Research article Special Issues

Effects of initial vegetation heterogeneity on competition of submersed and floating macrophytes

  • Received: 05 June 2024 Revised: 16 July 2024 Accepted: 07 August 2024 Published: 08 October 2024
  • Non-spatial models of competition between floating aquatic vegetation (FAV) and submersed aquatic vegetation (SAV) predict a stable state of pure SAV at low total available limiting nutrient level, N, a stable state of only FAV for high N, and alternative stable states for intermediate N, as described by an S-shaped bifurcation curve. Spatial models that include physical heterogeneity of the waterbody show that the sharp transitions between these states become smooth. We examined the effects of heterogeneous initial conditions of the vegetation types. We used a spatially explicit model to describe the competition between the vegetation types. In the model, the FAV, duckweed (L. gibba), competed with the SAV, Nuttall's waterweed (Elodea nuttallii). Differences in the initial establishment of the two macrophytes affected the possible stable equilibria. When initial biomasses of SAV and FAV differed but each had the same initial biomass in each spatial cell, the S-shaped bifurcation resulted, but the critical transitions on the N-axis are shifted, depending on FAV:SAV biomass ratio. When the initial biomasses of SAV and FAV were randomly heterogeneously distributed among cells, the vegetation pattern of the competing species self-organized spatially, such that many different stable states were possible in the intermediate N region. If N was gradually increased or decreased through time from a stable state, the abrupt transitions of non-spatial models were changed into smoother transitions through a series of stable states, which resembles the Busse balloon observed in other systems.

    Citation: Linhao Xu, Donald L. DeAngelis. Effects of initial vegetation heterogeneity on competition of submersed and floating macrophytes[J]. Mathematical Biosciences and Engineering, 2024, 21(10): 7194-7210. doi: 10.3934/mbe.2024318

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  • Non-spatial models of competition between floating aquatic vegetation (FAV) and submersed aquatic vegetation (SAV) predict a stable state of pure SAV at low total available limiting nutrient level, N, a stable state of only FAV for high N, and alternative stable states for intermediate N, as described by an S-shaped bifurcation curve. Spatial models that include physical heterogeneity of the waterbody show that the sharp transitions between these states become smooth. We examined the effects of heterogeneous initial conditions of the vegetation types. We used a spatially explicit model to describe the competition between the vegetation types. In the model, the FAV, duckweed (L. gibba), competed with the SAV, Nuttall's waterweed (Elodea nuttallii). Differences in the initial establishment of the two macrophytes affected the possible stable equilibria. When initial biomasses of SAV and FAV differed but each had the same initial biomass in each spatial cell, the S-shaped bifurcation resulted, but the critical transitions on the N-axis are shifted, depending on FAV:SAV biomass ratio. When the initial biomasses of SAV and FAV were randomly heterogeneously distributed among cells, the vegetation pattern of the competing species self-organized spatially, such that many different stable states were possible in the intermediate N region. If N was gradually increased or decreased through time from a stable state, the abrupt transitions of non-spatial models were changed into smoother transitions through a series of stable states, which resembles the Busse balloon observed in other systems.



    The axially moving system is a significant part of mechanical system, which is widely used and plays a vital role in modern industry such as belts, string and so on. Involving precision electronic manufacturing is applied to electronics, machinery, automation and other directions [1,2,3]. However, there are also some problems. These structures, on account of their flexible features, which move in an axial direction, show oscillations when disturbances are present. Too much or too little vibration will have a negative effect on machining accuracy and work rate, even trigger a spectrum of unsafe accident occurrences. It is critical to emphasize that non-smooth input non-linearities such as saturation, clearance, hysteresis and dead zones are widespread in industrial automation systems, including mechanical, fluid technology, medical biology and physical systems [4,5,6,7]. As researcher's attention is increasingly focused on the work performance, the axially moving system has been widely concerned in recent decades.

