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Research and implementation of variable-domain fuzzy PID intelligent control method based on Q-Learning for self-driving in complex scenarios


  • In the control of the self-driving vehicles, PID controllers are widely used due to their simple structure and good stability. However, in complex self-driving scenarios such as curvature curves, car following, overtaking, etc., it is necessary to ensure the stable control accuracy of the vehicles. Some researchers used fuzzy PID to dynamically change the parameters of PID to ensure that the vehicle control remains in a stable state. It is difficult to ensure the control effect of the fuzzy controller when the size of the domain is not selected properly. This paper designs a variable-domain fuzzy PID intelligent control method based on Q-Learning to make the system robust and adaptable, which is dynamically changed the size of the domain to further ensure the control effect of the vehicle. The variable-domain fuzzy PID algorithm based on Q-Learning takes the error and the error rate of change as input and uses the Q-Learning method to learn the scaling factor online so as to achieve online PID parameters adjustment. The proposed method is verified on the Panosim simulation platform.The experiment shows that the accuracy is improved by 15% compared with the traditional fuzzy PID, which reflects the effectiveness of the algorithm.

    Citation: Yongqiang Yao, Nan Ma, Cheng Wang, Zhixuan Wu, Cheng Xu, Jin Zhang. Research and implementation of variable-domain fuzzy PID intelligent control method based on Q-Learning for self-driving in complex scenarios[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 6016-6029. doi: 10.3934/mbe.2023260

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  • In the control of the self-driving vehicles, PID controllers are widely used due to their simple structure and good stability. However, in complex self-driving scenarios such as curvature curves, car following, overtaking, etc., it is necessary to ensure the stable control accuracy of the vehicles. Some researchers used fuzzy PID to dynamically change the parameters of PID to ensure that the vehicle control remains in a stable state. It is difficult to ensure the control effect of the fuzzy controller when the size of the domain is not selected properly. This paper designs a variable-domain fuzzy PID intelligent control method based on Q-Learning to make the system robust and adaptable, which is dynamically changed the size of the domain to further ensure the control effect of the vehicle. The variable-domain fuzzy PID algorithm based on Q-Learning takes the error and the error rate of change as input and uses the Q-Learning method to learn the scaling factor online so as to achieve online PID parameters adjustment. The proposed method is verified on the Panosim simulation platform.The experiment shows that the accuracy is improved by 15% compared with the traditional fuzzy PID, which reflects the effectiveness of the algorithm.



    This paper is devoted to the existence of weak solutions to the Cauchy problem for the two-component Novikov equation [18]

    {mt+uvmx+(2vux+uvx)m=0,m=uuxx,t>0,nt+uvnx+(2uvx+vux)n=0,n=vvxx. (1)

    Note that this system reduces respectively to the Novikov equation [23]

    mt+3uuxm+u2mx=0, (2)

    when v=u, and the celebrated Camassa-Holm (CH) equation [1]

    mt+2uxm+umx=0, (3)

    when v=1.

    The CH equation was proposed as a nonlinear model describing the unidirectional propagation of the shallow water waves over a flat bottom [1]. Based on the Hamiltonian theory of integrable systems, it was found earlier by using the method of recursion operator due to Fuchssteiner and Fokas [10]. It can also be obtained by using the tri-Hamiltonian duality approach related to the bi-Hamiltonian representation of the Korteweg-de Vries (KdV) equation [9,25]. The CH equation exhibits several remarkable properties. One is the the existence of the multi-peaked solitons on the line R and unit circle S1 [1,2], where the peaked solitons are the weak solution in the sense of distribution. Second, it can describes wave breaking phenomena [4], which is different from the classical integrable systems. The existence of H1-conservation law to the CH equation enables ones to define the H1-weak solution [28]. There have been a number of results concerning about integrability, well-posedness, blow up and wave breaking, orbital stability in the energy space and geometric formulations etc, see for instance [4,5,6,8,28] and references therein.

    The Novikov equation (2) can be viewed as a cubic generalization of the CH equation, which was introduced by Novikov [23,24] in the classification for a class of equations while they possesses higher-order generalized symmetries. Eq. (2) was proved to be integrable since it enjoys Lax-pair and bi-Hamiltonian structure [14], and is equivalent to the first equation in the negative flow of the Sawada-Kotera hierarchy via Liouville transformation [16]. The Novikov equation (2) also admits peaked solitons over the line R and unit circle S1 [14,20], which can be derived by the inverse spectral method. Orbital stability of peaked solitons over the line R and unit circle S1 of (2) in the energy space were verified [20] based on the conservation laws and the structure of peaked solitons of the Novikov equation (2). The well-posedness and wave breaking of the Novikov equation have been discussed in a number of papers, and it reveals that the Cauchy problem of the Novikov equation (2) has global strong solutions when the initial data u0Hs, s>3/2 [3,15,26,27]. The existence of global weak solutions to the Cauchy problem of the Novikov equation (2) was also discussed in [17].

    As the two-component generalization of Novikov equation (2), the so-called Geng-Xue system [11]

    mt+3vuxm+uvmx=0,nt+3uvxn+uvnx=0, (4)

    has been studied extensively [11,13]. The integrability [11,19], dynamics and structure of the peaked solitons of (4) [21] were discussed. In [13], well-posedness and wave breaking phenomena of the Cauchy problem of (4) were discussed. The single peakons and multi-peakons of system (4) were constructed in [21] by using compatibility of Lax-pair, which are not the weak solutions in the sense of distribution. Furthermore, the Geng-Xue system does not have the H1-conserved density, this is different from the CH and Novikov equations. The weak solution in H1 is not well-defined since it does not obey the H1-conservation law.

    The main object in this work is to investigate the existence of weak solutions to system (1). It is of great interest to understand the effect from interactions among the two-components, nonlinear dispersion and various nonlinear terms. More specifically, we shall consider the Cauchy problem of (1) and aim to leverage ideas from previous works on CH and Novikov equations. The weak solution of the Cauchy problem associated with (1) is established in Theorem 3.1.

    The remainder of this paper is organized as follows. In the next section 2, we review some basic results and lemmas as well as invariant properties of momentum densities m and n. In Section 3, we establish the existence of weak solutions, our approach is the regular approximation method together with some a priori estimates.

    In this section, we recall the local well-posedness, some properties of strong and weak solutions to equation (1) and several approximation results.

    First, we introduce some notations. Throughout the paper, we denote the convolution by . Let X denote the norm of Banach space X and let , denote the duality paring between H1(R) and H1(R). Let M(R) be the space of Radon measures on R with bounded total variation and M+(R) be the subset of positive Radon measures. Moreover, we write BV(R) for the space of functions with bounded variation, V(f) being the total variation of fBV(R). Furthermore, for 0<p<, s0, let Lp and s denote the norm of Lp(R) space and Hs(R) space, respectively.

