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Research article Special Issues

Persistence of the heteroclinic loop under periodic perturbation

  • Received: 24 September 2022 Revised: 04 December 2022 Accepted: 04 December 2022 Published: 14 December 2022
  • We consider an autonomous ordinary differential equation that admits a heteroclinic loop. The unperturbed heteroclinic loop consists of two degenerate heteroclinic orbits γ1 and γ2. We assume the variational equation along the degenerate heteroclinic orbit γi has di(di>1,i=1,2) linearly independent bounded solutions. Moreover, the splitting indices of the unperturbed heteroclinic orbits are s and s (s0), respectively. In this paper, we study the persistence of the heteroclinic loop under periodic perturbation. Using the method of Lyapunov-Schmidt reduction and exponential dichotomies, we obtained the bifurcation function, which is defined from Rd1+d2+2 to Rd1+d2. Under some conditions, the perturbed system can have a heteroclinic loop near the unperturbed heteroclinic loop.

    Citation: Bin Long, Shanshan Xu. Persistence of the heteroclinic loop under periodic perturbation[J]. Electronic Research Archive, 2023, 31(2): 1089-1105. doi: 10.3934/era.2023054

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  • We consider an autonomous ordinary differential equation that admits a heteroclinic loop. The unperturbed heteroclinic loop consists of two degenerate heteroclinic orbits γ1 and γ2. We assume the variational equation along the degenerate heteroclinic orbit γi has di(di>1,i=1,2) linearly independent bounded solutions. Moreover, the splitting indices of the unperturbed heteroclinic orbits are s and s (s0), respectively. In this paper, we study the persistence of the heteroclinic loop under periodic perturbation. Using the method of Lyapunov-Schmidt reduction and exponential dichotomies, we obtained the bifurcation function, which is defined from Rd1+d2+2 to Rd1+d2. Under some conditions, the perturbed system can have a heteroclinic loop near the unperturbed heteroclinic loop.



    Recently, fractional calculus emerged as a crucial tool for describing the dynamics of real-world problems [1,2,3]. Indeed, many fractional-order systems (FOSs) have been reported in the literature, focusing on stability, fault tolerant control, and sliding mode control, among other issues (see [4,5,6] and references therein). Iterative learning control (ILC) is an interesting approach to obtain trajectory tracking of repetitive systems operated over finite-time [7]. In recent years, FOSs and ILC have been merged with the goal of increasing tracking performance. In [8], a Dα-type ILC scheme was designed and its convergence was addressed. In [9,10,11], both the P- and D-type learning schemes were adopted in FOSs with Lipschitz nonlinearities. In [12], fractional-order PID learning control was proposed for linear FOSs, and output convergence was analyzed using the Lebesgue-p norm. In [13], the ILC framework was adopted for FOSs with randomly varying trial lengths. In [14,15], ILC problems of multi-agent systems with fractional-order models were investigated. Despite many relevant contributions, it should be pointed out that the above mentioned works mainly address linear and Lipschitz FOSs. Moreover, a variety of control strategies have been proposed for nonlinear systems (NSs) to achieve the desired performance. In [16,17], the convergence analysis for locally Lipschitz NSs was addessed based on the contraction mapping approach. In [18], adaptive optimal control was investigated for NSs based on the policy iteration algorithm. In [19], zero-sum control for tidal turbine systems was studied though a reinforcement learning method.

    Compared with classical Lipschitz nonlinearity, one-sided Lipschitz (OSL) nonlinearity possesses less conservatism. Therefore, in recent years, is has often been used in control systems. Moreover, in many practical problems, the OSL constant is much smaller than the Lipschitz one, which simplifies the estimation of the influence of nonlinearities. OSL systems are a wide class of NSs, which contain Lipschitz systems as particular cases. Practical examples are Chua's circuits, Lorenz systems, and electromechanical systems [20,21,22]. In [23,24,25], observer design issues for OSL NSs were investigated. In [26], the classical OSL was considered, and an observer was designed by introducing the quadratically inner-bounded (QIB) constraint. In recent years, observer design and control of OSL NSs has attracted considerable attention. In [27], full- and reduced-order observers were derived via the Riccati equation. In [28], exponential observer design was investigated. In [29], tracking control for OSL nonlinear differential inclusions was considered. In [30], H attenuation control was considered for OSL NSs in the finite frequency domain. In [31], event-triggered sliding mode control was studied for OSL NSs with uncertainties. In [32,33], consensus control was discussed for OSL nonlinear multi-agent systems. Other meaningful results on ILC of OSL NSs have also been reported [34,35,36]. In particular, the QIB constraint was employed to reach perfect trajectory tracking [34,35]. Note that the above-mentioned results are about classical integer-order systems. To the best of the authors' knowledge, for FOSs with OSL nonlinearity, the problem of how to achieve exact trajectory tracking through appropriate ILC design has not yet been investigated, which motivates the present study.

    This paper deals with the ILC of a family of Caputo FOSs, where the fractional derivative is in the interval 0 and 1. The considered nonlinearity satisfies the OSL condition, which encompasses the classical Lipschitz condition. Open- and closed-loop P-type learning control algorithms are adopted. The convergence of the tracking error is guaranteed with the generalized Gronwall inequality. The novelty of this paper is summarized in the next two points.

    ● Unlike the control methods in references [18,19,29,30,31,32,33], the ILC method proposed in this paper can lead OSL nonlinear Caputo FOSs to exhibit perfect tracking capability;

    ● In contrast to the works of [34,35,36], the ILC theory is extended from integer-order OSL NSs to fractional-order OSL NSs.

    This paper is divided into 5 sections. Section 2 establishes some elemental assumptions and formulates the ILC problem of fractional OSL NSs. Section 3 constructs the open- and closed-loop P-type control algorithms, and presents the corresponding convergence results. Section 4 includes a numerical example to show the suitability of the algorithms. Finally, Section 5 summarizes the conclusions.

    Some relevant lemmas and definitions are introduced. Afterwards, the problem to be tackled is formulated.

    Definition 1 [37]. The Riemann-Liouville integral of order α>0 of a function x(t) is

    Iα0,tx(t)=1Γ(α)t0(tξ)α1x(ξ)dξ,t[0,),

    where Γ(α) stands for the Gamma function.

    Definition 2 [37]. The Caputo derivative of order 0<α<1 of a function x(t) is

    CDα0,tx(t)=I1α0,tddtx(t)=1Γ(1α)t0(tξ)α˙x(ξ)dξ,t[0,).

    Lemma 1 [38]. Consider the differentiable vector x(t)Rn. It follows that, for any time instant t0, we have

    CDα0,t(xT(t)x(t))2xT(t)CDα0,tx(t),α(0,1),

    where the superscript T denotes the vector (or matrix) transpose.

    Lemma 2. (Generalized Gronwall Inequality) [9] Consider that the function u(t) is continuous on the interval t[0,T], and let v(tξ) be nonnegative and continuous on 0ξtT. Additionally, consider that the function w(t) is positive continuous and nondecreasing on t[0,T]. If

    u(t)w(t)+t0v(tξ)u(ξ)dξ,t[0,T],

    then we have

    u(t)w(t)et0v(tξ)dξ,t[0,T].

    To simplify the notation, in the following, we use Dα to refer to the Caputo derivative CDα0,t.

    Let us consider the nonlinear FOS

    {Dαxk(t)=Axk(t)+Buk(t)+f(xk(t)),yk(t)=Cxk(t)+Duk(t), (2.1)

    where α(0,1), t[0,T], and k=0,1,2, is the repetition. Moreover, xk(t)Rn, uk(t)Rm, and yk(t)Rp represent the state, control, and output of (2.1), respectively; f(xk(t))Rn stands for a continuous nonlinear function; and A, B, C, and D are constant coefficients matrices.

    Assumption 1. The nonlinear function f() is OSL, meaning that, for x(t),ˆx(t)Rn,

    f(x(t))f(ˆx(t)),x(t)ˆx(t)σx(t)ˆx(t)2,

    where denotes the Euclidean norm, , represents the inner product, and σR is the OSL constant.

    Remark 1. Note that the above constant σ can assume any real value, while the Lipschitz constant is positive. From [26], a Lipschitz function is OSL (σ>0), but the converse may not hold.