    The axially moving structure is a typical class of infinite-dimensional distributed parameter systems. Most of the traditional control methods are designed based on truncated models, but unmodeled high-frequency modes may lead to spillover effects and affect the stability of system. Furthermore, if the control precision of the system is improved, the order of the controller will increase as the flexible mode increase, which is a difficult point. Boundary control can overcome the above disadvantages and is easy to implement. There are many studies on adaptive control and fuzzy control in ordinary differential equation systems [8,9,10,11,12,13]. There is also a great deal of research on partial differential systems. Among them, the control of the boundary building upon the infinite dimensional model of flexible structural systems has achieved fruitful research results [14,15,16,17,18,19,20,21,22]. In [14], an adaptive vibration control strategy with robustness is suggested for an axially moving beam system that experiences changing motion speeds. In [15], for Euler-Bernoulli beam system with input saturation, back-stepping technique is employed and a secondary system is created to offset input non-linearity for the purpose of attenuating vibrations. In [16], a formation control problem of multi-agent systems based on Volterra integral transformation is discussed. In [17], in an effort to suppress the 2D vibration of the Euler-Bernoulli beam, a plan for regulating boundaries is moved which is designed by the backstepping method to suppress the coupled vibration. In [18], neural network control and robust adaptive boundary control of the manipulator are the subject of discussion. In [19], an adaptable boundary management of the axial structure is studied under great acceleration/deceleration. In [20,21], the Lyapunov method is used to study the vibration suppression of axially moving strings under the perturbation of space-time tension and an unknown boundary. In [22], an interference detector is devised, employing a time-dependent gain, with the primary objective of preserving system stability in the presence of disruptions.

    However, all above-mentioned research results are developed without consideration of large A/D, input saturation and uncertainty of model parameters. The nonlinearity is usually due to physical constraints inherent in the dynamic system and constraints in the controllers that cannot be eradicated. Ignoring input nonlinearity in the system frame makes it severe to stabilize the actual axial motion system.

    Center outcomes of this study compared to the existing results can be generalized as follows:

    1) Large A/D have better flexibility which can ensure performance and reduce impact through the attenuation of acceleration in the starting stage.

    2) Actuator input saturation is considered. To overcome this issue, an auxiliary system is employed to make up for the inefficiency. Additionally, the interference observer is designed to monitor the system's boundary interference, which varies with time.

    3) In the practical field of engineering, many factors will lead to the uncertainty of system parameters, which cannot be directly used in the controller, so it is necessary to design parameters adaptive law to counterbalance the influence of parameters indeterminacy. To enhance practicality, we posit that every vital parameter of the closed-loop system is obscure, raising the challenge for this task.

    The subsequent portions of this document are organized as described below. In Section 2, we establish the dynamic restriction of the system utilizing the extended Hamilton's principle. Section 3 outlines the proposal of adaptable boundary regulation with an interference compensator, grounded in the Lyapunov theory, ensuring the consistent restriction of all states within the closed-loop system. Section 4 is dedicated to the presentation of numerical findings, while Section 5 represents the deductions.

    Notations: The following terms are defined in this paper.

    ()(x,t)=(),()x=()x,()t=()t,()xt=(())xt,()xx=2()x2,()tt=2()t2.

    In Figure 1, input saturation framework [23] is described as outlined below

    u(t)={sgn(u0(t))um,|u0(t)|umu0(t), |u0(t)|<um (1)
    Figure 1.  The diagram of input saturation model.

    Figure 2 displays a representative instance of the axial motion structure starting the coordinate system with its origin on the left side, and the controller input u(t) acting on the right end and in an upward direction, n(x,t) is the offset of structural at that moment, gc is the controller mass, d(t) is the end disturbance, q(x,t) is the distributed disturbance, b(t) is the motion velocity, r(t) is the accelerated/decelerated speed and h is the structure length.

    Figure 2.  The graphic portrayal of the axial motion structure.

    The algebraic expression of the system characterized by large A/D is expressed by the generalized Hamilton's principle [24]

    t2t1δ(Ek(t)Ep(t)+Wf(t)Wb(t))dt=0 (2)

    where δ is the variational operator; t1 and t2 is two moments, t1<t<t2 is the operation period; Ek and Ep are respectively kinetic energy and potential energy; δWf(t) is virtual work performed on non-conservative force; δWb(t) is imaginary momentum of the right limit.

    The system possesses kinetic energy, which is described as:

    Ek(t)=12gcn2t(h,t)+12gh0[nt(x,t)+b(t)nx(x,t)]2dx (3)

    where g is structure weight/unit length, the speed of motion is

    b(t)=b0+r(t)t (4)

    with b0 being initial velocity and r being motion acceleration/ deceleration.

    System potential energy is a function of

    Ep(t)=12Ph0n2x(x,t)dx (5)

    where P>0 is system tension.

    The virtual energy performed by the non-conservative force of the system can be expressed as

    δWf(t)=[u(t)dsnt(h,t)+d(t)]δn(L,t)+h0q(x,t)δn(x,t)dx sh0[nt(x,t)+b(t)nx(x,t)]δn(x,t)dx (6)

    where s is the viscous damping attenuation factor of the axially moving structure of acceleration/deceleration, ds represents actuator damping coefficient.

    The virtual momentum transmission at the right edge of the system is

    δWb(t)=gb(t)[nt(h,t)+b(t)nx(h,t)]δn(h,t) (7)

    By applying variational method with (3) and (5), integrating Eqs (6) and (7) by parts, we obtain

    t2t1δEk(t)dt=gct2t1ntt(h,t)δn(h,t)dt+gt2t1nt(h,t)δb(t)n(h,t)dt +gt2t1b2(t)nx(h,t)δn(h,t)dtgh0t2t1ntt(x,t)δn(x,t)dtdx gt2t1h0b(t)nxt(x,t)δn(x,t)dxdtgt2t1h0b2(t)nxx(x,t)δn(x,t)dxdt gh0t2t1(r(t)nx(x,t)+b(t)nxt(x,t))δn(x,t)dtdx (8)
    t2t1δEp(t)dt=Pt2t1nx(h,t)δn(h,t)dtPt2t1h0nxx(x,t)δn(x,t)dxdt (9)
    t2t1δWb(t)dt=t2t1gb(t)[nt(h,t)+b(t)nx(h,t)]δn(h,t)dt (10)
    t2t1δWf(t)dt=t2t1{[u(t)dsnt(h,t)+d(t)]δn(h,t)+h0q(x,t)δn(x,t)dx sh0[nt(x,t)+b(t)nx(x,t)]δn(x,t)dx}dt (11)

    Substituting (8)–(11) into (2), the system dynamics equation can be derived as

    gntt(x,t)+gr(t)nx(x,t)+2gb(t)nxt(x,t)+gb2(x,t)nxx(x,t)Pnxx(x,t)+s(nt(x,t)+b(t)nx(x,t))q(x,t)=0 (12)

    where (x,t)(0,h)×[0,+).

    The system's boundary restriction is

    {n(0,t)=0gcntt(h,t)+Pnx(h,t)u(t)d(t)+dsnt(h,t)=0 (13)

    where t[0,+).

    In the course of controller design and the examination of stability, we make use of the following lemmas, assumptions and properties.

    Lemma 1 [25,26,27]: If ϕ1(x,t), ϕ2(x,t)R, σ>0 and (x,t)[0,h]×[0,+), the following properties hold

    {ϕ1ϕ2|ϕ1ϕ2|ϕ21+ϕ22,ϕ1,ϕ2R|ϕ1ϕ2|=|(1σϕ1)(σϕ2)|1σϕ21+σϕ22 (14)

    Lemma 2 [25,26,27]: If ψ(x,t)R be a function with x[0,h], t[0,+) and it is subject to the following boundary condition

    ψ(0,t)=0 (15)

    The ensuing properties are applicable

    {h0ψ2dxh2h0ψ2xdxψ2hh0ψ2xdx (16)

    Assumption 1: We hypothesize the presence of constants , such as to any time scale, q(x,t)Q, any temporal and spatial scope, which is justifiable since motion velocity b(t), r(t), d(t) and q(x,t) possess restricted energy.

    Assumption 2: The assumption is made that the time rate of change for unspecified perturbations dt at the boundary is uniformly restricted, where there exists a constant , such that arbitrary time scale.

    Property 1 [28]: Given that the kinetic energy, as specified in (3) is capped any time, both nt and nxt are restricted within set boundaries any temporal and geographic range.

    Property 2 [29]: In the case where the potential energy, as defined in Eq (4), is restricted any temporal and geographic range, both nx and nxx are restricted as well any temporal and spatial scope.

    In the context of the axially moving model governed by control equation (12) and boundary condition (13), we put forward the ensuing adaptive boundary control scheme to achieve system stabilization under circumstances involving unknown system structural parameters P, gc, ds and input saturation.

    u0(t)=l1[nt(h,t)+l3nx(h,t)]l3ˆgcnxt(h,t)+ˆPnx(h,t)+ˆdsnt(h,t) ˆd(t)+l4τ(t) (17)

    where l1,l3,l4>0 are control gains, ˆgc, ˆP, ˆds, ˆd(t) are the estimators of gc, P, ds, d(t) respectively.