    With m=uuxx and n=vvxx, the Cauchy problem of equation (1) takes the form:

    {mt+uvmx+(2vux+uvx)m=0,m=uuxx,t>0,xR,nt+uvnx+(2uvx+vux)n=0,n=vvxx,u(0,x)=u0(x),v(0,x)=v0(x),xR. (5)

    Note that if P(x)=12e|x|, xR, we have (12x)1f=Pf for all the fL2(R) and Pm=u, Pn=v. Then we can rewrite the equation (5) as follows:

    {ut+uvux+Px(12u2xv+uuxvx+u2v)+12P(u2xvx)=0,t>0,xR,vt+uvvx+Px(12v2xu+vvxux+v2u)+12P(v2xux)=0,u(0,x)=u0(x),v(0,x)=v0(x),xR. (6)

    Next we recall the local well-posedness and the conservation laws.

    Lemma 2.1. [12] Let u0,v0Hs(R), s3. Assume that T=T(u0,v0)>0 be the maximal existence time of the corresponding strong solution (u,v). Then the initial value problem of system (1) possesses a strong solution

    u,vC([0,T);Hs(R))C1([0,T);Hs1(R))

    Moreover, the solution depends continuously on the initial data, i.e. the mapping (u0,v0)(u(,u0),v(,v0)):Hs(R)×Hs(R)C([0,T);Hs(R))C1([0,T);Hs1(R))×C([0,T);Hs(R))C1([0,T);Hs1(R)) is continuous.

    Lemma 2.2. [12] Let u0,v0Hs(R), s3, and let (u(t,x),v(t,x)) be the corresponding solution to equation (1) with the initial data (u0,v0). Then we have

    R(u2(t,x)+u2x(t,x))dx=R(u20+u20x)dx,R(v2(t,x)+v2x(t,x))dx=R(v20+v20x)dx,R(u(t,x)v(t,x)+ux(t,x)vx(t,x))dx=R(u0v0+u0xv0x)dx.

    Moreover, we have

    |u(t,x)|22u01,|v(t,x)|22v01.

    Note that equation (1) has the solitary waves with corner at their peaks. Obviously, such solitons are not strong solutions to equation (6). In order to provide a mathematical framework for the study of these solitons, we define the notion of weak solutions to equation (6). Let

    Fu(u,v)=uvux+Px(12u2xv+uuxvx+u2v)+12P(u2xvx),Fv(u,v)=uvvx+Px(12v2xu+vvxux+v2u)+12P(v2xux).

    Then equation (6) can be written as

    {ut+Fu(u,v)=0,vt+Fv(u,v)=0,u(0,x)=u0(x),v(0,x)=v0(x). (7)

    Lemma 2.3. [22] Let T>0. If

    f,gL2((0,T);H1(R))anddfdt,dgdtL2((0,T);H1(R)),

    then f,g are a.e. equal to functions continuous from [0,T] into L2(R) and

    f(t),g(t)f(s),g(s)=tsdf(τ)dτ,g(τ)dτ+tsdg(τ)dτ,f(τ)dτ

    for all s,t[0,T].

    Throughout this paper, let {ρn}n1 denote the mollifiers

    ρn=(Rρ(ξ)dξ)1nρ(nx),xR,n1,

    where ρCc(R) is defined by

    ρ(x)={e1x21,for|x|<1,0,for|x|1.

    Next, we recall two crucial approximation results and two identities.

    Lemma 2.4. [7] Let f:RR be uniformly continuous and bounded. If μM(R), then

    [ρn(fμ)(ρnf)(ρnμ)]0,asninL1(R).

    Lemma 2.5. [7] Let f:RR be uniformly continuous and bounded. If gL(R), then

    ρn(fg)(ρnf)(ρng)0,asninL(R).

    Lemma 2.6. [7] Assume that u(t,)W1,1(R) is uniformly bounded in W1,1(R) for all tR+. Then for a.e. tR+, there hold

    ddtR|ρnu|dx=R(ρnut)sgn(ρnu)dx

    and

    ddtR|ρnux|dx=R(ρnuxt)sgn(ρnux)dx.

    Consider the flow governed by (uv)(t,x):

    {dq(t,x)dt=(uv)(t,q),t>0,xR,q(0,x)=x,xR. (8)

    Applying classical results in the theory of ODEs, one can obtain the following useful result on the above initial value problem.

    Lemma 2.7. [12] Let u0,v0Hs(R), s3, and T>0 be the life-span of the corresponding strong solution (u,v) to equation (5) with the initial data (u0,v0). Then equation (8) has a unique solution qC1([0,T)×R,R). Moreover, the map q(t,) is an increasing diffeomorphism over R with

    qx=exp(t0(uv)x(s,q(s,x))ds),(t,x)[0,T)×R.

    Furthermore, setting m=uuxx and n=vvxx, we obtain

    m(t,q)=exp(t0(2vux+uvx)(s,q(s,x))ds)m0,n(t,q)=exp(t0(2uvx+vux)(s,q(s,x))ds)n0,(t,x)[0,T)×R.

    Theorem 2.8. Let u0,v0Hs(R), s3. Assume that m0=u02xu0 and n0=v02xv0 are nonnegative, and T>0 be the maximal existence time of the corresponding strong solution (u,v). Then the initial value problem of system (1) possesses a pair of unique strong solution (u,v), where

    u,vC([0,T);Hs(R))C1([0,T);Hs1(R)).

    Set m(t,)=u(t,)uxx(t,) and n(t,)=v(t,)vxx(t,). Then, Eu(u)=R(u2+u2x)dx, Ev(v)=R(v2+v2x)dx, H(u,v)=R(uv+uxvx)dx and E0(u,v)=R(mn)13dx are four conservation laws and we have for all tR+

    (i).m(t,)0,n(t,)0,u(t,)0,v(t,)0and|ux(t,)|u(t,),|vx(t,)|v(t,)onR;(ii).u(t,)L1m(t,)L1,u(t,)L22u(t,)1=22u01,andv(t,)L1n(t,)L1,v(t,)L22v(t,)1=22v01;(iii).ux(t,)L1m(t,)L1andvx(t,)L1n(t,)L1.

    Moreover, if m0,n0L1(R), we obtain

    m(t,)L1eu01v01tm0L1andn(t,)L1eu01v01tn0L1.

    Proof. Let u0,v0Hs(R), s3, and let T>0 be the maximal existence time of the solution (u,v) to equation (5) with the initial data (u0,v0). If m00 and n00, then Lemma 2.7 ensures that m(t,)0 and n(t,)0 for all t[0,). By u=Pm, v=Pn and the positivity of P, we infer that u(t,)0 and v(t,)0 for all t0. Note that v is analogous as u and

    u(t,x)=ex2xeym(t,y)dy+ex2xeym(t,y)dy, (9)

    and

    ux(t,x)=ex2xeym(t,y)dy+ex2xeym(t,y)dy. (10)

    From the above two relations and m0, we deduce that

    |ux(t,x)|u(t,x)22u(t,x)1.

