Research article

A new design method to global asymptotic stabilization of strict-feedforward nonlinear systems with state and input delays

  • Received: 05 January 2024 Revised: 05 March 2024 Accepted: 06 March 2024 Published: 07 March 2024
  • MSC : 34D20

  • This paper studies the global asymptotic stabilization problem of strict-feedforward nonlinear systems with state and input delays. We will first transform the considered system into an equivalent system by constructing the novel parameter-dependent state feedback controller and introducing the appropriate coordinate transformation. After that, the global asymptotic stability of the closed system is proved by giving the proper Lyapunov-Krasovskii functional and using the stability criterion of time-delay system.

    Citation: Mengmeng Jiang, Xiao Niu. A new design method to global asymptotic stabilization of strict-feedforward nonlinear systems with state and input delays[J]. AIMS Mathematics, 2024, 9(4): 9494-9507. doi: 10.3934/math.2024463

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  • This paper studies the global asymptotic stabilization problem of strict-feedforward nonlinear systems with state and input delays. We will first transform the considered system into an equivalent system by constructing the novel parameter-dependent state feedback controller and introducing the appropriate coordinate transformation. After that, the global asymptotic stability of the closed system is proved by giving the proper Lyapunov-Krasovskii functional and using the stability criterion of time-delay system.



    Feedforward systems (namely upper-triangular systems) are a class of important nonlinear systems. A mass of physical devices, such as the planar vertical takeoff and landing aircraft [1], the ball-beam with a friction term and the translational oscillator with a rotational actuator system [2], the cart-pendulum system [3,4], can be described by equations with the upper-triangular structure. Moreover, feedforward nonlinear systems cannot be linearized, which results that it is more hard to researchers to find appropriate control method. Based on this, the research of feedforward nonlinear systems has attracted considerable attention, see [5,6,7,8,9] and the references therein.

    On the other hand, time-delay systems constitute basic mathematical models of real phenomena and time delays are often encountered in multifarious engineering systems. Hence, the research of control problem for time-delay systems is one of the most interesting and significant problems, and the Lyapunov-Krasovskii method and Lyapunov-Razumikhin method are two powerful tools in the stability analysis and controller design for time-delay systems [10,11]. There are many results focused on time-delay systems. [12,13,14,15] considered the one-order feedforward nonlinear systems, [16,17,18] considered high-order feedforward nonlinear systems. However, these results only considered state time delay or input delay only appearing in the nonlinearities. Input delay is often unavoidable in practice and often generate instability due to sensors, information processing or transport [19]. [20] considered adaptive dynamic high-gain scaling based output-feedback control of nonlinear feedforward systems with time delays in input and state. [21,22,23] considered the stabilization of feedforward nonlinear systems with linear growth condition. [24] designed stabilizing controllers for high order feedforward nonlinear systems with input delay. [25] studied memoryless linear feedback control for a class of upper-triangular systems with large delays in state and input. [26] considered global stabilization by memoryless feedback for nonlinear systems with a small input delay and large state delays. [27] developed homogeneous output feedback design for time-delay nonlinear integrators and beyond.

    It is worth noting that the most mentioned above conclusions on feedforward time-delay systems take advantage of the homogeneous domination approach and it is useful to handle the special structure of feedforward nonlinear systems (see Remark 1 for the detailed discussion). However, this method does not work well for strict-feedforward nonlinear systems with state and input delays. The purpose of this paper is to find a useful method. The main contributions are:

    (i) By applying the stability criterion on time-delay system, a novel parameter-dependent state feedback controller is proposed to guarantee the global asymptotic stabilization of strict-feedforward nonlinear systems with time delay in state and input.

    (ii) The parameter-dependent state feedback controller is very simple and flexible, because this controller only depends on a positive parameter. And the design process and computing effort are greatly reduced.

    (iii) Due to the appearance of time-delay in control input, the L-K functionals in the existing papers are no longer applicable, a difficult work is how to find an appropriate L-K functional. And how to deal with the terms related to nonlinear function and controller is another difficulty.

