the 19th ITS World Congress, Vienna, Austria, 2012.
    [11] B. Besselink, K. H. Johansson, String stability and a delay-based spacing policy for vehicle platoons subject to disturbances, IEEE Trans. Autom. Control, 62 (2017), 4376–4391. https://doi.org/10.1109/TAC.2017.2682421 doi: 10.1109/TAC.2017.2682421
    [12] A. K. Bhoopalam, N. Agatz, R. Zuidwijk, Planning of truck platoons: A literature review and directions for future research, Transp. Res. Part B Methodol., 107 (2018), 212–228. https://doi.org/10.1016/j.trb.2017.10.016 doi: 10.1016/j.trb.2017.10.016
    [13] A. E. Bryson, Y. C. Ho, Applied optimal control: optimization, estimation and control, CRC Press, 1975.
    [14] B. Chalaki, L. E. Beaver, A. M. I. Mahbub, H. Bang, A. A. Malikopoulos, A research and educational robotic testbed for real-time control of emerging mobility systems: From theory to scaled experiments, IEEE Control Syst. Mag., 42 (2022), 20–34. https://doi.org/10.1109/MCS.2022.3209056 doi: 10.1109/MCS.2022.3209056
    [15] B. Chalaki, L. E. Beaver, A. A. Malikopoulos, Experimental validation of a real-time optimal controller for coordination of cavs in a multi-lane roundabout, 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE, Las Vegas, NV, USA, (2020), 775–780. https://doi.org/10.1109/IV47402.2020.9304531
    [16] B. Chalaki, A. A. Malikopoulos, Time-optimal coordination for connected and automated vehicles at adjacent intersections, IEEE Trans. Intell. Transp. Syst., 23 (2022), 13330–13345. https://doi.org/10.1109/TITS.2021.3123479 doi: 10.1109/TITS.2021.3123479
    [17] B. Chalaki, A. A. Malikopoulos, Optimal control of connected and automated vehicles at multiple adjacent intersections, IEEE Trans. Control Syst. Technol., 30 (2022), 972–984. https://doi.org/10.1109/TCST.2021.3082306 doi: 10.1109/TCST.2021.3082306
    [18] B. Chalaki, A. A. Malikopoulos, A priority-aware replanning and resequencing framework for coordination of connected and automated vehicles, IEEE Control Syst. Lett., 6 (2022), 1772–1777. https://doi.org/10.1109/LCSYS.2021.3133416 doi: 10.1109/LCSYS.2021.3133416
    [19] B. Chalaki, A. A. Malikopoulos, Robust learning-based trajectory planning for emerging mobility systems, 2022 American Control Conference (ACC), IEEE, Atlanta, GA, USA, (2022), 2154–2159. https://doi.org/10.23919/ACC53348.2022.9867265
    [20] X. Chang, H. Li, J. Rong, X. Zhao, A. Li, Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles, Physica A, 557 (2020), 124829. https://doi.org/10.1016/j.physa.2020.124829 doi: 10.1016/j.physa.2020.124829
    [21] A. de La Fortelle, Analysis of reservation algorithms for cooperative planning at intersections, 13th International IEEE Conference on Intelligent Transportation Systems, IEEE, Funchal, Portugal, (2010), 445–449. https://doi.org/10.1109/ITSC.2010.5624978
    [22] K. Dresner, P. Stone, Multiagent traffic management: A reservation-based intersection control mechanism, in Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagents Systems, IEEE Computer Society, (2004), 530–537. https://dl.acm.org/doi/10.5555/1018410.1018799
    [23] K. Dresner, P. Stone, A multiagent approach to autonomous intersection management, J. Artif. Intell. Res., 31 (2008), 591–656. https://doi.org/10.1613/jair.2502 doi: 10.1613/jair.2502
    [24] D. J. Fagnant, K. M. Kockelman, The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios, Transp. Res. Part C Emerg. Technol., 40 (2014), 1–13. https://doi.org/10.1016/j.trc.2013.12.001 doi: 10.1016/j.trc.2013.12.001
    [25] M. Fellendorf, P. Vortisch, Microscopic traffic flow simulator vissim, Fundamentals of Traffic Simulation, International Series in Operations Research and Management Science, Springer, New York, NY, 145 (2010), 63–93.