    In view of Lemma 2.2, we obtain that Eu(u) and Ev(v) are conserved and

    u(t,x)22u01,(t,x)R+×R.

    Since m(t,x)=uuxx, it follows that u=Pm and ux=Pxm. Note that PL1=PxL1=1. Applying Young's inequality, one can easily obtain (i)(iii). Since equation (1) can be used to derive the following form

    ((mn)13)t+((mn)13uv)x=0,

    it immediately follows that E0(u,v) is a conserved density. On the other hand, by equation (5), we have

    ddtRm(t,x)dx=(uvmx+(2vux+uvx)m)dx=(vuxm(uvm)x)dxuLvLm(t,x)dxu01v01m(t,x)dx.

    Since m0L1(R), in view of Gronwall's inequality, we can get

    m(t,)L1eu01v01tm0L1.

    Similarly, we find

    n(t,)L1eu01v01tn0L1.

    This completes the proof of Theorem 2.8.

    In this section, we will prove that there exists a unique global weak solution to equation (6), provided the initial data (u0,v0) satisfy certain sign-invariant conditions.

    Theorem 3.1. Let u0,v0H1(R). Assume m0=u0u0xxandn0=v0v0xxM+(R).Then equation (6) has a pair of unique weak solution (u,v), where

    u,vW1,(Rx×R)L(R+;H1(R))

    with the initial data u(0,x)=u0,v(0,x)=v0 and such that m=uuxx,n=vvxxM+(R) are bounded on [0,T), for any fixed T>0. Moreover, Eu(u)=R(u2+u2x)dx, Ev(v)=R(v2+v2x)dx and H(u,v)=R(uv+vxux)dx are conserved densities.

    Proof. First, we shall prove u,vW1,(Rx×R)L(R+;H1(R)). Let u0,v0H1(R) and assume that m0=u0u0,xx,n0=v0v0,xxM+(R). Note that u0=Pm0 and v0=Pn0. Thus, we have for any fL(R),

    u0L1=Pm0L1=supfL1Rf(x)(Pm0)(x)dx=supfL1Rf(x)RP(xy)dm0(y)dx=supfL1R(Pf)(y)dm0(y)supfL1PL1fLm0M(R)=m0M(R). (11)

    Similarly, we have

    v0L1n0M(R). (12)

    We first prove that there exists a corresponding (u,v) with the initial data (u0,v0), which belongs to H1loc(R+×R)L(R+;H1(R))×H1loc(R+×R)L(R+;H1(R)), satisfying equation (6) in the sense of distributions.

    Let us define un0=ρnu0H(R) and vn0=ρnv0H(R) for n1. Obviously, we have

    un0u0H1(R),n,vn0v0H1(R),n, (13)

    and for all n1,

    un01=ρnu01u01,vn01v01,un0L1=ρnu0L1u0L1,vn0L1v0L1, (14)

    in view of Young's inequality. Note that for all n1,

    mn0=un0un0,xx=ρnm00,andnn0=vn0vn0,xx=ρnv00.

    Comparing with the proof of relation (11) and (12), we get

    mn0L1m0M(R),andnn0L1n0M(R),n1. (15)

    By Theorem 2.8, we obtain that there exists a global strong solution

    un=un(,un0),vn=vn(,vn0)C([0,T);Hs(R))C1([0,T);Hs1(R))

    for every s3, and we have un(t,x)unxx(t,x)0 and vn(t,x)vnxx(t,x)0 for all (t,x)R+×R. In view of theorem 2.8 and (14), we obtain for n1 and t0,

    unx(t,)Lun(t,)Lun(t,)1=un01u01,vnx(t,)Lvn(t,)Lvn(t,)1=vn01v01. (16)

    By the above inequality, we have

    un(t,)vn(t,)unx(t,)L2un(t,)Lvn(t,)Lunx(t,)L2un(t,)21vn(t,)1u021v01. (17)

    Similarly, we have

    vn(t,)un(t,)vnx(t,)L2v021u01. (18)

    By Young's inequality and (16), for all t0 and n1, we obtain

    Px(12(unx)2vn+ununxvnx+(un)2vn)+12P((unx)2vnx)L2PxL212(unx)2vn+ununxvnx+(un)2vnL1+12PL2(unx)2vnxL112unx2L2vnL+12unLunxL2vnxL2+un2L2vnL+12vnxLunx2L252un21vn152u021v01. (19)

    Similarly, we get

    Px(12(vnx)2un+vnunxvnx+(vn)2un)+12P((vnx)2unx)L252v021u01. (20)

    Combining (17)-(20) with equation (6) for all t0 and n1, we find

    ddtun(t,)L272u021v01,andddtvn(t,)L272v021u01. (21)

    For fixed T>0, by Theorem 2.8 and (21), we have

    T0R([un(t,x)]2+[unx(t,x)]2+[unt(t,x)]2)dxdt(u021+494u041v021)T,T0R([vn(t,x)]2+[vnx(t,x)]2+[vnt(t,x)]2)dxdt(v021+494v041u021)T. (22)

    It follows that the sequence {un}n1 is uniformly bounded in the space H1((0,T)×R).Thus we can extract a subsequence such that

    unkuweaklyinH1(0,T)×R)fornk (23)

    and

    unku,a.e.on(0,T)×Rfornk, (24)

    for some uH1((0,T)×R). By Theorem 2.8, (11) and (14), we have that for fixed t(0,T), the sequence unkx(t,)BV(R) satisfies

    V[unkx(t,)]=unkxx(t,)L1unk(t,)L1+mnk(t,)L12mnk(t,)L12eunk01vnk01tmnk0L12eu01v01tm0M(R)

    and

    unkx(t,)Lunk(t,)1=unk0(t,)1u01.

    Applying Helly's theorem, we obtain that there exists a subsequence, denoted again by {unkx(t,)}, which converges at every point to some function ˆu(t,) of finite variation with

    V[ˆu(t,)]2eu01v01tm0M(R).

    Since for almost all t(0,T), unkx(t,)ux(t,) in D(R) in view of (24), it follows that ˆu(t,)=ux(t,) for a.e. t(0,T). Therefore, we have

    unkxuxa.e.on(0,T)×Rfornk, (25)

    and for a.e. t(0,T),

    V[ux(t,)]=uxx(t,)M(R)2eu01v01tm0M(R).

    We can analogously extract a subsequence of {vnk}, denote again by {vnk} such that

    vnkva.e.on(0,T)×Rfornkandvnkxvxa.e.on(0,T)×Rfornk. (26)

    By Theorem 2.8 (ii)(iii) and (16), we have

    12(unx)2vn+ununxvnx+(un)2vn+12(unx)2vnxL13u021v01.