    The interference observer is designed as

    ˆdt(t)=κσˆd(t)+σ[nt(h,t)+l3nx(h,t)] (18)

    The corresponding estimated error is

    {˜P=PˆP˜gc=gcˆgc˜ds=dsˆds˜d=dˆd (19)

    where κ, σ being positive constants.

    The adaptive control law is designed as

    {ˆPt=μ1ξ1ˆPξ1nx(h,t)[nt(h,t)+l3nx(h,t)]ˆgct=μ2ξ2ˆgc+ξ2l3nxt(h,t)[nt(h,t)+l3wx(h,t)]ˆdst=μ3ξ3ˆdsξ3nt(h,t)[nt(h,t)+l3wx(h,t)] (20)

    where μ1, μ2, μ3, ξ1, ξ2 and ξ3 are all non-negative values.

    Derivative of (18) and (20), together with (19), we have

    ˙˜d=˙dσ[nt(h,t)+l3nx(h,t)]+σκˆd (21)

    and

    {˜Pt=μ1ξ1ˆP+ξ1nx(h,t)[nt(h,t)+l3nx(h,t)]˜gct=μ2ξ2ˆgcξ2l3nxt(h,t)[nt(h,t)+l3nx(h,t)]˜dst=μ3ξ3ˆds+ξ3nt(h,t)[nt(h,t)+l3nx(h,t)] (22)

    In order to eliminate saturation, we designate the secondary system as

    τt(t)={Δul2τ[nt(h,t)+l3nx(h,t)]Δu+0.5(Δu)2τ,|τ|τ00, |τ|<τ0 (23)

    where l2,l3>0, Δu=u(t)u0(t), τ0 is a tiny positive design factor, τ is the status of the secondary system.

    Remark 1: The displacement of the boundary n(h,t) can be monitored by the position transducer, and the inclination angle of the boundary nx(h,t) can be gauged via the inclinometer. For the signal nt(h,t) and nxt(h,t) are result of backward difference calculation.

    Assumption 3 [27]: Assuming that the axially moving system illustrated by the control equation (12) and the boundary conditions (13) with adaptive boundary control (17) is well-posed.

    Select the Lyapunov function as

    V(t)=V1(t)+V2(t)+V3(t)+V4(t) (24)

    among which, the energy term V1, a small crossing term V2, the addition item V3, the error term V4 are expressed as follows, respectively.

    V1=12γgh0[nt(x,t)+bnx(x,t)]2dx+12γPh0n2x(x,t)dx (25)
    V2=2λgh0xnx(x,t)(nt(x,t)+bnx(x,t))dx (26)
    V3=12ηga[nt(h,t)+l3nx(h,t)]2+12ητ(t)2 (27)
    V4=12ςdη˜d2(t)+12ξ1η˜P2+12ξ2η˜ga2+12ξ3η˜ds2 (28)

    where γ, λ, η are positive weight coefficients, σ, ξ1, ξ2, ξ3 are defined as in (18) and (20).

    Lemma 3. The Lyapunov function (24) is bounded from above and below.

    0θ1[V1(t)+V3(t)+V4(t)]Vθ2[V1(t)+V3(t)+V4(t)] (29)

    where θ1, θ2 are supportive variables.

    Proof: According to Lemmas 1 and 2, it follows that

    |V2(t)|λghh0n2x(x,t)dx+λghh0[nt(x,t)+b(t)nx(x,t)]2dxεV1(t) (30)

    where ε satisfies the condition

    ε=2λghmin(γg,γP) (31)

    Choosing appropriate parameters λ and γ satisfies that

    {ε1=1ε=12λghmin(γg,γP)>0ε2=1+ε=1+2λghmin(γg,γP)>1 (32)

    Since 0<ε<1, which implies that

    λ<min(γg,γP)2gh (33)

    Substituting (32) into (30) gives

    0<ε1V1(t)V1(t)+V2(t)ε2V1(t) (34)

    Therefore, we conclude that

    0θ1[V1(t)+V3(t)+V4(t)]Vθ2[V1(t)+V3(t)+V4(t)] (35)

    where θ1=min(ε1,1), θ2=max(ε2,1).

    Lemma 4. The Lyapunov function (24)'s alteration velocity relative to time has upper bound

    Vt(t)θV(t)+ε (36)

    where υ>0, ε>0.