    This paper is organized as follows. Section 2 gives some preliminaries. The main results is given in Section 3. Section 4 presents the extended results. Two numerical examples are given in Section 5. Section 6 concludes this paper.

    Some notions and lemmas are to be used throughout this paper.

    Notations. || is the Euclidean norm of a vector and stands for the Frobenius norm of a matrix. A function f:RnR is C if it is continuous and is C1 if it is continuously differential. K denotes the set of all functions: R+R+ that are continuous, strictly increasing and vanishing at zero. K denotes the set of all functions that are of class K and unbounded.

    Lemma 2.1: [28] Consider system

    ˙x=f(t,x(t+θ)), (2.1)

    where θ[d,0], x(t)Rn and f:R×CRn with f(t,0)0. Suppose that f:R×CRn given in (2.1), maps every R× (bounded set in C) into a bounded set in Rn, and that u,v,w:R+R+ are continuous nondecreasing functions, where additionally u(s) and v(s) are positive for s>0, and u(0)=v(0)=0. If there exists a continuous differentiable functional V:R×CR such that

    u(x(0))V(t,x)v(supdθ0|x(t+θ)|)

    and

    ˙V(t,x)w(x(0)),

    then the trivial solution of (2.1) is uniformly stable. If w(s)>0 for s>0, then it is uniformly asymptotically stable. In addition, if limsu(s)=, then it is globally uniformly asymptotically stable.

    Lemma 2.2: [29]. For any given vectors y,z and constant a>0, there are real numbers μ>1 and ν>1 satisfying (μ1)(ν1)=1 such that

    yzaμμ|y|μ+1νaν|z|ν.

    Lemma 2.3: [30]. For any function f(t)C([τ,):R+) and positive integer p,τR+, then

    (ttτf(σ)dσ)pτp1ttτfp(σ)dσ.

    In this paper, we consider the following strict-feedforward nonlinear systems with state and input delays described by

    ˙x1(t)=x2(t)+f1(x2(t),,xn(t),x2(tτ2),,xn(tτn)),˙x2(t)=x3(t)+f2(x3(t),,xn(t),x3(tτ3),,xn(tτn)),˙xn(t)=u(tτ1), (3.1)

    where x(t)=(x1(t),,xn(t))Rn and u(t)R are system state and control input, respectively. For i=1,,n, τi>0 is time-invariant delay, ˉτ=max{τ1,τ2,,τn}, xi(tτi) and u(tτ1) are time-delayed systems state and time-delayed control input. Nonlinear functions fi, i=1,,n1, are continuous.

    This paper aims to construct a novel parameter-dependent state feedback controller of system (3.1) such that the eqailibrium at the origin of the closed-loop system is global asymptotically stable. In order to achieve this purpose, the following assumption is needed.

    Assumption 3.1: There is a known positive constant c such that

    |fi()|cnj=i+1(|xj(t)|+|xj(tτj)|),i=1,,n1.

    Remark 3.1: As discussed in feedforward nonlinear systems [14,21,22,23], the linear growth condition in Assumption 1 is a general condition for dealing with the nonlinearity fi() in feedforward systems. It is not hard to see from Assumption 3.1 that the nonlinear term fi() in this paper contains system states xi+1,,xn rather than xi+2,,xn in [14,21,22,23]. Hence, system (3.1) can be viewed as a class of strict-feedfroward nonlinear systems.

    It is obvious that system (3.1) can be rewritten as

    ˙x=Ax+Bu(tτ1)+F, (3.2)

    where

    A=[0(n1)×1I(n1)×(n1)001×(n1)],B=[0(n1)×11],F=[f1()fn1()0].

    The main result of this paper is stated in the following theorem.

    Theorem 3.1: If Assumption 3.1 holds for system (3.1) and there exists a positive parameter λ such that ˉΦ(λ)˜Φ(λ)2ˉτˆΦ(λ)>0, then the equilibrium at the origin of closed-loop system is global asymptotically stable by adopting the parameter-dependent state feedback controller

    u=[Θ1λn,Θ2λn1,,Θnλ]x=:Θ(λ)x, (3.3)

    where ˉΦ(λ), ˜Φ(λ) and ˆΦ(λ) are defined in (3.14) and ˉτ is defined in system (3.1), [Θ1,,Θn]=ˉΘ satisfying AˉΘ=:A+BˉΘ is Hurwitz.