    [26] S. Feng, Y. Zhang, S. E. Li, Z. Cao, H. X. Liu, L. Li, String stability for vehicular platoon control: Definitions and analysis methods, Annu. Rev. Control, 47 (2019), 81–97. https://doi.org/10.1016/j.arcontrol.2019.03.001 doi: 10.1016/j.arcontrol.2019.03.001
    [27] A. Ferrara, S. Sacone, S. Siri, Freeway Traffic Modeling and Control, Springer, Berlin, 2018. https://doi.org/10.1007/978-3-319-75961-6
    [28] J. Guanetti, Y. Kim, F. Borrelli, Control of connected and automated vehicles: State of the art and future challenges, Annu. Rev. Control, 45 (2018), 18–40. https://doi.org/10.1016/j.arcontrol.2018.04.011 doi: 10.1016/j.arcontrol.2018.04.011
    [29] S. V. D. Hoef, J. Mårtensson, D. V. Dimarogonas, K. H. Johansson, A predictive framework for dynamic heavy-duty vehicle platoon coordination, ACM Trans. Cyber-Phys. Syst., 4 (2019), 1–25. https://doi.org/10.1145/3299110 doi: 10.1145/3299110
    [30] S. Huang, A. Sadek, Y. Zhao, Assessing the mobility and environmental benefits of reservation-based intelligent intersections using an integrated simulator, IEEE Trans. Intell. Transp. Syst., 13 (2012), 1201–1214. https://doi.org/10.1109/TITS.2012.2186442 doi: 10.1109/TITS.2012.2186442
    [31] A. Johansson, E. Nekouei, K. H. Johansson, J. Mårtensson, Multi-fleet platoon matching: A game-theoretic approach, 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, Maui, HI, USA, 2018, 2980–2985. https://doi.org/10.1109/ITSC.2018.8569379
    [32] M. Kamal, M. Mukai, J. Murata, T. Kawabe, Model predictive control of vehicles on urban roads for improved fuel economy, IEEE Trans. Control Syst. Technol., 21 (2013), 831–841. https://doi.org/10.1109/TCST.2012.2198478 doi: 10.1109/TCST.2012.2198478
    [33] S. Karbalaieali, O. A. Osman, S. Ishak, A dynamic adaptive algorithm for merging into platoons in connected automated environments, IEEE Trans. Intell. Transp. Syst., 21 (2019), 4111–4122. https://doi.org/10.1109/TITS.2019.2938728 doi: 10.1109/TITS.2019.2938728
    [34] P. Kavathekar, Y. Chen, Vehicle platooning: A brief survey and categorization, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington, DC, USA, (2011), 829–845. https://doi.org/10.1115/DETC2011-47861
    [35] V. L. Knoop, H. J. Van Zuylen, S. P. Hoogendoorn, Microscopic traffic behaviour near accidents, Transportation and Traffic Theory 2009: Golden Jubilee, Springer, Boston, MA, 2009.
    [36] S. Kumaravel, A. A. Malikopoulos, R. Ayyagari, Decentralized cooperative merging of platoons of connected and automated vehicles at highway on-ramps, in 2021 American Control Conference (ACC), IEEE, New Orleans, LA, USA, (2021), 2055–2060. https://doi.org/10.23919/ACC50511.2021.9483390
    [37] S. Kumaravel, A. A. Malikopoulos, R. Ayyagari, Optimal coordination of platoons of connected and automated vehicles at signal-free intersections, IEEE Trans. Intell. Veh., 7 (2022), 186–197. https://doi.org/10.1109/TIV.2021.3096993 doi: 10.1109/TIV.2021.3096993
    [38] J. Larson, K. Y. Liang, K. H. Johansson, A distributed framework for coordinated heavy-duty vehicle platooning, IEEE Trans. Intell. Transp. Syst., 16 (2015), 419–429. https://doi.org/10.1109/TITS.2014.2320133 doi: 10.1109/TITS.2014.2320133
    [39] W. Levine, M. Athans, On the optimal error regulation of a string of moving vehicles, IEEE Trans. Autom. Control, 11 (1966), 355–361. https://doi.org/10.1109/TAC.1966.1098376 doi: 10.1109/TAC.1966.1098376
    [40] J. Lioris, R. Pedarsani, F. Y. Tascikaraoglu, P. Varaiya, Platoons of connected vehicles can double throughput in urban roads, Transp. Res. Part C Emerging Technol., 77 (2017), 292–305. https://doi.org/10.1016/j.trc.2017.01.023 doi: 10.1016/j.trc.2017.01.023
    [41] A. M. I. Mahbub, V. Karri, D. Parikh, S. Jade, A. A. Malikopoulos, A decentralized time- and energy-optimal control framework for connected automated vehicles: From simulation to field test, arXiv preprint, 2020. https://doi.org/10.48550/arXiv.1911.01380