    For fixed t(0,T), it follows that the sequence {12(unx)2vn+ununxvnx+(un)2vn+12(unx)2vnx} is uniformly bounded in L1(R). Therefore, it has a subsequence converging weakly in L1(R), denoted again by {12(unx)2vn+ununxvnx+(un)2vn+12(unx)2vnx}. By (24) and (25), we deduce that the weak L1(R)-limit is 12(ux)2vn+uuxvx+u2v+12(ux)2vx. Note that P,PxL(R). It follows that

    Px[12(unx)2vn+ununxvnx+(un)2vn]+P(12(unx)2vnx)Px[12u2xvn+uuxvx+u2v]+P(12u2xvx),asn. (27)

    We can analogously obtain that

    Px[12(vnx)2un+vnvnxunx+(vn)2un]+P(12(vnx)2unx)Px[12v2xun+vvxux+v2u]+P(12v2xux),asn. (28)

    Combining (24)-(26) with (27) and (28), we deduce that (u,v) satisfies equation (6) in D((0,T)×R).

    Since unkt(t,) is uniformly bounded in L2(R) for all tR+ and unk(t,)1 has a uniform bound as tR+ and all n1. Hence the family tunk(t,)H1(R) is weakly equicontinuous on [0,T] for any T>0. An application of the Arzela-Ascoli theorem yields that {unk} contains a subsequence, denoted again by {unk}, which converges weakly in H1(R), uniformly in t[0,T]. The limit function is u. Because T is arbitrary, we have that u is locally and weakly continuous from [0,) into H1(R), i.e.

    uCw,loc(R+;H1(R)).

    For a.e. tR+, since unk(t,)u(t,) weakly in H1(R), in view of (15) and (16), we obtain

    u(t,)Lu(t,)1lim infnkun(t,)1=lim infnkunk0(t,)1lim infnkP1mnk0(t,)L1m0M(R), (29)

    for a.e. tR+. The previous relation implies that

    uL(R+×R)L(R+;H1(R)).

    Note that by Theorem 2.8 and (15), we have

    unx(t,)Lun(t,)Lun(t,)1P1mn0(t,)L1m0(t,)M(R). (30)

    Combining this with (25), we deduce that

    uxL(R+×R).

    This shows that

    uW1,(R+×R)L(R+;H1(R)).

    Taking the same way as u, we get

    vW1,(R+×R)L(R+;H1(R)).

    Please note that we use the subsequence of {vnk} which is determined after using the Arzela-Ascoli theorem.

    Now, by a regularization technique, we prove that Eu(u), Ev(v) and H(u,v) are conserved densities. As (u,v) solves equation (6) in the sense of distributions, we see that for a.e. tR+, n1,

    {ρnut+ρn(uvux)+ρnPx(12u2xv+uuxvx+u2v)+12ρnP(u2xvx)=0,ρnvt+ρn(uvvx)+ρnPx(12v2xv+vuxvx+v2u)+12ρnP(v2xux)=0. (31)

    By differentiation of the first equation of (31), we obtain

    ρnuxt+ρn(uvux)x+ρnPx(12u2xvx)+ρnPxx(12u2xv+uuxvx+u2v)=0. (32)

    Note that 2(Pf)=Pff, fL2(R). We can rewrite (32) as

    ρnuxt+ρnx(uvux)+ρnP(12u2xv+uuxvx+u2v)ρn(12u2xv+uuxvx+u2v)+ρnPx(12u2xvx)=0. (33)

    Take these two equation (32) and (33) into the integration below, we obtain

    12ddtR(ρnu)2+(ρnux)2dx=R(ρnu)(ρnut)+(ρnux)(ρnuxt)dx=R(ρnu)(ρn(uvux)+ρnPx(12u2xv+uuxvx+u2v)+ρnP(12u2xvx))dxR(ρnux)(ρnx(uvux)+ρnP(12u2xv+uuxvx+u2v)ρn(12u2xv+uuxvx+u2v)+ρnPx(12u2xvx))dx. (34)

    Note that

    limnρnuuL2=limnρn(uvux)uvuxL2=0.

    Therefore, by using H¨older inequality, we have for a.e. tR+

    R(ρnu)(ρn(uvux))dxRu2vuxdx,asn.

    Similarly, for a.e. tR

    R(ρnu)(ρnPx(12u2xv+uuxvx+u2v))dxRuPx(12u2xv+uuxvx+u2v)dx,asn,
    R(ρnu)(ρnP(12u2xvx))dxRuP(12u2xvx)dx,asn,
    R(ρnux)(ρnP(12u2xv+uuxvx+u2v))dxRuxP(12u2xv+uuxvx+u2v)dx,asn,
    R(ρnux)(ρn(12u2xv+uuxvx+u2v))dxRux(12u2xv+uuxvx+u2v)dx,asn,
    R(ρnux)(ρnPx(12u2xvx))dxRuxPx(12u2xvx)dx,asn,

    as u(t,),v(t,)H1(R) and ux,vxL(R+×R). Furthermore, note that

    R(ρnux)(ρnx(uvux))dx=R(ρn,xxu)(ρ(uvux))dx+R(ρn,xxu)(ρnuv)(ρnux)dx+12R(ρnux)2(ρn(uv)x)dx. (35)

    Observe that

    R(ρnux)2(ρn(uv)x)dxRu2x(uv)xdx,asn.

    On the other hand

    ρnxxuL1uxxM(R)2eu01v01tm0M(R),t[0,T).

    As u(t,),v(t,)H1(R) and ux,vxL(R+×R), by Lemma 2.5, it follows that

    (ρnuv)(ρnux)(ρn(uvux))L0,n.

    Therefore,

    R(ρn,xxu)((ρnuv)(ρnux)ρn(uvux))dx0,n.

    In view of the above relations and (35), we obtain

    R(ρnux)(ρnx(uvux))dx12Ru2x(uv)xdx,n. (36)

    Let us define

    Eun(t)=R(ρnu)2+(ρnux)2dx, (37)

    and

    Gun(t)=2R(ρnu)(ρn(uvux)+ρnPx(12u2xv+uuxvx+u2v)+ρnP(12u2xvx))dx2R(ρnux)(ρnx(uvux)+ρnP(12u2xv+uuxvx+u2v)ρn(12u2xv+uuxvx+u2v)+ρnPx(12u2xvx))dx.

    We have proved that for fixed T>0, for a.e. t[0,T),

    {ddtEun(t)=Gun(t),n1,Gun(t)0,n. (38)

    Therefore, we get

    Eun(t)Eun(0)=t0Gun(s)ds,t[0,T),n1. (39)

    By Young's inequality and H¨older's inequality, it follows that there is a Ku(T)>0 such that

    |Gun(t)|Ku(T),n1.

    In view of (38) and (39), an application of Lebesgue's dominated convergence theorem yields that for fixed a.e. tR+,

    limn(Eun(t)Eun(0))=0.

    By (24) and the above relation, for fixed tR+, we can get

    Eu(u)=limnEun(t)=limnEun(0)=Eu(u0).