    Proof: According to (25), we have

    V1t(t)=A1+A2+A3+A4 (37)

    where

    A1=γgh0[nt(x,t)+bnx(x,t)]ntt(x,t)dx,
    A2=γgh0[rnt(x,t)nx(x,t)+brn2x(x,t)]dx,
    A3=γgh0[bnt(x,t)nxt(x,t)+bnx(x,t)nxt(x,t)]dx,
    A4=γPh0nx(x,t)nxt(x,t)dx

    Integrating A3 and A4 by parts, using Lemma 1, substituting them into (37), we get

    V1t(t)(γsγδ1)h0[nt(x,t)+bnx(x,t)]2dx+γδ1h0q2(x,t)dx γb2(Pgb2)n2x(0,t)γgb2[nt(h,t)+bnx(h,t)]2 +γP2l3[nt(h,t)+l3nx(h,t)]2γP2l3n2t(h,t)γP2(l3b)n2x(h,t) (38)

    where δ1>0.

    According to (26), we have

    V2t(t)=2λgh0xnxt(x,t)[nt(x,t)+bnx(x,t)]dx +2λgh0xnx(x,t)[ntt(x,t)+rnx(x,t)+bnxt(x,t)]dx (39)

    Performing integration by parts, by virtue of Lemma 1and Lemma 2, we deduce that

    (40)

    where δ2,δ3>0.

    With the help of Lemmas 1 and 2, together with (17), (18) and (23) yields

    V3t(t)η(l1l42)[nt(h,t)+l3nx(h,t)]2η(l4l3212)τ2(t)+η[nt(h,t)+l3nx(h,t)][l3˜ganxt(h,t)˜Pnx(h,t)˜dsnt(h,t)+˜d(t)] (41)

    Similarly, from (28), we get

    V4t(t)η2(γ1P2+γ2ga2+γ3ds2+1σδ4d2t+κd2) η2(γ1˜P2+γ2˜ga2+γ3˜ds2)+η[nt(h,t)+l3nx(h,t)][˜Pnx(h,t)l2˜ganxt(h,t)+˜dsnt(h,t)˜d(t)] (42)

    Thus, one can obtain from (38), (40), (41) and (42) that

    (43)

    where the parameters κ, σ, λ, η, δ1, δ2, δ3, Ω1 to Ω5 are selected to fulfill the following requirements:

    (44)

    Putting (44) into (43), combined with (25) to (29) leads to

    Vt(t)η2(γ1˜P2+γ2˜ga2+γ3˜ds2)Ω1h0[nt(x,t)+bnx(x,t)]2dx Ω2h0n2x(x,t)dxη(l4l3212)τ2(t)+(2λhδ2+γδ1)Qh Ω3[nt(h,t)+l3nx(h,t)]2Ω4˜d2Ω5τ2(t)+ε θ3[V1(t)+V(t)3+V4(t)]+ε θV(t)+ε (45)

    among which

    θ=(θ3/θ2),
    θ3=min(2Ω1γg,2Ω2γP,2Ω3ηga,2Ω4ςd,2Ω5ητ,ξ1γ1,ξ2γ2,ξ3γ3),ε=(2λhδ2+γδ1)Qh+η2(γ1P2+γ2ga2+γ3ds2+1σδ4d2t+κd2)<+.

    Based on the above analysis, we shall prove stability theorems.

    Theorem 1: In the case of the axially moving system described by (12) and (13), given that the initial states are limited and the inequalities outlined in (44) are existed by selecting suitable parameters, in accordance with Assumptions 1 and 2, the planned controller (17) and the adaptation rules (18), (20), give rise to the following findings:

    1) Uniformly boundedness: The variables of a closed-loop axial motion structure remain within a limited range.

    M1:={n(x,t)R||n(x,t)|χ1}

    where (x,t)[0,h]×[0,+), χ1=2hγPθ1[V(0)eθt+εθ].

    2) Ultimately evenly restrained: Dynamic variables of a closed-loop axial motion structure ultimately merge into a condensed quantity.

    M2:={n(x,t)R|limt|n(x,t)|χ2}

    where x[0,h], χ2=2hεγPθ1θ.

    Proof: Multiplying (45) by eθt, we have

    Vt(t)eθtθV(t)eθt+εeθtt[V(t)eθt]εeθt

    Thus, direct calculations yield that

    V(t)V(0)eθt+εθ (46)

    In that case, one can deduce that V(t) remains within limits due to the constrained nature of the initial values.