    [42] A. M. I. Mahbub, V. A. Le, A. A. Malikopoulos, A safety-prioritized receding horizon control framework for platoon formation in a mixed traffic environment, arXiv preprint. https://doi.org/10.48550/arXiv.2205.10673
    [43] A. M. I. Mahbub, V. A. Le, A. A. Malikopoulos, Safety-aware and data-driven predictive control for connected automated vehicles at a mixed traffic signalized intersection, IFAC-PapersOnLine, 24 (2022), 51–56. https://doi.org/10.1016/j.ifacol.2022.10.261 doi: 10.1016/j.ifacol.2022.10.261
    [44] A. M. I. Mahbub, A. A. Malikopoulos, Concurrent optimization of vehicle dynamics and powertrain operation using connectivity and automation, arXiv preprint, 2019. https://doi.org/10.48550/arXiv.1911.03475
    [45] A. M. I. Mahbub, A. A. Malikopoulos, Conditions for state and control constraint activation in coordination of connected and automated vehicles, 2020 American Control Conference (ACC), IEEE, Denver, CO, USA, (2020), 436–441. https://doi.org/10.23919/ACC45564.2020.9147842
    [46] A. M. I. Mahbub, A. A. Malikopoulos, A platoon formation framework in a mixed traffic environment, IEEE Control Syst. Lett., 6 (2021), 1370–1375. https://doi.org/10.1109/LCSYS.2021.3092188 doi: 10.1109/LCSYS.2021.3092188
    [47] A. M. I. Mahbub, A. A. Malikopoulos, Conditions to provable system-wide optimal coordination of connected and automated vehicles, Automatica, 131 (2021), 109751. https://doi.org/10.1016/j.automatica.2021.109751 doi: 10.1016/j.automatica.2021.109751
    [48] A. M. I. Mahbub, A. A. Malikopoulos, Platoon formation in a mixed traffic environment: A model-agnostic optimal control approach, 2022 American Control Conference (ACC), IEEE, Atlanta, GA, USA, (2022), 4746–4751. https://doi.org/10.23919/ACC53348.2022.9867168
    [49] A. M. I. Mahbub, L. Zhao, D. Assanis, A. A. Malikopoulos, Energy-optimal coordination of connected and automated vehicles at multiple intersections, 2019 American Control Conference (ACC), IEEE, Philadelphia, PA, USA, (2019), 2664–2669. https://doi.org/10.23919/ACC.2019.8814877
    [50] A. I. Mahbub, A. A. Malikopoulos, L. Zhao, Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios, Automatica, 117 (2020), 108958. https://doi.org/10.1016/j.automatica.2020.108958 doi: 10.1016/j.automatica.2020.108958
    [51] A. A. Malikopoulos, A duality framework for stochastic optimal control of complex systems, IEEE Trans. Autom. Control, 18 (2016), 780–789. https://doi.org/10.1109/TAC.2015.2504518 doi: 10.1109/TAC.2015.2504518
    [52] A. A. Malikopoulos, L. E. Beaver, I. V. Chremos, Optimal time trajectory and coordination for connected and automated vehicles, Automatica, 125 (2021), 109469. https://doi.org/10.1016/j.automatica.2020.109469 doi: 10.1016/j.automatica.2020.109469
    [53] A. A. Malikopoulos, C. G. Cassandras, Y. J. Zhang, A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections, Automatica, 93 (2018), 244–256. https://doi.org/10.1016/j.automatica.2018.03.056 doi: 10.1016/j.automatica.2018.03.056
    [54] A. A. Malikopoulos, L. Zhao, A closed-form analytical solution for optimal coordination of connected and automated vehicles, 2019 American Control Conference (ACC), IEEE, Philadelphia, PA, USA, (2019), 3599–3604. https://doi.org/10.23919/ACC.2019.8814759
    [55] A. A. Malikopoulos, L. Zhao, Optimal path planning for connected and automated vehicles at urban intersections, 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, Nice, France, (2019), 1261–1266. https://doi.org/10.1109/CDC40024.2019.9030093
    [56] R. Margiotta, D. Snyder, An agency guide on how to establish localized congestion mitigation programs, Technical report, U.S. Department of Transportation, Federal Highway Administration, 2011.