    By Theorem 2.8, we infer that for all fixed tR+, Eu(u) is conserved. Similarly, we can show that Ev(v) is also conserved.

    Next, we prove that H(u,v) is a conserved density.

    By differentiation of the second equation of (31), we obtain this relation:

    ρnvxt+ρnx(uvvx)+ρnP(12v2xu+vuxvx+v2u)ρn(12v2xu+vuxvx+v2u)+ρnPx(12v2xux)=0. (40)

    In view of (31), (33) and (40), we obtain

    ddtR(ρnu)(ρnv)+(ρnux)(ρnvx)dx=R(ρnu)(ρnvt)+(ρnux)(ρnvxt)+(ρnut)(ρnv)+(ρnuxt)(ρnvx)dx=R(ρnu)(ρn(uvvx)+ρnPx(12v2xu+vuxvx+v2u)
    +ρnP(12v2xux))dxR(ρnux)(ρnx(uvvx)+ρnP(12v2xu+vuxvx+v2u)ρn(12v2xu+vuxvx+v2u)+ρnPx(12v2xux))dxR(ρnv)(ρn(vuux)+ρnPx(12u2xv+uuxvx+u2v)+ρnP(12u2xvx))dxR(ρnvx)(ρnx(uvux)+ρnP(12u2xv+uuxvx+u2v)ρn(12u2xv+uuxvx+u2v)+ρnPx(12u2xvx))dx. (41)

    We can analogously get the similar convergence like the case ddtR(ρnu)2+(ρnux)2dx by using Lemma 2.5, u(t,),v(t,)H1(R) and ux,vxL(R+×R).

    It is nature to define

    Hn(t)=R(ρnu)(ρnv)+(ρnux)(ρnvx)dx, (42)

    and

    Gu,vn(t)=R(ρnu)(ρn(uvvx)+ρnPx(12v2xu+vuxvx+v2u)+ρnP(12v2xux))dxR(ρnux)(ρnx(uvvx)+ρnP(12v2xu+vuxvx+v2u)ρn(12v2xu+vuxvx+v2u)+ρnPx(12v2xux))dxR(ρnv)(ρn(vuux)+ρnPx(12u2xv+uuxvx+u2v)+ρnP(12u2xvx))dxR(ρnvx)(ρnx(uvux)+ρnP(12u2xv+uuxvx+u2v)ρn(12u2xv+uuxvx+u2v)+ρnPx(12u2xvx))dx. (43)

    And it is easy to get

    Hn(t)Hn(0)=t0Gu,vn(s)ds,t[0,T),n1. (44)

    Similarly, we get this estimate by using Young's inequality and Holder's inequality:

    |Gu,vn(t)|Ku,v(T),n1.

    An application of Lebesgue's dominated convergence theorem yields that for fixed a.e. tR+,

    limn[Hn(t)Hn(0)]=0.

    By these convergence above, for fixed tR+, we can get

    H(u,v)=limnHn(t)=limnHn(0)=H(u0,v0),

    which indicates that H(u, v) is a conserved density.

    Since L^1(\mathbb R)\subset(L^\infty(\mathbb R))^*\subset(C_0(\mathbb R))^* = {\mathcal {M}}(\mathbb R) . It is not too hard to show that for a.e. t\in[0, T) ,

    \begin{eqnarray*} \begin{aligned} \|m(t, \cdot)\|\leq 3e^{\|u_0\|_1\|v_0\|_1t}\|m_0\|_{{\mathcal {M}}(\mathbb R)}. \end{aligned} \end{eqnarray*}

    For any fixed T , \forall t\in[0, T) , we have proved

    \begin{eqnarray*} \begin{aligned} (u(t, \cdot)-u_{xx}(t, \cdot))\in {\mathcal {M}}(\mathbb R). \end{aligned} \end{eqnarray*}

    Therefore, in view of (24) and (25), we obtain that for all t\in[0, T) , as n\rightarrow\infty ,

    \begin{eqnarray*} \begin{aligned} u^{n_k}(t, \cdot)-u^{n_k}_{xx}(t, \cdot)\rightarrow u(t, \cdot)-u_{xx}(t, \cdot)\quad {\rm in} \quad D'(\mathbb R). \end{aligned} \end{eqnarray*}

    Since u^{n_k}(t, \cdot)-u^{n_k}_{xx}(t, \cdot)\geq 0 for all (t, x)\in{\mathbb R}_+\times \mathbb R , we deduce that for a.e. t\in [0, T)

    \begin{eqnarray*} \begin{aligned} u(t, \cdot)-u_{xx}(t, \cdot) \in {\mathcal {M}}^+(\mathbb R). \end{aligned} \end{eqnarray*}

    Similarly, we arrive at the conclusion:

    \begin{eqnarray*} \begin{aligned} v(t, \cdot)-v_{xx}(t, \cdot)\in {\mathcal {M}}^+(\mathbb R). \end{aligned} \end{eqnarray*}

    Finally, we show the uniqueness of the weak solutions of equation (6). Let (u, v) and (\bar{u}, \bar{v}) be two weak solutions of equation (6) in the class

    \begin{eqnarray*} \begin{aligned} (f, g)\in W^{1, \infty}({\mathbb R}_+\times \mathbb R)\cap L^\infty({\mathbb R}_+;H^1(\mathbb R))\times W^{1, \infty}({\mathbb R}_+\times \mathbb R)\cap L^\infty({\mathbb R}_+;H^1(\mathbb R)) \end{aligned} \end{eqnarray*}

    Note that

    \begin{eqnarray*} \begin{aligned} &\|u(t, \cdot)-u_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}\leq 3e^{\|u_0\|_1\|v_0\|_1t}\|m_0\|_{{\mathcal {M}}(\mathbb R)}, \\ &\|v(t, \cdot)-v_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}\leq 3e^{\|u_0\|_1\|v_0\|_1t}\|n_0\|_{{\mathcal {M}}(\mathbb R)}\quad {\rm for} \quad{\rm a.e.}\quad t\in[0, T). \end{aligned} \end{eqnarray*}

    Define

    \begin{eqnarray*} \begin{aligned} M(T) = \sup\limits_{t\in[0, T)}&\left\{\|u(t, \cdot)-u_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}+\|v(t, \cdot)-v_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}\right.\\ &\qquad\qquad\qquad\left.+\|\bar{u}(t, \cdot)-\bar{u}_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}+\|\bar{v}(t, \cdot)-\bar{v}_{xx}(t, \cdot)\|_{{\mathcal {M}}(\mathbb R)}\right\}. \end{aligned} \end{eqnarray*}