    Assumption 2. The desired trajectory yd(t) is possible, meaning that a control ud(t) exists, guaranteeing

    {Dαxd(t)=Axd(t)+Bud(t)+f(xd(t)),yd(t)=Cxd(t)+Dud(t),

    with xd(t) being the desired state.

    Assumption 3. The system defined by expression (2.1) meets the initial condition

    xk(0)=xd(0),k=0,1,2,,

    where xd(0) represents the desired initial state.

    Remark 2. Assumption 2 is a representative condition for OSL Caputo FOSs in control law design. Assumption 3 is the identical initialization condition, which has been widely used in ILC design to obtain perfect tracking [7].

    The main objective herein is to design a control sequence uk(t) so that the output yk(t) of (1) can track the specified trajectory yd(t), with t[0,T], as k.

    For the nonlinear FOS (2.1), we design an open-loop P-type learning control algorithm

    uk+1(t)=uk(t)+Ψek(t), (3.1)

    where the output tracking error at the kth iteration is defined as ek(t)=yd(t)yk(t) and the learning gain matrix is ΨRm×p.

    Theorem 1. Let us assume that Assumptions 1–3 hold for the FOS (2.1) with algorithm (3.1). If Ψ can be chosen such that

    ρ1=IDΨ<1, (3.2)

    then yk(t) converges to yd(t) for t[0,T].

    Proof. Let us use δ()k(t)=()k+1(t)()k(t), where () stands for the variables x, u, and f. It follows from (2.1) and (3.1) that

    Dα(δxk(t))=Aδxk(t)+Bδuk(t)+δfk(t)=Aδxk(t)+δfk(t)+BΨek(t). (3.3)

    If we left-multiply (3.3) by (δxk(t))T and use Assumption 1, then we have

    (δxk(t))TDα(δxk(t))=Aδxk(t),δxk(t)+BΨek(t),δxk(t)+δfk(t),δxk(t)(Aδxk(t))Tδxk(t)+(BΨek(t))Tδxk(t)+σδxk(t)2(A+|σ|)δxk(t)2+BΨδxk(t)ek(t). (3.4)

    According to Lemma 1,

    Dα((δxk(t))Tδxk(t))2(δxk(t))TDα(δxk(t)). (3.5)

    From (3.4) and (3.5), we get

    Dα(δxk(t)2)2(A+|σ|)δxk(t)2+2BΨδxk(t)ek(t)(2A+2|σ|+1)δxk(t)2+BΨ2ek(t)2=c1δxk(t)2+c2ek(t)2, (3.6)

    where c1=2A+2|σ|+1 and c2=BΨ2. Applying the α-order integral on (3.6), we get

    Iα0,tDα(δxk(t)2)Iα0,t(c1δxk(t)2+c2ek(t)2). (3.7)

    It follows from Assumption 3 that δxk(0)2=0, and we further get

    Iα0,tDα(δxk(t)2)=Iα0,tI1α0,tddt(δxk(t)2)=I10,tddt(δxk(t)2)=δxk(t)2δxk(0)2=δxk(t)2,

    which, together with (3.7), leads to

    δxk(t)2Iα0,t(c1δxk(t)2+c2ek(t)2)=c1Γ(α)t0(tξ)α1δxk(ξ)2dξ+c2Γ(α)t0(tξ)α1ek(ξ)2dξ=c1Γ(α)t0(tξ)α1δxk(ξ)2dξ+c2Γ(α)t0(tξ)α1e2λξ{e2λξek(ξ)2}dξc1Γ(α)t0(tξ)α1δxk(ξ)2dξ+c2Γ(α)t0(tξ)α1e2λξdξek2λ. (3.8)

    We can see that

    t0(tξ)α1e2λξdξtξ=τt0τα1e2λ(tτ)dτ=e2λtt0τα1e2λτdτ2λτ=ξe2λt(2λ)α2λt0ξα1eξdξ<e2λt(2λ)α+0ξα1eξdξ=e2λt(2λ)αΓ(α). (3.9)

    From (3.8) and (3.9), we have

    δxk(t)2c1Γ(α)t0(tξ)α1δxk(ξ)2dξ+c2e2λt(2λ)αek2λ.