    [57] F. Morbidi, P. Colaneri, T. Stanger, Decentralized optimal control of a car platoon with guaranteed string stability, 2013 European Control Conference (ECC), IEEE, Zurich, Switzerland, (2013), 3494–3499. https://doi.org/10.23919/ECC.2013.6669336
    [58] G. J. Naus, R. P. Vugts, J. Ploeg, M. J. van De Molengraft, M. Steinbuch, String-stable cacc design and experimental validation: A frequency-domain approach, IEEE Trans. Veh. Technol., 59 (2010), 4268–4279. https://doi.org/10.1109/TVT.2010.2076320 doi: 10.1109/TVT.2010.2076320
    [59] I. A. Ntousakis, I. K. Nikolos, M. Papageorgiou, Optimal vehicle trajectory planning in the context of cooperative merging on highways, Transp. Res. Part C Emerging Technol., 71 (2016), 464–488. https://doi.org/10.1016/j.trc.2016.08.007 doi: 10.1016/j.trc.2016.08.007
    [60] M. Papageorgiou, A. Kotsialos, Freeway ramp metering: An overview, IEEE Trans. Intell. Transp. Syst., 3 (2002), 271–281. https://doi.org/10.1109/TITS.2002.806803 doi: 10.1109/TITS.2002.806803
    [61] H. Pei, S. Feng, Y. Zhang, D. Yao, A cooperative driving strategy for merging at on-ramps based on dynamic programming, IEEE Trans. Veh. Technol., 68 (2019), 11646–11656. https://doi.org/10.1109/TVT.2019.2947192 doi: 10.1109/TVT.2019.2947192
    [62] N. Pourmohammad Zia, F. Schulte, R. R. Negenborn, Platform-based platooning to connect two autonomous vehicle areas, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, Rhodes, Greece, (2020), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294689
    [63] R. Rajamani, H. S. Tan, B. K. Law, W. B. Zhang, Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons, IEEE Trans. Control Syst. Technol., 8 (2000), 695–708. https://doi.org/10.1109/87.852914 doi: 10.1109/87.852914
    [64] J. Rios-Torres, A. A. Malikopoulos, A survey on coordination of connected and automated vehicles at intersections and merging at highway on-ramps, IEEE Trans. Intell. Transp. Syst., 18 (2017), 1066–1077. https://doi.org/10.1109/TITS.2016.2600504 doi: 10.1109/TITS.2016.2600504
    [65] J. Rios-Torres, A. A. Malikopoulos, Automated and cooperative vehicle merging at highway on-ramps, IEEE Trans. Intell. Transp. Syst., 18 (2017), 780–789. https://doi.org/10.1109/TITS.2016.2587582 doi: 10.1109/TITS.2016.2587582
    [66] B. Schrank, B. Eisele, T. Lomax, 2019 Urban Mobility Scorecard, Technical report, Texas A and M Transportation Institute, 2019.
    [67] M. Shida, T. Doi, Y. Nemoto, K. Tadakuma, A short-distance vehicle platooning system: 2nd report, evaluation of fuel savings by the developed cooperative control, in Proceedings of the 10th International Symposium on Advanced Vehicle Control (AVEC), KTH Royal Institute of Technology Loughborough, United Kingdom, (2010), 719–723.
    [68] S. E. Shladover, C. A. Desoer, J. K. Hedrick, M. Tomizuka, J. Walrand, W. B. Zhang, et al., Automated vehicle control developments in the PATH program, IEEE Trans. Veh. Technol., 40 (1991), 114–130. https://doi.org/10.1109/25.69979 doi: 10.1109/25.69979
    [69] S. Singh, Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey. (Traffic Safety Facts Crash Stats.), Technical Report, 2018.
    [70] K. Spieser, K. Treleaven, R. Zhang, E. Frazzoli, D. Morton, M. Pavone, Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in singapore, Road vehicle automation, Springer, Cham, (2014), 229–245.
    [71] S. S. Stanković, M. J. Stanojević, D. D. Šiljak, Decentralized suboptimal lqg control of platoon of vehicles, Proc. 8th IFAC/IFIP/IFORS Symp. Transp. Syst., 1 (1997) 83–88.