    Then for fixed T , we obtain M(T)<\infty . For all (t, x)\in[0, T)\times \mathbb R , in view of (11), we find that

    \begin{eqnarray} \begin{aligned} &\|u(t, \cdot)\|_{L^1}\leq \|P\|_{L^1}M(T) = M(T), \\ &\|u_x(t, \cdot)\|_{L^1}\leq \|P_x\|_{L^1}M(T) = M(T), \\ &\|v(t, \cdot)\|_{L^1}, \|v_x(t, \cdot)\|_{L^1}, \|\bar{u}(t, \cdot)\|_{L^1}, \|\bar{u}_x(t, \cdot)\|_{L^1}, \|\bar{v}(t, \cdot)\|_{L^1}\, {\rm{and}}\, \|\bar{v}_x(t, \cdot)\|_{L^1}\leq M(T). \end{aligned} \end{eqnarray} (45)

    On the other hand, from (29) and (30), we have

    \begin{eqnarray} \begin{aligned} &\|u(t, \cdot)\|_{L^\infty}\leq \|m_0\|_{{\mathcal {M}}(\mathbb R)}\leq N, \qquad \|u_x(t, \cdot)\|_{L^\infty}\leq \|m_0\|_{{\mathcal {M}}(\mathbb R)}\leq N, \\ &\|v(t, \cdot)\|_{L^\infty}\leq \|n_0\|_{{\mathcal {M}}(\mathbb R)}\leq N, \qquad \|v_x(t, \cdot)\|_{L^\infty}\leq \|n_0\|_{{\mathcal {M}}(\mathbb R)}\leq N, \\ &\|\bar{u}(t, \cdot)\|_{L^\infty}, \|\bar{u}_x(t, \cdot)\|_{L^\infty}, \|\bar{v}(t, \cdot)\|_{L^\infty}\, {\rm{and}}\, \|\bar{v}_x(t, \cdot)\|_{L^\infty}\leq N. \end{aligned} \end{eqnarray} (46)

    Let us define

    \begin{eqnarray*} \begin{aligned} \hat{u}(t, x) = u(t, x)-\bar{u}(t, x)\quad {\rm{and}}\quad \hat{v}(t, x) = v(t, x)-\bar{v}(t, x), \quad (t, x)\in[0, T)\times\mathbb R. \end{aligned} \end{eqnarray*}

    Convoluting equation (6) for (u, v) and (\bar{u}, \bar{v}) with \rho_n , we have that for a.e. t\in[0, T) and all n\geq 1 ,

    \begin{eqnarray} \begin{aligned} {\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{u}|dx = &\int_{\mathbb R}\rho_n\ast\hat{u}_t{\rm{sgn}}(\rho_n\ast\hat{u})dx\\ = &-\int_{\mathbb R}\rho_n\ast\left(\hat{u}vu_x+\bar{u}u_x\hat{v}+\bar{u}\bar{v}\hat{u}_x\right){\rm{sgn}}(\rho_n\ast\hat{u})dx\\ & -\int_{\mathbb R}\rho_n\ast P_{xx}{\rm{*}}({\frac 12}\hat{u}(u_x+\bar{u}_x)v+{\frac 12}\bar{u}^2_x\hat{v}+\hat{u}u_xv_x+\bar{u}v_x\hat{u}_x\\ &+\bar{u}\bar{u}_x\hat{v}_x+\hat{u}(u+\bar{u})v+u^2\hat{v}){\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ &-\int_{\mathbb R}\rho_n\ast{\frac 12}P\ast\left(\hat{u}_x(u_x+\bar{u}_x)v_x+\bar{u}^2_x\hat{v}_x\right){\rm{sgn}}(\rho_n\ast\hat{u})dx. \end{aligned} \end{eqnarray} (47)

    Using (46) and Young's inequality, we infer that for a.e. t\in[0, T) and all n\geq 1

    \begin{eqnarray} \begin{aligned} &{\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{u}|dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right). \end{aligned} \end{eqnarray} (48)

    where C is a constant depending on N . Similarly, convoluting equation (6) for (u, v) and (\bar{u}, \bar{v}) with \rho_{n, x} , it follows that

    \begin{eqnarray} \begin{aligned} {\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx = &\int_{\mathbb R}\rho_n\ast\hat{u}_{xt}{\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ = &-\int_{\mathbb R}\rho_n\ast\left(\hat{u}vu_x+\bar{u}u_x\hat{v}+\bar{u}\bar{v}\hat{u}_x\right)_x{\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ -&\int_{\mathbb R}\rho_n\ast P_{xx}\ast({\frac 12}\hat{u}(u_x+\bar{u}_x)v+{\frac 12}\bar{u}^2_x\hat{v}+\hat{u}u_xv_x+\bar{u}v_x\hat{u}_x\\ &\qquad\qquad+\bar{u}\bar{u}_x\hat{v}_x+\hat{u}(u+\bar{u})v+u^2\hat{v}){\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ -&\int_{\mathbb R}\rho_n\ast{\frac 12}P_x\ast\left(\hat{u}_x(u_x+\bar{u}_x)v_x+\bar{u}^2_x\hat{v}_x\right){\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ = &I_1+I_2+I_3. \end{aligned} \end{eqnarray} (49)

    For the term I_1 , we have

    \begin{eqnarray*} \begin{aligned} &I_1\\ = &-\int_{\mathbb R}\rho_n\ast(\hat{u}_xvu_x+\hat{u}u_xv_x+\hat{u}vu_{xx}+\bar{u}_xu_{x}\hat{v}+\bar{u}u_{xx}\hat{v}+\bar{u}u_x\hat{v}_x\\ &\qquad\qquad\qquad\qquad+\bar{u}_x\bar{v}\hat{u}_x+\bar{u}\bar{v}_x\hat{u}_x+\bar{u}\bar{v}\hat{u}_{xx}){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &\qquad\qquad\qquad\qquad-\int_{\mathbb R}\rho_n\ast(\hat{u}vu_{xx}+\bar{u}u_{xx}\hat{v}+\bar{u}\bar{v}\hat{u}_{xx}){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &-\int_{\mathbb R}(\rho_n\ast\hat{u}v)(\rho_n\ast u_{xx}){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx-\int_{\mathbb R}(\rho_n\ast\bar{u}\hat{v})(\rho_n\ast u_{xx}){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx\\ &\qquad\qquad\qquad\qquad-\int_{\mathbb R}(\rho_n\ast\bar{u}\bar{v})(\rho_n\ast \hat{u}_{xx}){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx+R_n(t) \end{aligned} \end{eqnarray*}
    \begin{eqnarray} \begin{aligned}&\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &+\int_{\mathbb R}(\rho_n\ast(\hat{u}v)_x)(\rho_n\ast u_x){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx+\int_{\mathbb R}(\rho_n\ast(\bar{u}\hat{v})_x)(\rho_n\ast u_x){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx\\ &\qquad\qquad\qquad\qquad+\int_{\mathbb R}(\rho_n\ast(\bar{u}\bar{v})_x)(\rho_n\ast \hat{u}_x){\rm{sgn}}(\rho_{nx}\ast \hat{u})dx+R_n(t)\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right) +R_n(t), \end{aligned} \end{eqnarray} (50)

    where C is a constant depending on M(T) , N , \|u_0\|_1 and \|v_0\|_1 and R_n(t) satisfies

    \begin{eqnarray} \left\{ \begin{aligned} &R_n(t)\longrightarrow 0, \qquad n\rightarrow\infty, \\ &|R_n(t)|\leq \kappa(T), \;\;\, n\geq 1, \;\;t\in[0, T). \end{aligned} \right. \end{eqnarray} (51)

    For the second term I_2 , we find

    \begin{eqnarray} \begin{aligned} &I_2\\ = -&\int_{\mathbb R}\rho_n\ast P_{xx}\ast\left({\frac 12}\hat{u}(u_x+\bar{u}_x)v+{\frac 12}\bar{u}^2_x\hat{v}+\hat{u}u_xv_x+\bar{u}v_x\hat{u}_x\right.\\ &\qquad+\left.\bar{u}\bar{u}_x\hat{v}_x+\hat{u}(u+\bar{u})v+u^2\hat{v}\right){\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ \leq& 2\int_{\mathbb R}\rho_n\ast\left|{\frac 12}\hat{u}(u_x+\bar{u}_x)v+{\frac 12}\bar{u}^2_x\hat{v}+\hat{u}u_xv_x+\bar{u}v_x\hat{u}_x+\bar{u}\bar{u}_x\hat{v}_x+\hat{u}(u+\bar{u})v+u^2\hat{v}\right|dx\\ \leq& C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right). \end{aligned} \end{eqnarray} (52)

    For the final term I_3 , we have

    \begin{eqnarray} \begin{aligned} I_3 = -&\int_{\mathbb R}\rho_n\ast{\frac 12}P_x\ast\left(\hat{u}_x(u_x+\bar{u}_x)v_x+\bar{u}^2_x\hat{v}_x\right){\rm{sgn}}(\rho_{nx}\ast\hat{u})dx\\ \leq& C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right). \end{aligned} \end{eqnarray} (53)

    Adding these three terms, we obtain

    \begin{eqnarray} \begin{aligned} &{\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)+R_n(t). \end{aligned} \end{eqnarray} (54)

    For these terms {\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{v}|dx and {\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx , we have similar results:

    \begin{eqnarray} \begin{aligned} &{\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{v}|dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right), \\ &{\frac d{dt}}\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)+R_n(t). \end{aligned} \end{eqnarray} (55)

    From (48), (54) and (55), we infer that

    \begin{eqnarray} \begin{aligned} &{\frac d{dt}}\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &\leq C\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)+R_n(t). \end{aligned} \end{eqnarray} (56)

    If \int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\ne 0 , then by Gronwall's inequality, we obtain

    \begin{eqnarray} \begin{aligned} &\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &\leq e^{\int^t_0C+\tilde{R}_n(\tau)d\tau}\left(|\rho_n\ast\hat{u}|+|\rho_n\ast\hat{u}_x|+|\rho_n\ast\hat{v}|+|\rho_n\ast\hat{v}_x|\right)(0, x), \end{aligned} \end{eqnarray} (57)

    where \tilde{R}_n(t) = R_n(t) \left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)^{-1} . From Lebesgue's dominated convergence theorem, it follows that

    \begin{eqnarray} \begin{aligned} &\left(\int_{\mathbb R}|\rho_n\ast\hat{u}|dx+\int_{\mathbb R}|\rho_n\ast\hat{u}_x|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}|dx+\int_{\mathbb R}|\rho_n\ast\hat{v}_x|dx\right)\\ &\leq e^{Ct}\left(|\rho_n\ast\hat{u}|+|\rho_n\ast\hat{u}_x|+|\rho_n\ast\hat{v}|+|\rho_n\ast\hat{v}_x|\right)(0, x), \end{aligned} \end{eqnarray} (58)

    As T is arbitrary, \hat{u}_0 = \hat{u}_{0x} = \hat{v}_0 = \hat{v}_{0, x} = 0 , we obtain (u, v) = (\bar{u}, \bar{v}) . This completes the proof of theorem 3.1.