    Combining with (16), (24), (25), (29), one has

    γP2hn2(x,t)γP2h0n2x(x,t)dxV1(t)V1(t)+V3(t)+V4(t)1θ1V(t) (47)

    According to (46), (47), we have

    |n(x,t)|2hγPθ1[V(0)eθt+εθ] (48)

    where (x,t)[0,h]×[0,+).

    From (48), we additionally obtain

    limt|n(x,t)|limt2hγPθ1[V(0)eθt+εθ]=2hεγPθ1θ (49)

    where x[0,h].

    Remark 2: Based on (46), (47), we are able to achieve that V1(t)V4(t) are bounded. Kinetic Ek(t) and positional energy Ep(t) are then bounded, we can use Property 1 and 2 to summarize nt, nxt, nx and nxx are bounded within the stipulated time and area. From the boundedness of V4(t), it is easy to see ˜P, ˜gc, ˜ds and ˜d are bounded. According to the definition of u0(t), one obtains that u(t) is bounded. Integrating Eq (12) with the earlier statements, ntt is also bounded within the given time and territory. This guarantees the practicability of the moved control strategy and all signals of the closed-loop system are bounded.

    Through simulation instances, this portion demonstrates the soundness of the proposed adaptive algorithm. Table 1 details the system specifications. The specified values for Sc-A/D include peak A, peak D and time scale: rA=rD=3.5G, tm=1s,2s,3s,7s,8s,9s,10s(m=17). The following variables are used to control l1=104, l2=l3=100, κ=0.01, σ=106, ξ1=ξ2=ξ3=1, γ1=γ2=γ3=0.1. τ0=0.001. Original setup of the structure in following manner n(x,0)=nt(x,0)=0. The disruptions are in the form of

    {q(x,t)=0.00001x[1+cm=1sin(mπxt)],m=1,2,3d(t)=3+0.1cm=1msin(mt),m=1,2,3 (50)
    Table 1.  Variables of the axially shifting conveyor system.
    Variable Magnitude
    g 1.0kg/m
    gc 5.0kg
    h 1.0m
    s 1.0Ns/m2
    ds 0.25Ns/m
    G 9.8N/kg
    P 5000N
    b0 0

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    Figures 3 and 4 show the displacement of the system without control in constant speed and large acceleration/deceleration under scattered perturbation and border interference. The deflection of the system is illustrated in Figures 5 and 6, both of which represent scenarios with the presented control (17) under constant speed and large acceleration/deceleration, all while facing the same external conditions. The oscillatory displacements of the system at the mid and the end with and without control are shown in Figure 7. Figure 8 shows the simulation results of the unregulated and regulated reactions. Figure 9 provides a time-based comparison between prescribed input u0(t) and non-linear input u(t). The interference tracking result and the estimation of error are given in Figure 10.

    Figure 3.  Oscillatory motion of the system under a fixed speed without regulation.
    Figure 4.  Uncontrolled system vibrations under conditions of high acceleration/deceleration.
    Figure 5.  System's vibrational response with the recommended control under a steady speed.
    Figure 6.  System's oscillations under the provided control during rapid acceleration/deceleration.
    Figure 7.  Oscillatory motion of the structure at border and center.
    Figure 8.  Magnify the perspective of oscillatory excursion.
    Figure 9.  The proffered adaptive boundary control input u0(t). Suggested adaptive limit saturated control signal u(t).
    Figure 10.  A tracing diagram of boundary interference. The tracing error diagram of boundary interference.

    We consider vibration suppression of the structure with large A/D and uncertain structural parameters in the context of interference and saturation constraints. Pursuant to the dynamic model of infinite dimensional partial differential equation, Lyapunov theory, Sc A/D method, adaptive technology and compensation system are designed to sort out saturation, an adaptive boundary regulator is developed, positioned on the rightmost side, which can quell the oscillation of the structure. The adaptive controller used cannot only solve the control overflow problem caused by the truncated reduced order model, but also balance for the imprecision of system structural parameters and the limitation of saturation. Therefore, the regulator designed has good robustness and adaptability, and verifies that the propounded system is stable and exhibits uniform boundedness. The numerical simulation of the proffered algorithm is implemented, and the simulation information prove that the moved directive algorithm is reliable. In future studies, we plan to study the influence of more non-linear inputs on the system and considerate practical experiments on actual systems to scrutinize the functionality of recommended control strategy.

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

    This work is supported by the National Natural Science Foundation of China (No. 62273171 and No.62203200) and the Natural Science Foundation of Liaoning Province (No.2021-MS-318).

    The authors declare there is no conflict of interest.



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