    Proof. The proof procedure of Theorem 3.1 can be divided into two parts.

    Part I: Introduce the coordinate transformations:

    ξ=[x1λx2λn1xn]=:Γ(λ)x, (3.4)

    where ξ=[ξ1,ξ2,,ξn] and Γ(λ)=diag[1,λ,,λn1].

    Meanwhile, according to (3.2)-(3.4) and using the fact of λΓ(λ)(A+BΘ(λ))=AˉΘΓ(λ), the closed-loop system is transformed into

    ˙ξ=λ1AˉΘξ+Γ(λ)B(u(tτ1)u(t))+Γ(λ)F. (3.5)

    Choose the candidate Lyapunov function

    V(ξ)=δ12ξPξ, (3.6)

    where δ1 is a positive constant and P is a symmetric positive definite matrix that satisfies PAˉΘ+AˉΘP=I. Applying (3.5) and (3.6), one has

    ˙Vδ1λ1|ξ|2+2δ1(ξP)(Γ(λ)F)+2δ1(ξP)Γ(λ)B(u(tτ1)u(t)). (3.7)

    Let us consider the last two terms on the right-hand side.

    By Assumption 3.1 and (3.4), one has

    |Γ(λ)F|cn1i=1λi1(nl=i+1|xi|+nl=i+1|xl(tτl)|)=cni=2i2k=0λk(|xi|+|xi(tτi)|)=cni=2i1k=11λk(|ξi|+|ξi(tτi)|)cn1i=11λini=2(|ξi|+|ξi(tτi)|)cn1n1i=11λi|ξ|+cn1i=11λinj=2|ξj(tτj)|. (3.8)

    Then, by Lemma 2.2 and (3.8), one can obtain

    2δ1(ξP)(Γ(λ)F)n1Φ1(λ)|ξ|2+Φ1(λ)nj=2|ξ||ξj(tτj)|(c21(n1)2+n1)Φ1(λ)|ξ|2+Φ1(λ)2c21nj=2|ξj(tτj)|2, (3.9)

    where Φ1(λ)=2cδ1Pn1i=11λi and c1 is a positive real number.

    By using Lemma 2.2, Assumption 3.1 and (3.3), one leads to

    2δ1(ξP)Γ(λ)B(u(tτ1)u(t))2Γ(λ)λnni=1Θiδ1P|ξ|nj=1|ξj(tτ1)ξj(t)|Φ2(λ)(c22|ξ|2+1c22(nj=1(ξj(tτ1)ξj(t)))2), (3.10)

    where Φ2(λ)=Γ(λ)λnδ1Pni=1|Θi| and c2 is a positive real number. When i=,n1,

    |ξj(tτ1)ξj(t)||ttτ1˙ξj(σ)dσ|ttτ1|λ1ξj+1+λj1fj|dσΦ3,j(λ)ttτ1nj=2(|ξj(σ)|+|ξj(στj)|)dσ, (3.11)

    where Φ3,j(λ)=(c+1)(1λ+nl=j+11λlj).

    When i=n,

    |ξn(tτ1)ξn(t)||ttτ1˙ξn(σ)dσ|1λttτ1nj=1|Θjξj(στ1)|dσΦ4(λ)tt2ˉτ(nj=1|ξj(σ)|)dσ, (3.12)

    where Φ4(λ)=1λnj=1|Θj|. With the help of Lemma 2.3, (3.10)-(3.12), one can obtain

    2δ1(ξP)Γ(λ)B(u(tτ1)u(t))Φ2(λ)(c22|ξ|2+2ˉτ((n1j=1Φ3,j(λ))2+Φ4(λ)2)c22tt2ˉτni=2|ξi(σ)|2dσ). (3.13)

    Combining (3.9) and (3.13), one leads to

    ˙V{δ1λ1(c21(n1)2+n1)Φ1(λ)c22Φ2(λ)}|ξ|2+12c21Φ1(λ)nj=1|ξj(tτj)|2+1c22Φ2(λ)(2ˉτ((n1j=1Φ3,j(λ))2+Φ4(λ)2))tt2ˉτnj=1ξ2j(σ)dσ=:ˉΦ(λ)|ξ|2+˜Φ(λ)nj=1|ξj(tτj)|2+ˆΦ(λ)tt2ˉτnj=1|ξj(σ)|2dσ. (3.14)

    Construct the following L-K functional

    ˉV=V+˜Φ(λ)ni=1ttτj|ξj(σ)|2dσ+ˆΦ(λ)tt2ˉτtμnj=1|ξj(σ)|2dσdμ, (3.15)

    then

    ˙ˉV(ˉΦ(λ)˜Φ(λ)2ˉτˆΦ(λ))|ξ|2=:ϕ(λ)|ξ|2. (3.16)

    Taking γ(s)=ϕ(λ)s2 and applying ˉΦ(λ)˜Φ(λ)2ˉτΦ(λ)>0, then γ(s) is a K function and (3.16) is changed into

    ˙ˉVγ(|ξ|). (3.17)

    Part II: Next, we verify that ˉV satisfies the first condition of Lemma 2.1.

    On the basis of |ξ|sup2ˉτθ0|ξ(θ+t)| and (3.6), one has

    π1(|ξ|)Vπ21(sup2ˉτθ0|ξ(θ+t)|), (3.18)

    where π21(s)=δ12λ2min(P)s2 and π21(s)=δ12λ2max(P)s2 are class K functions.

    ˜Φ(λ)ni=2ttτj|ξj(σ)|2dσ˜Φ(λ)ni=20ˉτ|ξj(θ+t)|2dθ˜Φ(λ)ˉτsupˉτθ0|ξ(θ+t)|2˜Φ(λ)ˉτ(sup2ˉτθ0|ξ(θ+t)|)2=:π22(sup2ˉτθ0|ξ(θ+t)|), (3.19)

    where π22(s)=˜Φ(λ)ˉτs2 is a class K function.

    ˆΦ(λ)tt2ˉτtμnj=2|ξj(σ)|2dσdμ2ˆΦ(λ)ˉτtt2ˉτnj=2|ξj(σ)|2dσ2ˆΦ(λ)ˉτ02ˉτnj=2|ξj(s+θ)|2d(s+θ)2ˆΦ(λ)ˉτ(sup2ˉτθ0|ξ(θ+t)|)2=:π23(sup2ˉτθ0|ξ(θ+t)|), (3.20)

    where π23(s)=ˆΦ(λ)ˉτs2 is a class K function. Choosing π2=π21+π22+π23, one has

    π1(|ξ|)ˉVπ2(sup2ˉτθ0|ξ(θ+t)|). (3.21)

    According to (3.17), (3.21) and Lemma 2.1, one concludes that the equilibrium at the origin of closed-loop system is global asymptotic stabilization.

    Remark 3.2: In the existing atricles, backstepping method is a usually method to design state feedback controller. However, this method requires calculation step-by-step. For n-order systems, the nonlinear term must be calculated at each step. From the above calculation process, it can be seen that the form of controller u has been given. We only need to calculate the last two terms of (3.7). And then the appropriate parameter λ can be selected according to (3.16).

    As a matter of fact, the design scheme of section 3 can also be generalized to a class of strict-feedforward stochastic nonlinear systems with multiple time-variant delays in the following form

    ˙x1(t)=x2(t)+˜f1(x2(t),,xn(t),x2(tτ2(t)),,xn(tτn(t))),˙x2(t)=x3(t)+˜f2(x3(t),,xn(t),x3(tτ3(t)),,xn(tτn(t))),˙xn(t)=u(tτ1(t)), (4.1)

    where τj(t):R+[0,τ] is time-variant delay, τ>0,j=1,,n. Nonlinear functions ˜fi, i=1,,n1, are continuous.

    To obtain the stability theorem of system (4.1), we need the following assumptions.

    Assumption 4.1: There is a known positive constant ˉc such that

    |fj|ˉcnj=i+1(|xj(t)|+|xj(tτj(t))|),j=1,,n1.

    Assumption 4.2: For j=1,,n, there is a known constant β such that ˙τj(t)β<1.

    Similar to (3.2), system (4.1) can be rewritten as

    ˙x=Ax+Bu(tτ1(t))+˜F, (4.2)

    where

    ˜F=[˜f1()˜fn1()0].

    Then the extended result is summarized in the following theorem.

    Theorem 4.1: If Assumptions 4.1 and 4.2 hold for systems (4.1) and there exists a positive parameter λ such that ˉΨ(λ)˜Ψ(λ)1β2τˆΨ(λ)>0, then the equilibrium at the origin of closed-loop system is global asymptotically stable by adopting the parameter-dependent state feedback controller (3.3), where ˉΨ(λ), ˜Ψ(λ) and ˆΦ(λ) are defined in (4.8).

    Proof. We will prove it by two parts as well as Theorem 4.1.

    Part I: Using (3.4)-(3.5) and (4.2), the closed-loop system is transformed into

    ˙ξ=λ1AˉΘξ++Γ(λ)B(u(tτ1(t))u(t))+Γ(λ)˜F. (4.3)

    Choose the candidate Lyapunov function

    V1(ξ)=δ22ξQξ, (4.4)

    where δ2 is a positive constant and Q is a symmetric positive definite matrix that satisfies QAˉΘ+AˉΘQ=I. Applying (3.5) and (3.6), one has

    ˙V1δ2λ1|ξ|2+2δ2(ξQ)(Γ(λ)˜F)+2δ2(ξQ)Γ(λ)B(u(tτ1(t))u(t)). (4.5)

    Similar to inequality (3.9), according to Assumption 4.1, one has

    2δ2(ξP)(Γ(λ)˜F)n1Ψ1(λ)|ξ|2+Ψ1(λ)nj=2|ξ||ξj(tτj)|(c23(n1)2+n1)Ψ1(λ)|ξ|2+12c23Ψ1(λ)nj=1|ξj(tτj(t))|2, (4.6)

    where Ψ1(λ)=2δ2Qn1k=11λk and c3 is a positive real number.

    Similar to the proof of (3.13), using Assumptions 4.1, 4.2 and (3.4), one leads to

    2δ2(ξP)Γ(λ)B(u(tτ1(t))u(t))Ψ2(λ)(c24|ξ|2+2τ((n1j=1Ψ3,j)2+Ψ4(λ)2)c24tt2τni=1|ξi(σ)|2dσ), (4.7)

    where Ψ2(λ)=1λnδ2Pnj=1|Θi|, Ψ3,j(λ)=(ˉc+1)(1λ+nl=j+11λlj), Ψ4(λ)=1λnnj=1|Θi| and c4 is a positive real number. Combining (4.6) and (4.7), one leads to

    ˙V1{δ2λ1(c23(n1)2+n1)Ψ1(λ)c24ψ2(λ)}|ξ|2+12c23Ψ1(λ)nj=1|ξj(tτj)|2+2τc24Ψ2(λ)((n1j=1Ψ3,j)2+Ψ4(λ)2)tt2τnj=1ξ2j(σ)dσ=:ˉΨ(λ)|ξ|2+˜Ψ(λ)nj=1|ξj(tτj)|2+ˆΨ(λ)tt2τnj=1|ξj(σ)|2dσ. (4.8)

    Introduce the following L-K functional

    ˉV1=V1+˜Ψ(λ)1βni=2ttτj(t)|ξj(σ)|2dσ+ˆΨ(λ)tt2τtμnj=2|ξj(σ)|2dσdμ, (4.9)

    then

    ˙ˉV1(ˉΨ(λ)˜Ψ(λ)1β2τˆΨ(λ))|ξ|2=:ψ(λ)|ξ|2. (4.10)

    Taking ˜γ(s)=ψ(λ)s2 and applying ˉΨ(λ)˜Ψ(λ)1β2τΨ(λ)>0, then ˜γ(s) is a class K function and (3.16) is changed into

    ˙ˉV1˜γ(|ξ|). (4.11)

    Part II: Next, we verify that ˉV1 satisfies the first condition of Lemma 2.1.