    Setting

    v(tξ)=c1Γ(α)(tξ)α1,w(t)=c2e2λt(2λ)αek2λ,

    and using Lemma 2, we get

    δxk(t)2c2e2λt(2λ)αec1Γ(α)t0(tξ)α1dξek2λ=c2e2λt(2λ)αec1Γ(α)tααek2λc2e2λt(2λ)αec1TαΓ(α+1)ek2λ.

    Multiplying the above inequality by e2λt, and using the λ-norm λ, we have

    δxk2λc2ec1TαΓ(α+1)(2λ)αek2λ,

    where λ=supt[0,T]{eλt}.

    Therefore, we get

    δxkλc3λαekλ, (3.10)

    where

    c3=c2ec1TαΓ(α+1)2α.

    It is obvious that

    ek+1(t)=ek(t)Cδxk(t)Dδuk(t)=(IDΨ)ek(t)Cδxk(t). (3.11)

    It follows from (3.2), (3.10), and (3.11) that

    ek+1λIDΨekλ+Cδxkλρ1ekλ+Cδxkλρ1ekλ+c3Cλαekλ=ˆρ1ekλ, (3.12)

    where

    ˆρ1=ρ1+c3Cλα.

    As 0ρ1<1 by (3.2), we can select λ as large as needed so that ˆρ1<1. Thus, we obtain

    limkekλ=0.

    Note that ekseλTekλ, with s=supt[0,T] denoting the supremum norm. Therefore, limkeks=0, meaning that

    limkyk(t)=yd(t),t[0,T].

    This ends the proof.

    Now, we design a closed-loop P-type learning control algorithm, such that

    uk+1(t)=uk(t)+Φek+1(t), (3.13)

    where the learning gain is ΦRm×p.

    Theorem 2. Consider that Assumptions 1–3 hold for the FOS (2.1) with the learning algorithm (3.13). If the gain Φ can be chosen such that

    ρ2=(I+DΦ)1<1, (3.14)

    then yk(t) converges to yd(t) for t[0,T].

    Proof. From (2.1) and (3.13), we get

    Dα(δxk(t))=Aδxk(t)+Bδuk(t)+δfk(t)=Aδxk(t)+δfk(t)+BΦek+1(t). (3.15)

    Left multiplying (3.15) by (δxk(t))T and considering Assumption 1, we obtain

    (δxk(t))TDα(δxk(t))=Aδxk(t),δxk(t)+BΦek+1(t),δxk(t)+δfk(t),δxk(t)(Aδxk(t))Tδxk(t)+(BΦek+1(t))Tδxk(t)+σδxk(t)2(A+|σ|)δxk(t)2+BΦδxk(t)ek+1(t). (3.16)

    Obviously, (3.16) together with (3.5) implies

    Dα(δxk(t)2)2(A+|σ|)δxk(t)2+2BΦδxk(t)ek+1(t)(2A+2|σ|+1)δxk(t)2+BΨ2ek+1(t)2=c1δxk(t)2+c4ek+1(t)2, (3.17)

    where c4=BΦ2. Similarly to the procedure adopted in Theorem 1, we get

    δxkλc5λαek+1λ, (3.18)

    where

    c5=c4ec1TαΓ(α+1)2α.

    From expressions (2.1) and (3.13), we have

    ek+1(t)=ek(t)Cδxk(t)Dδuk(t)=ek(t)Cδxk(t)DΦek+1(t),

    that is

    (I+DΦ)ek+1(t)=ek(t)Cδxk(t),

    where the symbol I stands for the identity matrix. Since I+DΦ is nonsingular, we get

    ek+1(t)=(I+DΦ)1(ek(t)Cδxk(t)).

    Furthermore, we derive

    ek+1λ(I+DΦ)1ekλ+(I+DΦ)1Cδxkλρ2ekλ+(I+DΦ)1Cδxkλ. (3.19)

    Substituting (3.18) into (3.19) yields

    ek+1λρ2ekλ+c5λα(I+DΦ)1Cek+1λ.

    Taking λ such that

    c5λα(I+DΦ)1C<1,

    then we have

    ek+1λˆρ2ekλ, (3.20)

    where

    ˆρ2=ρ21c5λα(I+DΦ)1C.

    As 0ρ2<1, we can choose λ as large as needed so that ˆρ2<1. From expression (3.20), we can obtain

    limkekλ=0.