    [1] R. K. Khadanga, A. Kumar, S. Panda, Frequency control in hybrid distributed power systems via type-2 fuzzy pid controller, IET Renewable Power Gener., 15 (2021), 1706–1723. https://doi.org/10.1049/rpg2.12140 doi: 10.1049/rpg2.12140
    [2] M. K. Diab, H. H. Ammar, R. E. Shalaby, Self-driving car lane-keeping assist using pid and pure pursuit control, in 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), IEEE, (2020), 1–6. https://doi.org/10.1109/3ICT51146.2020.9311987
    [3] H. Maghfiroh, M. Ahmad, A. Ramelan, F. Adriyanto, Fuzzy-pid in bldc motor speed control using matlab/simulink, J. Rob. Control (JRC), 3 (2022), 8–13. https://doi.org/10.18196/jrc.v3i1.10964 doi: 10.18196/jrc.v3i1.10964
    [4] J. R. Nayak, B. Shaw, B. K. Sahu, K. A. Naidu, Application of optimized adaptive crow search algorithm based two degree of freedom optimal fuzzy pid controller for agc system, Eng. Sci. Technol. Int. J., 32 (2022), 101061. https://doi.org/10.1016/j.jestch.2021.09.007 doi: 10.1016/j.jestch.2021.09.007
    [5] N. Ma, D. Li, W. He, Y. Deng, J. Li, Y. Gao, et al., Future vehicles: interactive wheeled robots, Sci. China Inf. Sci., 64 (2021), 1–3. https://doi.org/10.1007/s11432-020-3171-4 doi: 10.1007/s11432-020-3171-4
    [6] N. Ma, Y. Gao, J. Li, D. Li, Interactive cognition in self-driving, Chin. Sci.: Inf. Sci., 48 (2018), 1083–1096.
    [7] D. Li, N. Ma, Y. Gao, Future vehicles: learnable wheeled robots, Sci. China Inf. Sci., 63 (2020), 1–8. https://doi.org/10.1007/s11432-019-2787-2 doi: 10.1007/s11432-019-2787-2
    [8] T. Yang, N. Sun, Y. Fang, Adaptive fuzzy control for a class of mimo underactuated systems with plant uncertainties and actuator deadzones: Design and experiments, IEEE Trans. Cybern., 52 (2022), 8213–8226. https://doi.org/10.1109/TCYB.2021.3050475 doi: 10.1109/TCYB.2021.3050475
    [9] S. H. Park, K. W. Kim, W. H. Choi, M. S. Jie, Y. Kim, The autonomous performance improvement of mobile robot using type-2 fuzzy self-tuning PID controller, Adv. Sci. Technol. Lett., 138 (2016), 182–187. https://doi.org/10.14257/astl.2016.138.37 doi: 10.14257/astl.2016.138.37
    [10] P. Parikh, S. Sheth, R. Vasani, J. K. Gohil, Implementing fuzzy logic controller and pid controller to a dc encoder motor–-"a case of an automated guided vehicle", Procedia Manuf., 20 (2018), 219–226. https://doi.org/10.1016/j.promfg.2018.02.032 doi: 10.1016/j.promfg.2018.02.032
    [11] Q. Bu, J. Cai, Y. Liu, M. Cao, L. Dong, R. Ruan, et al., The effect of fuzzy pid temperature control on thermal behavior analysis and kinetics study of biomass microwave pyrolysis, J. Anal. Appl. Pyrolysis, 158 (2021), 105176. https://doi.org/10.1016/j.jaap.2021.105176 doi: 10.1016/j.jaap.2021.105176
    [12] M. S. Jie, W. H. Choi, Type-2 fuzzy pid controller design for mobile robot, Int. J. Control Autom., 9 (2016), 203–214.
    [13] N. Kumar, M. Takács, Z. Vámossy, Robot navigation in unknown environment using fuzzy logic, in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), IEEE, (2017), 279–284. https://doi.org/10.1109/SAMI.2017.7880317
    [14] T. Muhammad, Y. Guo, Y. Wu, W. Yao, A. Zeeshan, Ccd camera-based ball balancer system with fuzzy pd control in varying light conditions, in 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), IEEE, (2019), 305–310. https://doi.org/10.1109/ICNSC.2019.8743305
    [15] A. Wong, T. Back, A. V. Kononova, A. Plaat, Deep multiagent reinforcement learning: Challenges and directions, Artif. Intell. Rev., 2022 (2022). https://doi.org/10.1007/s10462-022-10299-x doi: 10.1007/s10462-022-10299-x
    [16] Z. Cao, S. Xu, H. Peng, D. Yang, R. Zidek, Confidence-aware reinforcement learning for self-driving cars, IEEE Trans. Intell. Transp. Syst., 23 (2022), 7419–7430. https://doi.org/10.1109/TITS.2021.3069497 doi: 10.1109/TITS.2021.3069497
    [17] T. Ribeiro, F. Gonçalves, I. Garcia, G. Lopes, A. F. Ribeiro, Q-learning for autonomous mobile robot obstacle avoidance, in 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE, (2019), 1–7. https://doi.org/10.1109/ICARSC.2019.8733621
    [18] S. Danthala, S. Rao, K. Mannepalli, D. Shilpa, Robotic manipulator control by using machine learning algorithms: A review, Int. J. Mech. Prod. Eng. Res. Dev., 8 (2018), 305–310.
    [19] X. Lei, Z. Zhang, P. Dong, Dynamic path planning of unknown environment based on deep reinforcement learning, J. Rob., 2018 (2018). https://doi.org/10.1155/2018/5781591 doi: 10.1155/2018/5781591
    [20] Y. Shan, B. Zheng, L. Chen, L. Chen, D. Chen, A reinforcement learning-based adaptive path tracking approach for autonomous driving, IEEE Trans. Veh. Technol., 69 (2020), 10581–10595. https://doi.org/10.1109/TVT.2020.3014628 doi: 10.1109/TVT.2020.3014628
    [21] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, et al., Continuous control with deep reinforcement learning, preprint, arXiv: 1509.02971. https://doi.org/10.48550/arXiv.1509.02971
    [22] P. Ramanathan, K. K. Mangla, S. Satpathy, Smart controller for conical tank system using reinforcement learning algorithm, Measurement, 116 (2018), 422–428. https://doi.org/10.1016/j.measurement.2017.11.007 doi: 10.1016/j.measurement.2017.11.007
    [23] L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, et al., Safe learning in robotics: From learning-based control to safe reinforcement learning, Annu. Rev. Control Rob. Auton. Syst., 5 (2022), 411–444. https://doi.org/10.1146/annurev-control-042920-020211 doi: 10.1146/annurev-control-042920-020211
    [24] A. I. Lakhani, M. A. Chowdhury, Q. Lu, Stability-preserving automatic tuning of PID control with reinforcement learning, preprint, arXiv: 2112.15187. https://doi.org/10.20517/ces.2021.15
    [25] O. Dogru, K. Velswamy, F. Ibrahim, Y. Wu, A. S. Sundaramoorthy, B. Huang, et al., Reinforcement learning approach to autonomous pid tuning, Comput. Chem. Eng., 161 (2022), 107760. https://doi.org/10.1016/j.compchemeng.2022.107760 doi: 10.1016/j.compchemeng.2022.107760
    [26] X. Yu, Y. Fan, S. Xu, L. Ou, A self-adaptive sac-pid control approach based on reinforcement learning for mobile robots, Int. J. Robust Nonlinear Control, 32 (2022), 9625–9643. https://doi.org/10.1002/rnc.5662 doi: 10.1002/rnc.5662
    [27] B. Guo, Z. Zhuang, J. S. Pan, S. C. Chu, Optimal design and simulation for pid controller using fractional-order fish migration optimization algorithm, IEEE Access, 9 (2021), 8808–8819. https://doi.org/10.1109/ACCESS.2021.3049421 doi: 10.1109/ACCESS.2021.3049421
    [28] M. Praharaj, D. Sain, B. Mohan, Development, experimental validation, and comparison of interval type-2 mamdani fuzzy pid controllers with different footprints of uncertainty, Inf. Sci., 601 (2022), 374–402.
    [29] Y. Jia, R. Zhang, X. Lv, T. Zhang, Z. Fan, Research on temperature control of fuel-cell cooling system based on variable domain fuzzy pid, Processes, 10 (2022), 534. https://doi.org/10.3390/pr10030534 doi: 10.3390/pr10030534
    [30] J. Wei, L. Gang, W. Tao, G. Kai, Variable universe fuzzy pid control based on adaptive contracting-expanding factors, Eng. Mech., 38 (2021), 23–32. https://doi.org/10.6052/j.issn.1000-4750.2020.11.0786 doi: 10.6052/j.issn.1000-4750.2020.11.0786
    [31] R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction, MIT press, 2018.
    [32] P. R. Montague, Reinforcement learning: an introduction, by Sutton, RS and Barto, AG, Trends Cognit. Sci., 3 (1999), 360. https://doi.org/10.1016/S1364-6613(99)01331-5 doi: 10.1016/S1364-6613(99)01331-5
    [33] D. Wang, R. Walters, X. Zhu, R. Platt, Equivariant q learning in spatial action spaces, in Conference on Robot Learning, PMLR, (2022), 1713–1723.
    [34] E. Anderlini, D. I. Forehand, P. Stansell, Q. Xiao, M. Abusara, Control of a point absorber using reinforcement learning, IEEE Trans. Sustainable Energy, 7 (2016), 1681–1690. https://doi.org/10.1109/TSTE.2016.2568754 doi: 10.1109/TSTE.2016.2568754
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