    On the basis of |ξ|sup2τθ0|ξ(θ+t)| and (3.6), one has

    π3(|ξ|)V1π41(sup2τθ0|ξ(θ+t)|), (4.12)

    where π31(s)=δ22λ2min(Q)s2 and π41(s)=δ22λ2max(Q)s2 are class K functions.

    ˜Ψ(λ)1βnj=1ttτj(t)|ξj(σ)|2dσπ42(sup2τθ0|ξ(θ+t)|), (4.13)

    where π42(s)=˜Ψ(λ)1β is a class K function.

    ˆΨ(λ)tt2τtμnj=1|ξj(σ)|2dσdμπ43(sup2τθ0|ξ(θ+t)|), (4.14)

    where π23(s)=˜Ψ(λ)ˉτs2 is a class K function. Choosing π4=π41+π42+π43, one has

    π3(|ξ|)ˉV1π4(sup2τθ0|ξ(θ+t)|). (4.15)

    According to (4.12), (4.15) and Lemma 2.1, one concludes that the equilibrium at the origin of closed-loop system is global asymptotic stabilization.

    For the sake of verifying the effectiveness of the proposed controller, we consider the following numerical example.

    ˙x1(t)=x2(t)+110x2(t2)+13ln(1+x23(t)),˙x2(t)=x3(t)+15sin(x3(t1))),˙x3(t)=u(t1). (5.1)

    With the help of |sinx||x|, ln(1+x2)|x|, Assumption 3.1 is held with c=13.

    It is obvious that Assumption 3.1 holds. By system (5.1), A=[010001000],B=[001]. Choosing Θ1=38,Θ2=118,Θ3=52, one has AˉΘ=A+BˉΘ=[0100013811852]. According to PAˉΘ+AˉΘP=I, one obtains P=[3.02983.88691.33333.88698.67263.19051.33333.19051.4762]. Constructing the parameter-dependent controller

    u=38λ3x1118λ2x252λx3. (5.2)

    Define ξ=[ξ1ξ2ξ3]=[x1λx2λ2x3], consider the L-K functional ˉV=12ξPξ+0.258(1λ+2λ2+2λ3)3i=1ttτi|ξi(σ)|2dσ+0.0446(1λ+2λ2+4λ3)3i=1tt4μt|ξi(σ)|2dσdμ. According to the design procedure, one can deduce ˉΦ(λ)=0.258λ0.0516(1λ+1λ2)0.0671+λ2+λ4λ3, ˜Φ(λ)=0.021(1λ+1λ2), ˆΦ(λ)=0.00161+λ2+λ4λ3(48λ3+4.16λ2). Then ˉΦ(λ)˜Φ(λ)2ˉτˆΦ(λ)>0 by taking λ=2. Therefore, the condition of Theorem 3.1 is satisfied.

    In the simulation, we take the initial data x1(0)=1,x2=0.5,x3=0.5, Figure 1 demonstrates the effectiveness of the controller.

    Figure 1.  The responses of the closed-loop systems (5.1) and (5.2).

    By introducing the Lyapunov-Krasoviskii functional and applying the stability criterion on time-delay system, a novel parameter-dependent state feedback controller is proposed to guarantee the global asymptotic stabilization of strict-feedforward nonlinear systems with time delays in state and input.

    Some problems are still remained, e.g., 1) How to solve the problem of output feedback stabilization of the nonlinear strict-feedforward systems? 2) For the stochastic nonlinear systems with state and input delays, can we design the parameter-dependent state feedback controller?

    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 [grant number 62103175].

    All the authors declare no conflict of interest.



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