    As ekseλTekλ, we know that limkeks=0, and it follows that

    limkyk(t)=yd(t),t[0,T].

    This completes the proof.

    We illustrate the applicability of the P-type learning algorithms by means of a practical example.

    Let us choose the following nonlinear FOS, which can be used to describe the motion of a moving object in Cartesian coordinates [39]

    {D0.5xk(t)=Axk(t)+Buk(t)+f(xk(t)),yk(t)=Cxk(t)+Duk(t),

    where xk(t)=[x1k(t)x2k(t)]T, with t[0,1],

    A=[1211],B=[1021],C=[1002],D=[1001],
    f(xk(t))=[x1k(t)(x21k(t)+x22k(t))x2k(t)(x21k(t)+x22k(t))].

    We know from [26] that the nonlinear function f() is globally OSL with σ=0 in R2. Let us use

    yd(t)=[sin(3πt)te0.1t],

    and consider

    xk(0)=[00],u0(t)=[00].

    i) Open-loop algorithm (3.1).

    Using the gain matrix

    Ψ=[0.5000.5],

    we then have

    ρ1=IDΨ=0.5<1.

    Figures 1 and 2 depict the desired trajectories y(1)d(t) and y(2)d(t), and the outputs y(1)k(t) and y(2)k(t), respectively, at the 3rd, 5th, and 7th iterations, obtained with the learning algorithm (3.1). Figure 3 represents the errors, showing that perfect tracking is reached as the number of iterations increases.

    Figure 1.  Desired trajectory and system output, y(1)d(t) and y(1)k(t), at the 3rd, 5th, and 7th iterations, when using the learning algorithm (3.1).
    Figure 2.  Desired trajectory and system outputs, y(2)d(t) and y(2)k(t), at the 3rd, 5th, and 7th iterations, obtained with the learning algorithm (3.1).
    Figure 3.  The output tracking errors versus the number of iterations, obtained with the learning algorithm (3.1).

    ii) Closed-loop algorithm (3.13).

    Using the gain matrix

    Φ=[1001],

    then we have

    ρ2=(I+DΦ)1=0.5<1,

    meaning that the convergence is verified. Figures 4 and 5 illustrate that y(1)k(t) and y(2)k(t) follow the desired trajectories from the 6th iteration. Figure 6 shows that the error converges under algorithm (3.13).

    Figure 4.  Desired trajectory and system output, y(1)d(t) and y(1)k(t), at the 2nd, 4th, and 6th iterations, when using the learning algorithm (3.13).
    Figure 5.  Desired trajectory and system output, y(2)d(t) and y(2)k(t), at the 2nd, 4th, and 6th iterations, when using the learning algorithm (3.13).
    Figure 6.  The output tracking errors versus the number of iterations, obtained with the learning algorithm (3.13).

    We also verify that, at the 10th iteration, ||e(i)k||s(i=1,2) {achieves} [1.4×103, 5.7×103] and [0.5×103, 1.4×103] when using algorithms (3.1) and (3.13), respectively. This confirms the results observed in Figures 3 and 6, meaning that algorithm (3.13) performs better than (3.1) in terms of convergence speed.

    The ILC for a class of Caputo FOSs with OSL nonlinearity was investigated. Open- and closed-loop P-type learning algorithms were designed to guarantee perfect tracking of a desired trajectory, and their convergence was verified using the generalized Gronwall inequality. An example was provided to verify the theoretical results. It should be noted that the QIB constraint was used in the framework of ILC for OSL NSs with irregular dynamics [34,35]. To some extent, this limits the applicability of NSs due to their need to simultaneously satisfy the OSL and QIB constraints. Therefore, the ILC for irregular OSL nonlinear FOSs needs to be further investigated by relaxing or removing the QIB constraint.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This work was supported by the National Natural Science Foundation of China (Nos. 62073114 and 11971032) and Anhui Provincial Key Research and Development Project (202304a05020060).

    The authors declare there is no conflict of interest.



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  • This article has been cited by:

    1. Bin Long, Yiying Yang, Applying Lin's method to constructing heteroclinic orbits near the heteroclinic chain, 2024, 47, 0170-4214, 10235, 10.1002/mma.10118
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