    [72] C. Tang, Y. Li, Consensus-based platoon control for non-lane-discipline connected autonomous vehicles considering time delays, 2018 37th Chinese Control Conference (CCC), IEEE, Wuhan, (2018), 7713–7718. https://doi.org/10.23919/ChiCC.2018.8484016
    [73] S. Tsugawa, An overview on an automated truck platoon within the energy its project, IFAC Proc. Volumes, 46 (2013), 41–46. https://doi.org/10.3182/20130904-4-JP-2042.00110 doi: 10.3182/20130904-4-JP-2042.00110
    [74] A. Tuchner, J. Haddad, Vehicle platoon formation using interpolating control, IFAC-PapersOnLine, 48 (2015), 414–419. https://doi.org/10.1016/j.ifacol.2015.09.492 doi: 10.1016/j.ifacol.2015.09.492
    [75] A. Valencia, A. M. I. Mahbub, A. A. Malikopoulos, Performance analysis of optimally coordinated connected automated vehicles in a mixed traffic environment, 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, Vouliagmeni, Greece, (2022), 1053–1058. https://doi.org/10.1109/MED54222.2022.9837281
    [76] S. Van De Hoef, K. H. Johansson, D. V. Dimarogonas, Fuel-efficient en route formation of truck platoons, IEEE Trans. Intell. Transp. Syst., 19 (2017), 102–112. https://doi.org/10.1109/TITS.2017.2700021 doi: 10.1109/TITS.2017.2700021
    [77] P. Varaiya, Smart cars on smart roads: Problems of control, IEEE Trans. Autom. Control, 38 (1993), 195–207. https://doi.org/10.1109/9.250509 doi: 10.1109/9.250509
    [78] Z. Wadud, D. MacKenzie, P. Leiby, Help or hindrance? the travel, energy and carbon impacts of highly automated vehicles, Transp. Res. Part A Policy Pract., 86 (2016), 1–18. https://doi.org/10.1016/j.tra.2015.12.001 doi: 10.1016/j.tra.2015.12.001
    [79] Z. Wang, G. Wu, P. Hao, K. Boriboonsomsin, M. Barth, Developing a platoon-wide eco-cooperative adaptive cruise control (cacc) system, 2017 ieee intelligent vehicles symposium (iv), IEEE, Los Angeles, CA, USA, (2017), 1256–1261. https://doi.org/10.1109/IVS.2017.7995884
    [80] R. Wiedemann, Simulation des Strassenverkehrsflusses, PhD thesis, Universität Karlsruhe, Karlsruhe, 1974.
    [81] W. Xiao, C. G. Cassandras, Decentralized optimal merging control for connected and automated vehicles with safety constraint guarantees, Automatica, 123 (2021), 109333. https://doi.org/10.1016/j.automatica.2020.109333 doi: 10.1016/j.automatica.2020.109333
    [82] X. Xiong, E. Xiao, L. Jin, Analysis of a stochastic model for coordinated platooning of heavy-duty vehicles, 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, Nice, France, (2019), 3170–3175. https://doi.org/10.1109/CDC40024.2019.9029179
    [83] F. Xu, T. Shen, Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles, IEEE Trans. Intell. Transp. Syst.. https://doi.org/10.1109/TITS.2021.3054903
    [84] L. Xu, W. Zhuang, G. Yin, C. Bian, H. Wu, Modeling and robust control of heterogeneous vehicle platoons on curved roads subject to disturbances and delays, IEEE Trans. Veh. Technol., 68 (2019), 11551–11564. https://doi.org/10.1109/TVT.2019.2941396 doi: 10.1109/TVT.2019.2941396
    [85] S. Yao, B. Friedrich, Managing connected and automated vehicles in mixed traffic by human-leading platooning strategy: A simulation study, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, Auckland, New Zealand, (2019), 3224–3229. https://doi.org/10.1109/ITSC.2019.8917007
    [86] L. Zhang, F. Chen, X. Ma, X. Pan, Fuel economy in truck platooning: A literature overview and directions for future research, J. Adv. Transp., 2020 (2020). https://doi.org/10.1155/2020/2604012 doi: 10.1155/2020/2604012
    [87] Y. Zhang, C. G. Cassandras, Decentralized optimal control of connected automated vehicles at signal-free intersections including comfort-constrained turns and safety guarantees, Automatica, 109 (2019), 108563. https://doi.org/10.1016/j.automatica.2019.108563 doi: 10.1016/j.automatica.2019.108563
  • This article has been cited by:

    1. Shenxing Li, Wenhe Li, Dynamical Behaviors of a Stochastic Susceptible-Infected-Treated-Recovered-Susceptible Cholera Model with Ornstein-Uhlenbeck Process, 2024, 12, 2227-7390, 2163, 10.3390/math12142163
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2069) PDF downloads(76) Cited by(0)

Figures and Tables

Figures(5)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog