Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Bifurcation analysis and exact solutions for a class of generalized time-space fractional nonlinear Schrödinger equations


  • In this work, we focus on a class of generalized time-space fractional nonlinear Schrödinger equations arising in mathematical physics. After utilizing the general mapping deformation method and theory of planar dynamical systems with the aid of symbolic computation, abundant new exact complex doubly periodic solutions, solitary wave solutions and rational function solutions are obtained. Some of them are found for the first time and can be degenerated to trigonometric function solutions. Furthermore, by applying the bifurcation theory method, the periodic wave solutions and traveling wave solutions with the corresponding phase orbits are easily obtained. Moreover, some numerical simulations of these solutions are portrayed, showing the novelty and visibility of the dynamical structure and propagation behavior of this model.

    Citation: Baojian Hong. Bifurcation analysis and exact solutions for a class of generalized time-space fractional nonlinear Schrödinger equations[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14377-14394. doi: 10.3934/mbe.2023643

    Related Papers:

    [1] Tongyu Wang, Yadong Chen . Event-triggered control of flexible manipulator constraint system modeled by PDE. Mathematical Biosciences and Engineering, 2023, 20(6): 10043-10062. doi: 10.3934/mbe.2023441
    [2] Yunxia Wei, Yuanfei Zhang, Bin Hang . Construction and management of smart campus: Anti-disturbance control of flexible manipulator based on PDE modeling. Mathematical Biosciences and Engineering, 2023, 20(8): 14327-14352. doi: 10.3934/mbe.2023641
    [3] Xingjia Li, Jinan Gu, Zedong Huang, Chen Ji, Shixi Tang . Hierarchical multiloop MPC scheme for robot manipulators with nonlinear disturbance observer. Mathematical Biosciences and Engineering, 2022, 19(12): 12601-12616. doi: 10.3934/mbe.2022588
    [4] Kangsen Huang, Zimin Wang . Research on robust fuzzy logic sliding mode control of Two-DOF intelligent underwater manipulators. Mathematical Biosciences and Engineering, 2023, 20(9): 16279-16303. doi: 10.3934/mbe.2023727
    [5] Yongli Yan, Fucai Liu, Teng Ren, Li Ding . Nonlinear extended state observer based control for the teleoperation of robotic systems with flexible joints. Mathematical Biosciences and Engineering, 2024, 21(1): 1203-1227. doi: 10.3934/mbe.2024051
    [6] Tianqi Yu, Lei Liu, Yan-Jun Liu . Observer-based adaptive fuzzy output feedback control for functional constraint systems with dead-zone input. Mathematical Biosciences and Engineering, 2023, 20(2): 2628-2650. doi: 10.3934/mbe.2023123
    [7] Balázs Csutak, Gábor Szederkényi . Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation. Mathematical Biosciences and Engineering, 2025, 22(1): 109-137. doi: 10.3934/mbe.2025006
    [8] Siyu Li, Shu Li, Lei Liu . Fuzzy adaptive event-triggered distributed control for a class of nonlinear multi-agent systems. Mathematical Biosciences and Engineering, 2024, 21(1): 474-493. doi: 10.3934/mbe.2024021
    [9] Xinyu Shao, Zhen Liu, Baoping Jiang . Sliding-mode controller synthesis of robotic manipulator based on a new modified reaching law. Mathematical Biosciences and Engineering, 2022, 19(6): 6362-6378. doi: 10.3934/mbe.2022298
    [10] Xingjia Li, Jinan Gu, Zedong Huang, Wenbo Wang, Jing Li . Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators. Mathematical Biosciences and Engineering, 2022, 19(9): 9371-9387. doi: 10.3934/mbe.2022436
  • In this work, we focus on a class of generalized time-space fractional nonlinear Schrödinger equations arising in mathematical physics. After utilizing the general mapping deformation method and theory of planar dynamical systems with the aid of symbolic computation, abundant new exact complex doubly periodic solutions, solitary wave solutions and rational function solutions are obtained. Some of them are found for the first time and can be degenerated to trigonometric function solutions. Furthermore, by applying the bifurcation theory method, the periodic wave solutions and traveling wave solutions with the corresponding phase orbits are easily obtained. Moreover, some numerical simulations of these solutions are portrayed, showing the novelty and visibility of the dynamical structure and propagation behavior of this model.



    Flexible manipulators, as autonomous robots, possess the capability to move and execute tasks independently without direct human intervention, as highlighted in references such as [1,2,3]. These robots are typically equipped with a range of functionalities, including autonomous navigation, environmental sensing, decision-making and execution capabilities. These features enable them to operate autonomously, navigate diverse environments, interact with their surroundings and accomplish predefined tasks. The applications of flexible manipulators are extensive, spanning various domains such as industrial automation, smart homes, agriculture, field management, exploration and search and rescue operations. They contribute to increased work efficiency, reduced labor requirements, and the ability to handle hazardous and challenging tasks effectively.

    Attitude tracking control of flexible manipulators or nonlinear systems has always been a hot research topic [4,5,6]. To address these issues, researchers have proposed various methods in the control of flexible manipulators. In [7], a disturbance observer is designed to estimate the presence of external disturbances in a flexible manipulator. Additionally, they utilize H control based on specified performance and iterative learning control techniques to address both the vibration and inertia uncertainties of the flexible manipulator while achieving good tracking performance. In [8], the study addressed the vibration suppression and angle tracking issues of a flexible unmanned aerospace system with input nonlinearity, asymmetric output constraints and uncertain system parameters. Utilizing inversion techniques, a boundary control scheme was developed to mitigate vibrations and adjust the spacecraft's angle. Simulations also demonstrated the strong robustness of this approach. In [9], researchers address the challenge of tracking desired motion trajectories within an underwater robot-manipulator system, particularly when direct velocity feedback is unavailable. To tackle this issue, a comprehensive controller-observer scheme is developed, leveraging an observer to estimate the system's velocity. This innovative approach not only achieves exponential convergence in motion tracking but also ensures a simultaneous convergence in estimation errors. Different from the References [7] and [9], Reference [10] proposes a finite-time trajectory tracking controller for space manipulators. In this context, a radial basis function neural network is utilized to both estimate and compensate for the uncertain model of the space manipulator, especially when dealing with the capture of unknown loads. An auxiliary system is designed to mitigate actuator saturation. In [11], a new adaptive control law is proposed to solve the terminal tracking problem of underwater robot-manipulator systems. In addition, using the unit quaternion to represent attitude overcomes the problem of kinematic singularity. The primary objective of this controller is to guarantee the convergence of tracking errors to zero, even in the presence of uncertainties. Furthermore, it is designed to maintain system stability and achieve satisfactory tracking performance, even when operating under underactuated conditions [12]. In essence, the proposed control strategy provides a robust and effective means to ensure precise tracking performance for the aerial manipulator system, even when facing uncertainties and underactuation challenges.

    In practical control systems, one of the most common nonlinear challenges arises from the physical characteristics of actuators, specifically, their limited output amplitude. This issue is known as the input saturation problem [13,14,15,16,17]. Furthermore, limitations in actuator control inputs refer to the existence of input restrictions in the control system of the flexible manipulator system [18,19]. This means that the actuators of the flexible manipulator, such as motors or hydraulic cylinders, may have limitations and cannot provide an infinite amount of force or speed. This affects the response time and control accuracy of the flexible manipulator. In [18], under the constraints of external disturbance and asymmetric output, a boundary control law with disturbance observer is constructed to suppress vibration and adjust the position of the flexible manipulator. As we all know, due to the high-dimensional characteristics of the flexible manipulator and its complex modeling, many scholars often build the flexible manipulator into a dynamic model with partial differential equations (PDEs) [20,21]. In [22], the dynamic model of a three-dimensional flexible manipulator is established using the Hamiltonian principle, resulting in a set of PDEs. Moreover, the designed control algorithm enables joint angle control and manages external disturbances even when the controller becomes saturated. In [23], the study investigates the vibration suppression and angular position tracking problems of a robotic manipulator system composed of a rotating hub and a variable-length mechanical arm. To achieve precise dynamic responses, a PDE modeling approach is employed for the manipulator system. Furthermore, two boundary control laws are proposed to achieve vibration suppression and angular position tracking for the robotic arm system. This research methodology is quite novel and intriguing. Similar to [22], the dynamics of high-dimensional flexible manipulator is expressed by PDEs. In addition, [24] designs an adaptive law to compensate uncertainty and disturbance, while meeting physical conditions and input constraints. In [25], under the inverse control algorithm, a design scheme for adaptive fuzzy tracking control based on observers is proposed to address the issues of system saturation and nonlinearity in the operation arm of a single-link robot. The main contribution of [26] is the design of a controller based on disturbance observer to regulate the joint angular position and quickly suppress the vibration of the beam, considering the presence of input saturation in the robotic system. Theoretical analysis demonstrates the asymptotic stability of the closed-loop system, and numerical simulations validate the effectiveness of the proposed approach. In [27], this study addresses the problem of asymptotic tracking for high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. It introduces a novel transformation method that converts the original nonaffine nonlinear system into an equivalent affine one. The controller design utilizes online approximators and Nussbaum gain techniques to handle unknown dynamics and unknown control coefficients in the affine system. It is rigorously proven that the proposed control method ensures asymptotic convergence of the tracking error and ultimate uniform boundedness of all other signals. The feasibility of the control approach is further confirmed through numerical simulations.

    Taking inspiration from the references mentioned earlier, this paper seeks to achieve precise state tracking for a single-joint manipulator in the presence of external disturbances and constrained control inputs. To address these challenges, we introduce a novel control framework grounded in the backstepping methodology. Simultaneously, we employ the Nussbaum function method to effectively manage the limitations associated with control inputs. The primary contributions of this paper are the following:

    1. Different from other control methods to deal with disturbance [28,29,30], in order to achieve accurate tracking of the system state and effectively combat external disturbances, we employ the backstepping method [31,32] as a central control strategy. This method aims to enhance tracking precision and resilience to disturbances.

    2. Furthermore, recognizing the constraints imposed by control input limitations in the flexible manipulator's actuator control system, we introduce a design approach centered on the Nussbaum function [33,34]. This innovative method is implemented to overcome these limitations, enabling robust control even within these constraints.

    3. Finally, the effectiveness and disturbance rejection capabilities of the proposed control strategy are substantiated through numerical comparative simulations conducted in MATLAB/Simulink. These simulations offer empirical evidence of the strategy's reliability, emphasizing its potential to address challenges related to external disturbances and control input limitations in the context of flexible manipulator control.

    The subsequent sections of this paper are structured as follows: Section 2 establishes the dynamic model of the flexible manipulator and provides certain lemmas. Section 3 introduces the control algorithm based on the backstepping method and the Nussbaum function. In Section 4, we perform numerical comparative simulations using MATLAB/Simulink to further validate the robustness and disturbance rejection performance of the proposed control method. Finally, Section 5 serves as the conclusion of this paper.

    A single-joint flexible robotic arm consists of components including a transmission system, sensors, a controller, a power supply system and an outer casing with connecting elements. The controller serves as the intelligent core of the single-joint flexible robotic arm, enabling it to achieve bending, twisting and rotating motions, and it is used in various applications. The research object is a single-link flexible manipulator that moves horizontally, which as shown in Figure 1. From Figure 1, we can see that there is a u(t) with limited input and external disturbances d(t) at the end of the flexible manipulator. In the absence of gravitational effects, XOY represents the inertial coordinate system, while xOy serves as the follower coordinate system.

    Figure 1.  The structural schematic diagram of flexible manipulator.

    For the convenience of controller design, the single-joint flexible manipulator can be simplified as the following controlled object:

    ¨θ=1I(2˙θ+mgLcosθ)+1Iτ(t). (2.1)

    Let x1=θ, x2=˙θ and set f(x)=1I(2x2+mgLcosx1), 1Iτ(t)=u(t), then the controlled object can be written as:

    {˙x1=x2.˙x2=F(x)+u(t)+d(t). (2.2)

    where, I is the moment of inertia, u(t) represents the control input, d(t) represents the disturbance and F(x) represents the nonlinear function.

    Remark 1: In this paper, the external disturbance d(t) acting on the flexible manipulator is assumed to be equivalence bounded and satisfies |d(t)|D. In practical control systems of flexible manipulators, the existence of control input u(t) constraints may lead to system divergence and loss of control. The control input constraint problem is a research focus. Therefore, the issue of control input constraints in the system will be investigated in the following sections.

    Lemama 1 (see [35]): For a function V:[0,)R and an inequality equation ˙VαV+f,tt00, the solution is given by

    V(t)eα(tt0)V(t0)+tt0eα(tτ)f(τ)dτ, (2.3)

    where α is an arbitrary constant.

    Lemama 2 (see [36]): Let Ξ:[0)Rtt00, if ˙ΞςΞ+, then

    Ξ(t)eς(tt0)Ξ(t0)+tt0eς(ts)(s)ds, (2.4)

    where ς>0.

    Lemama 3 (see [37]): For kb>0, if the following inequality holds, then |x|<cb:

    lncTbcbcTbcbxTxxTxcTbcbxTx. (2.5)

    Consider the following hyperbolic tangent smoothing function:

    ω(χ)=uMtanh(χuM)=uMeχ/ueχ/uMeχ/uM+eχ/uM. (2.6)

    The function has the following four properties:

    |ω(χ)|=uM|tanh(χuM)|uM, (2.7)
    0<ω(χ)χ=4(eχ/uM+eχ/uM)21, (2.8)
    |ω(χ)χ|=|4(eχ/uM+eχ/uM)2|1, (2.9)
    |ω(χ)χχ|=|4χ(eχ/uM+eχ/uM)2|uM2. (2.10)

    Remark 2: According to Figure 2, it can be observed that using a hyperbolic tangent smooth function can achieve bounded control input. For example, according to a Theorem in [38], using a hyperbolic tangent smooth function as a direct control law can achieve global asymptotic stability of the closed-loop system. However, this method is only suitable for the case when Eq (2.1) has F(x)=0 and d(t)=0. Building upon the work in [15,39], the following method presents a control algorithm for managing the input of a single-input single-output nonlinear system with the model structure given in Eq (2.1) when the control input is limited.

    Figure 2.  The schematic diagram of the hyperbolic tangent smoothing function and switching function.

    Remark 3: The Nussbaum function serves as a valuable mathematical tool for managing control input constraints, particularly in systems characterized by bounded control inputs. When compared to alternative methods for addressing control input saturation, such as saturation functions (as discussed in [40]), feedback linearization (as outlined in [41]) and dynamic output feedback (as explored in [42]), control laws based on the Nussbaum function offer the advantage of guaranteeing global asymptotic stability of the system. In simpler terms, regardless of the system's initial conditions, employing Nussbaum function-based control laws ensures that the system will converge to the desired equilibrium point, making it an ideal property in control systems. To sum up, the Nussbaum function provides an effective and robust approach for managing control input constraints, ensuring global stability and convergence of the control system, even when faced with bounded control inputs. Consequently, in order to address control input constraints in the context of robotic control systems, this paper has adopted an approach grounded in the Nussbaum function.

    This paper addresses challenges related to external disturbances and control input constraints in the context of a flexible manipulator control system. We propose a control strategy that combines the Nussbaum function and the backstepping method, as illustrated in Figure 3. This strategy is designed to ensure the stability of the system and to achieve accurate tracking of the system states, as represented by Eq (2.2). In this paper, let yd represent the reference signal, and the primary control objective is to guarantee that the control input u(t) remains bounded, specifically |u(t)|umax. Additionally, as time t tends toward infinity, our aim is for the system states x1 to converge to yd and x2 to converge to ˙yd. This dual objective of input saturation control and asymptotic tracking is fundamental to our approach.

    Figure 3.  The schematic diagram of control system structure.

    In order to satisfy |u(t)|umax, the control law is designed as follows

    u(t)=(χ)=umaxtanh(χumax), (3.1)

    where umax represents the maximum value of the control input u(t).

    Then, the design task of the control law is transformed into the design of (χ), that is, the design of χ.

    The auxiliary system with stable design is

    ˙χ=χmaxtanh(ωχmax)(χ)1=(χ)1f(I), (3.2)
    ˙=(f())1U, (3.3)

    where f()=χmaxtanh(χmax), χmax, χ, and U are auxiliary control signals.

    Therefore, have

    ˙u(t)=χ˙χ=χmaxtanh(χχmax)=f(), (3.4)

    where |˙u(t)|χmax the design task of the control law is transformed into the design of .

    Remark 4: The advantage of backstepping control lies in its ability to handle nonlinear systems, unknown disturbances and parameter uncertainties, while exhibiting strong robustness and adaptability. Compared to other control methods, backstepping control offers design flexibility, robustness and adaptability to nonlinear systems. Therefore, in this paper, the control strategy based on backstepping is chosen to enhance the robustness against external disturbances.

    Remark 5: Taking into account that the next controller design contains yd and its first to third derivatives (˙yd,¨yd,yd), the corresponding assumptions are made. In our work, we assume that yd and its first to third derivatives (˙yd,¨yd,yd) are bounded, and the exact values of these derivatives can be obtained. This assumption is to ensure that our controller design is feasible for practical application and can provide reliable performance.

    The basic design steps of the inversion control method are:

    Step 1: Define the position error as

    e1=x1yd (3.5)

    Taking the derivative of Eq (3.5) with respect to time yields:

    ˙e1=˙x1˙yd=x2˙yd. (3.6)

    Define

    e2=x2τ1˙yd. (3.7)

    Define virtual control quantity

    τ1=b1e1, (3.8)

    where b1>0.

    Then

    e2=x2+b1(x1yd)˙yd. (3.9)

    Select Lyapunov function as

    L1=12e21. (3.10)

    Along with the trajectories of Eq (3.10), it can be shown that

    ˙L1=e1˙e1=e1(x2˙yd)=e1(e2+τ1). (3.11)

    Substituting the Eq (3.8) into the Eq (3.11), it can be obtained that

    ˙L1=b1e21+e1e2. (3.12)

    If e2=0, then ˙L10. To achieve this, the next step of the design is needed.

    Step 2: Define the Lyapunov function as

    L2=L1+12e22. (3.13)

    Then

    ˙e2=˙x2˙τ1¨yd=F(x)+(χ)+d¨yd˙τ1. (3.14)

    Remark 6: If we follow the traditional backstepping design method [43,44,45] for the control law designed based on the above equation, u(t) can not be guaranteed to be bounded. In order to achieve bounded control input, we introduce a virtual term τ2 to design u(t). Specifically, we let e3=(χ)τ2, which further leads to

    ˙e2=F(x)+τ2+e3+d˙τ1¨yd. (3.15)

    Then

    ˙L2=˙L1+e2˙e2=b1e21+e1e2+e2(F(x)+e3+τ2+d˙τ1¨yd). (3.16)

    The virtual control law is defined as

    τ2=e1b2e2F(x)+˙τ1+¨ydη1tanh(e2b1), (3.17)

    where b2>0.

    Subsequently

    ˙L2=b1e21b2e22+e2e3+e2de2η1tanh(e2ε1). (3.18)

    Since e2d|e2d|η1|e2|, then

    e2de2η1tanh(e2ε1)η1(|e2|e2tanh(e2ε1))η1kuε1, (3.19)

    where

    0|e2|e2tanh(e2ε1)kuε1,ku=0.2785. (3.20)

    Therefore

    ˙L2=b1e21b2e22+e2e3+η1kuε1. (3.21)

    From the τ2 expression, Eq (3.20) can be obtained

    τ2=(x1yd)b2(x2+b1(x1yd)yd)F(x)b1(x2˙yd)+¨ydη1tanh(x2+b1(x1yd)˙ydε1). (3.22)

    It can be seen that τ2 is a function of x1, x2, yd, ˙yd and ¨yd, then

    ˙τ2=τ2x1x2+τ2x2(F(x)+(χ)+d)+τ2yd˙yd+τ2˙yd¨yd+τ2¨ydyd=θ1+τ2x2d, (3.23)

    where

    θ1=τ2x1x2+τ2x2(F(x)+(χ))+τ2yd˙yd+τ2˙yd¨yd+τ2¨ydyd.

    From e3=(χ)τ2, we can get

    ˙e3=(χ)˙χ˙τ2=f(J)˙τ2. (3.24)

    Step 3: Define the Lyapunov function as

    L3=L2+12e23. (3.25)

    Then

    ˙L3=˙L2+e3˙e3=b1e21b2e22+e2e3+η1kuε1+e3˙e3. (3.26)

    Select

    e4=(χ)τ3. (3.27)

    Then

    ˙e3=e1+τ3(θ1+τ2x2d). (3.28)
    ˙L3=b1e21b2e22+e2e3+η1kuε1+e3(e4+τ3θ1τ2x2d). (3.29)

    Take

    τ3=θ1e2b3e3η1τ2x2tanh(e3τ2x2ε2). (3.30)

    where b3>0.

    According to Eq (3.30), Eq (3.29) can be further rewritten as

    ˙L3=b1e21b2e22b3e23+e3e4+η1kuε1η1e3τ2x2tanh(e3τ2x2ε2)e3τ2x2d, (3.31)

    with

    η1e3τ2x2tanh(e3τ2x2ε2)η1|e3τ2x2|.

    According to Eq (3.20), we have

    η1e3τ2x2tanh(e3τ2x2ε2)e3τ2x2dη1|e3τ2x2|η1e3τ2x2tanh(e3τ2x2ε2)η1knε2. (3.32)

    Then

    ˙L3η1kuε1+η1kuε2b1e21b2e22b3e23+e3e4. (3.33)

    Since

    ˙e4=˙f()˙τ3=f()˙˙τ3=U˙τ3. (3.34)
    τ3=e2b3e3+θ1η1τ2x2tanh(e3τ2x2ε2). (3.35)

    It can be seen that τ3 is x1, x2, yd, ˙yd and ¨yd, then

    ˙τ3=τ3x1x2+τ3x2(F(x)+(χ)+d)+τ3yd˙yd+τ3˙yd¨yd+τ3¨ydyd+τ3ydyd+τ3(χ)(χ)χ˙χ=θ2+τ3xd, (3.36)

    where

    θ2=τ3x1x2+τ3x2(F(x)+(χ))+τ3(χ)f()+τ3yd˙yd+τ3˙yd¨yd+τ3ydyd+τ3ydyd.

    The Lyapunov function is defined as

    L4=L3+12e24. (3.37)

    Along with the trajectories of Eq (3.37), it can be shown that

    ˙L4=˙L3+e4˙e1b1e21b2e22b3e23+e3e4+η1kuε1+η1kuε2+e4(Uθ2τ3x2d). (3.38)

    Therefore, the design control law is

    U=θ2e3b4e4η1τ3x2tanh(e4τ3x2ε3), (3.39)

    where b4>0.

    Substituting the Eq (3.39) into the Eq (3.38), we can further obtain

    ˙L4b1e21b2e22b3e23b4e24+η1kuε1+η1kuε2+e4(η1τ3x2tanh(e4τ3x2ε3)τ3x2d), (3.40)

    with

    τ3x2e4dη1|e4τ3x2|.

    According to Eq (3.20), we have

    η1e4τ3x2tanh(e4τ3x2ε2)τ3x2e4dη1|e4τ3x2|η1e4τ3x2tanh(e4τ3x2ε3)η1kuε3. (3.41)

    Then

    ˙L4b1e21b2e22b3e23b4e24+η1ku(ε1+ε2+ε3)CmL4+β, (3.42)

    where Cm=2min{b1,b2,b3,b4},β=η1ku(ε1+ε2+ε3).

    According to Lemma 1, the solution of ˙V4CmV4+β can be obtained as

    L4(t)eCtL4(0)+βt0eCm(tr)dτ=eCmtL4(0)+βCm(1eCmt), (3.43)

    where

    10eCm(tτ)dτ=1Cm10eCm(tτ)d(Cm(tτ))=1Cm(1eCmt).

    It can be seen that the final gain error of the closed-loop system depends on Cm and the upper bound of the disturbances η1. In the absence of disturbances, η1=0, L4(t)ecmtL4(0) and L4(t) is exponentially convergent. In other words, ei is exponentially convergent. When t, x1yd, x2˙yd.

    Remark 7: In Section 3.1, a bounded control input method based on backstepping control is designed. In the control law Eqs (3.2) and (3.3), because of ˙=(f())1U, when χ is very small, it is very easy to produce singular problems, which may usually lead to abnormal trajectory and even uncontrollable joint speed, bringing great damage to hardware equipment. Therefore, the design method of the Nussbaum function can be used to overcome this problem. In addition, the backstepping control method in Section 3.1 is still used.

    From e3=(χ)τ2, we can get

    ˙e3=(χ)˙χ˙τ2=(χ)(wbχ)˙τ2, (3.44)

    where, b is a constant and satisfies b>0.

    Design the auxiliary control signal as

    w=N(X)ˉw. (3.45)

    Definition 1: If the function N(X) satisfies the following conditions, then N(X) is a Nussbaum function. A Nussbaum function satisfies the following bilateral characteristics [15]:

    limk±sup1kk0N(s)ds=, (3.46)
    limk±inf1kk0N(s)ds=. (3.47)

    The Nussbaum function N(X) and its adaptive law are defined as

    N(X)=X2cos(X), (3.48)
    ˙N(X)=γXe3ˉw, (3.49)

    where γX>0.

    Combined with Eq (3.44), we take

    ˉw=b3e3e2+˙τ2+bχχ. (3.50)

    From Eqs (3.44) and (3.50), we can get

    ˙e3+ˉω=(χ)(wbχ)˙τ2c3z3+˙τ2+bχχe2=(χ)wb3e3e2. (3.51)

    Selecting Lyapunov function as

    ˜L3=L2+12e23. (3.52)

    Along with the trajectories of Eq (3.52), it can be shown that

    ˙L3b1e21b2e22+e2e3+e3˙e3=b1e21b2e22+e2e3+e3(˙e3+ˉwˉw)=b1e21b2e22+e2e3+e3(χb3e3e2)e3ˉwb1e21b2e22b3e23+(χN(X)1)e3ˉw. (3.53)

    Then

    ˙L3C1L3+1γX(ξN(X)1)˙X, (3.54)

    where

    C1=2min{c1,c2,c3}>0,
    ξ=(χ)χ=4(ev/uM+ev/nM)2>0,0<ξ1.

    By further integrating the Eq (3.53), we can obtain

    L3(t)L3(0)C1t0L3(τ)dτ+1γχw(t), (3.55)

    where

    w(t)=χ(t)χ(0)(ξN(s)1)ds.

    Remark 8: According to the theorem 1 analysis method in [15], the analysis is carried out by reduction to absurdity, and the conclusion that X is bounded can be drawn by considering the two cases of X having no upper bound and X having no lower bound. Based on the boundedness of X, we know that N(X) is bounded. According to the Eq (3.55), it can be known that L3(t) is bounded, so e1, e2, e3, ˙e1 and ˙e2 are bounded. Based on Eq (3.55), we have C1t0L3(τ)dτL3(t)L3(0)+1γXw(t). Therefore, t0L3(τ)dτ is bounded. Furthermore, t0e21(τ)dτ and t0e22(τ)dτ are bounded. According to the Barbalat lemma, when t, e10, e20. Thereby we find that under the condition of |u(t)|uM, x1xd and x2˙xd.

    In this study, we performed simulations using MATLAB/Simulink, utilizing a simulation duration of 100 seconds and a time step of 0.001 seconds. This choice of parameters aims to enhance the validation process for the effectiveness of the robust backstepping control (RBSC) method. Furthermore, to ensure a fair comparison, we conducted simulations within an identical simulation environment and under the same external disturbance conditions, allowing us to contrast the simulation outcomes with those obtained using the robust sliding mode control (RSMC) method.

    In addition, in order to verify the robustness and anti-disturbance performance of the control method (RBSC)} proposed in this paper, we chose the simulation verification under two kinds of disturbance (time-varying disturbance and constant disturbance). The time-varying disturbance and constant disturbance can be selected as:

    Time-varying disturbance:

    dvary(t)=0.5sin(2t)+0.005.

    Constant disturbance

    dcons(t)=0.015.

    In this section, we conducted numerical simulations using MATLAB/Simulink to validate the effectiveness of the control algorithm for a flexible manipulator under conditions of limited control input. The primary system program in Simulink, based on an S function, is illustrated in Figure 4. The flexible manipulator used as the controlled object is described by Eq (2.1), with a gravitational acceleration of 9.8 m/s2, a manipulator mass of m = 1.66 kg, and a length of 1.20 m. The auxiliary signal w is determined using Eq (3.45) through Eq (3.50), with parameters set as γχ=1, b=10, b1=10, b2=12 and b3=8. The expression for the control input restriction is |uM|18. The simulation results are shown in Figures 58.

    Figure 4.  The main program diagram of the system based on S-Function.
    Figure 5.  The response diagram of manipulator position and speed tracking.
    Figure 6.  The variation diagram of χ.
    Figure 7.  The diagram of control input u(t).
    Figure 8.  The variation diagram of X.

    In order to better verify the effectiveness and robustness of the precise tracking control proposed in this paper (RBSC method), we choose to compare it with the robust sliding mode control method (RBSC method)} in this paper. Aiming at the single-joint manipulator model (Eqs (2.1)–(2.2)), we choose a sliding mode function as

    s=ce+˙e, (4.1)

    where, c>0.

    Furthermore, the angle tracking error of manipulator is defined as

    e=x1xd. (4.2)

    We choose the Lyapunov function as

    V=12s2. (4.3)

    Taking the derivative of Eq (4.3) yields:

    ˙VRSMC=s˙s=s[c˙e+F(x)+uRSMC(t)¨xd]. (4.4)

    From Eq (4.4), the robust sliding mode controller is designed as follows

    uRSMC(t)=¨xdc˙eF(x)ks, (4.5)

    where k>0.

    From Figures 58, it is evident that the precise tracking control of the manipulator's state and the overall system's stability can be achieved through the control strategy proposed in this paper. In Figure 5, } under the control method proposed in this paper, we use visual representations to highlight the performance of our controller. Specifically, the red solid line represents the ideal reference signal, while the blue dashed line illustrates the actual tracking of both the position and velocity of the manipulator. The key takeaway from this visualization is the successful tracking of the desired reference signal. Both the position and velocity profiles closely align with the ideal reference, indicating that our control method effectively guides the manipulator to achieve the desired trajectory. This visual confirmation of accurate tracking is significant because it demonstrates the practical applicability and efficacy of our control approach. Figures 6 and 8 depict the variations in χ and X. In Figure 7, we observe the response curve of the system's control input, even when subject to input limitations (|u(t)|uM, where uM=18). Remarkably, the Nussbaum function method proposed in this paper maintains system input stability under such constrained conditions. Figure 9 illustrates the response graph for angle and velocity tracking control of the manipulator, comparing the two control methods. It is evident from Figure 9 that the precise tracking control of the manipulator's angle and velocity can be effectively achieved using the control method presented in this paper.

    Figure 9.  The response diagram of angle and velocity tracking under two methods.

    Furthermore, Figure 10 displays the response graph of the control input under both methods, revealing the superior stability of the control method proposed in this paper.

    Figure 10.  The response diagram of control input under two methods.

    Robustness is a crucial aspect of control system design. In order to further verify the effectiveness and robustness of the control method (RBSC method) in this paper, we refer to Figures 11 and 12. As can be seen from Figures 11 and 12, the angle and angular velocity of the manipulator can be well tracked under the control method (RBSC method) in this paper, and the control method (RBSC method) in this paper has stronger robustness and anti-disturbance performance.

    Figure 11.  The response diagram of angle and velocity tracking under two methods (time-varying disturbance).
    Figure 12.  The response diagram of angle and velocity tracking under two methods (constant disturbance).

    In summary, this study explores the challenges faced by flexible manipulators, versatile automated devices with a wide array of applications. These manipulators often encounter issues related to external disturbances and limitations in controlling their actuators, which significantly impact their tracking precision. To address these challenges, we have introduced a comprehensive control strategy. We employed the backstepping method to achieve precise state tracking and manage external disturbances effectively. Additionally, we utilized the Nussbaum function approach to tackle control input limitations, enhancing the robustness of the system.

    For future work, we plan to further improve and expand this control strategy. This may include studying advanced control algorithms, exploring adaptive techniques to deal with various disturbances, and optimizing the design of methods based on the Nussbaum function. In addition, considering that the flexible manipulator model is easily disturbed and the controller design is complicated, the establishment of a flexible manipulator model based on PDE should be paid attention to.

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

    Science and technology project support of China Southern Power Grid Corporation (project number: [GDKJXM20201943]).

    The authors declare there is no conflict of interest.



    [1] I. Podlubny, Fractional Differential Equations, Academic Press, San Diego, 1999.
    [2] R. Hilfer, Applications of Fractional Calculus in Physics, Academic Press, Orlando, 1999.
    [3] M. S. Tavazoei, M. Haeri, S. Jafari, S. Bolouki, M. Siami, Some applications of fractional calculus in suppression of chaotic oscillations, IEEE Trans. Ind. Electron., 55 (2008), 4094–4101. https://doi.org/10.1109/TIE.2008.925774 doi: 10.1109/TIE.2008.925774
    [4] L. Acedo, S. B. Yuste, K. Lindenberg, Reaction front in an a+b→c reaction-subdiffusion process, Phys. Rev. E, 69 (2004), 136–144. https://doi.org/10.1103/PhysRevE.69.036126 doi: 10.1103/PhysRevE.69.036126
    [5] D. A. Benson, S. W. Wheatcraft, M. M. Meerschaert, The fractional-order governing equation of lévy motion, Water Resour. Res., 36 (2000), 1413–1423. https://doi.org/10.1029/2000WR900032 doi: 10.1029/2000WR900032
    [6] J. H. He, Fractal calculus and its geometrical explanation, Results Phys., 10 (2018), 272–276. https://doi.org/10.1016/j.rinp.2018.06.011 doi: 10.1016/j.rinp.2018.06.011
    [7] J. H. He, A modified Li-He's variational principle for plasma, Int. J. Numer. Methods Heat Fluid Flow, 31 (2021), 1369–1372. https://doi.org/10.1108/HFF-06-2019-0523 doi: 10.1108/HFF-06-2019-0523
    [8] D. D. Dai, T. T. Ban, Y. L. Wang, W. Zhang, The piecewise reproducing kernel method for the time variable fractional order advection-reaction-diffusion equations, Therm. Sci., 25 (2021), 1261–1268. https://doi.org/10.2298/TSCI200302021D doi: 10.2298/TSCI200302021D
    [9] Q. T. Ain, J. H. He, N. Anjum, M. Ali, The Fractional complex transform: A novel approach to the time fractional Schrödinger equation, Fractals, 28 (2020), 2050141. https://doi.org/10.1142/S0218348X20501418 doi: 10.1142/S0218348X20501418
    [10] D. C. Lu, B. J. Hong, Bäcklund transformation and n-soliton-like solutions to the combined KdV-Burgers equation with variable coefficients, Int. J. Nonlinear Sci., 1 (2006), 3–10.
    [11] V. A. Matveev, M. A. Salle, Darboux Transformations and Solitons, Heidelberg: Springer Verlag, Berlin, 1991. https://doi.org/10.1007/978-3-662-00922-2
    [12] J. J. Fang, D. S. Mou, H. C. Zhang, Y. Y. Wang, Discrete fractional soliton dynamics of the fractional Ablowitz-Ladik model, Optik, 228 (2021), 166186. https://doi.org/10.1016/j.ijleo.2020.166186 doi: 10.1016/j.ijleo.2020.166186
    [13] B. J. Hong, D. C. Lu, New exact Jacobi elliptic function solutions for the coupled Schrödinger-Boussinesq equations, J. Appl. Math., 2013 (2013), 170835. https://doi.org/10.1155/2013/170835 doi: 10.1155/2013/170835
    [14] D. C. Lu, B. J. Hong, L. X. Tian, New explicit exact solutions for the generalized coupled Hirota-Satsuma KdV system, Comput. Math. Appl., 53 (2007), 1181–1190. https://doi.org/10.1016/j.camwa.2006.08.047 doi: 10.1016/j.camwa.2006.08.047
    [15] B. J. Hong, New Jacobi elliptic functions solutions for the variable-coefficient mKdV equation, Appl. Math. Comput., 215 (2009), 2908–2913. https://doi.org/10.1016/j.amc.2009.09.035 doi: 10.1016/j.amc.2009.09.035
    [16] S. K. Mohanty, O. V. Kravchenko, A. N. Dev, Exact traveling wave solutions of the Schamel Burgers equation by using generalized-improved and generalized G'/G -expansion methods, Results Phys., 33 (2022), 105124. https://doi.org/10.1016/j.rinp.2021.105124 doi: 10.1016/j.rinp.2021.105124
    [17] P. R. Kundu, M. R. A. Fahim, M. E. Islam, M. A. Akbar, The sine-Gordon expansion method for higher-dimensional NLEEs and parametric analysis, Heliyon, 7 (2021), e06459. https://doi.org/10.1016/j.heliyon.2021.e06459 doi: 10.1016/j.heliyon.2021.e06459
    [18] S. M. Mirhosseini-Alizamini, N. Ullah, J. Sabi'u, H. Rezazadeh, M. Inc, New exact solutions for nonlinear Atangana conformable Boussinesq-like equations by new Kudryashov method, Int. J. Modern Phys. B, 35 (2021), 2150163. https://doi.org/10.1142/S0217979221501630 doi: 10.1142/S0217979221501630
    [19] S. Zhang, H. Q. Zhang, Fractional sub-equation method and its applications to nonlinear fractional PDEs, Phys. Lett. A, 375 (2011), 1069–1073. https://doi.org/10.1016/j.physleta.2011.01.029 doi: 10.1016/j.physleta.2011.01.029
    [20] B. Xu, Y. F. Zhang, S. Zhang, Line soliton interactions for shallow ocean-waves and novel solutions with peakon, ring, conical, columnar and lump structures based on fractional KP equation, Adv. Math. Phys., 2021 (2021), 6664039. https://doi.org/10.1155/2021/6664039
    [21] B. Xu, S. Zhang, Riemann-Hilbert approach for constructing analytical solutions and conservation laws of a local time-fractional nonlinear Schrödinger equation, Symmetry, 13 (2021), 13091593. https://doi.org/10.3390/sym13091593 doi: 10.3390/sym13091593
    [22] Y. Y. Gu, L. W. Liao, Closed form solutions of Gerdjikov-Ivanov equation in nonlinear fiber optics involving the beta derivatives, Int. J. Modern Phys. B, 36 (2022), 2250116. https://doi.org/10.1142/S0217979222501168
    [23] Y. Y. Gu, N. Aminakbari, Bernoulli (G'/G)-expansion method for nonlinear Schrödinger equation with third-order dispersion, Modern Phys. Lett. B, 36 (2022), 2250028. https://doi.org/10.1007/s11082-021-02807-0 doi: 10.1007/s11082-021-02807-0
    [24] S. Zhang, Y. Y. Wei, B. Xu, Fractional soliton dynamics and spectral transform of time-fractional nonlinear systems: an concrete example, Complexity, 2019 (2019), 7952871. https://doi.org/10.1155/2019/7952871 doi: 10.1155/2019/7952871
    [25] K. L. Geng, D. S. Mou, C. Q. Dai, Nondegenerate solitons of 2-coupled mixed derivative nonlinear Schrödinger equations, Nonlinear Dyn., 111 (2023), 603–617. https://doi.org/10.1007/s11071-022-07833-5 doi: 10.1007/s11071-022-07833-5
    [26] W. B. Bo, R. R. Wang, Y. Fang, Y. Y. Wang, C. Q. Dai, Prediction and dynamical evolution of multipole soliton families in fractional Schrödinger equation with the PT-symmetric potential and saturable nonlinearity, Nonlinear Dyn., 111 (2023), 1577–1588. https://doi.org/10.1007/s11071-022-07884-8 doi: 10.1007/s11071-022-07884-8
    [27] R. R. Wang, Y. Y. Wang, C. Q. Dai, Influence of higher-order nonlinear effects on optical solitons of the complex Swift-Hohenberg model in the mode-locked fiber laser, Opt. Laser Technol., 152 (2022), 108103. https://doi.org/10.1016/j.optlastec.2022.108103 doi: 10.1016/j.optlastec.2022.108103
    [28] S. L. He, B. A. Malomed, D. Mihalache, X. Peng, X. Yu, Y. J. He, et al., Propagation dynamics of abruptly autofocusing circular Airy Gaussian vortex beams in the fractional Schrödinger equation, Chaos, Solitons Fractals, 142 (2021), 110470. https://doi.org/10.1016/j.chaos.2020.110470 doi: 10.1016/j.chaos.2020.110470
    [29] S. L. He, Z. W. Mo, J. L. Tu, Z. L. Lu, Y. Zhang, X. Peng, et al., Chirped Lommel Gaussian vortex beams in strongly nonlocal nonlinear fractional Schrödinger equations, Results Phys., 42 (2022), 106014. https://doi.org/10.1016/j.rinp.2022.106014 doi: 10.1016/j.rinp.2022.106014
    [30] R. K. Dodd, J. C. Eilbeck, J. D. Gibbon, H. C. Morris, Solitons and Nonlinear Wave Equations, Academic Press, London-New York, 1982.
    [31] M. Levy, Parabolic Equation Methods for Electromagnetic Wave Propagation, Institution of Electrical Engineers (IEE), London, 2000.
    [32] A. Arnold, Numerically absorbing boundary conditions for quantum evolution equations, VLSI Des., 6 (1998), 38298. https://doi.org/10.1155/1998/38298 doi: 10.1155/1998/38298
    [33] Y. S. Kivshar, G. P. Agrawal, Optical Solitons: From Fibers to Photonic Crystals, Academic Press, New York, 2003.
    [34] F. D. Tappert, The Parabolic Approximation Method, Springer, Berlin. https://doi.org/10.1007/3-540-08527-0_5
    [35] M. Hosseininia, M. H. Heydari, C. Cattani, N. Khanna, A wavelet method for nonlinear variable-order time fractional 2D Schrödinger equation, Discrete Contin. Dyn. Syst. Ser. S, 14 (2021), 2273–2295. https://doi.org/10.1016/j.camwa.2019.06.008 doi: 10.1016/j.camwa.2019.06.008
    [36] A. Somayeh, N. Mohammad, Exact solitary wave solutions of the complex nonlinear Schrödinger equations, Opt. Int. J. Light Electron. Opt., 127 (2016), 4682–4688. https://doi.org/10.1016/j.ijleo.2016.02.008 doi: 10.1016/j.ijleo.2016.02.008
    [37] M. A. E. Herzallah, K. A. Gepreel, Approximate solution to the time–space fractional cubic nonlinear Schrödinger equation, Appl. Math. Modell., 36 (2012), 5678–5685. https://doi.org/10.1016/j.apm.2012.01.012 doi: 10.1016/j.apm.2012.01.012
    [38] H. Gündodu, M. F. Gzükzl, Cubic nonlinear fractional Schrödinger equation with conformable derivative and its new travelling wave solutions, J. Appl. Math. Comput. Mech., 20 (2021), 29–41. https://doi.org/10.17512/jamcm.2021.2.03 doi: 10.17512/jamcm.2021.2.03
    [39] A. M. Wazwaz, A study on linear and nonlinear Schrödinger equations by the variational iteration method, Chaos Solitons Fractals, 37 (2008), 1136–1142. https://doi.org/10.1016/j.chaos.2006.10.009 doi: 10.1016/j.chaos.2006.10.009
    [40] A. Hasegawa, F. Tappert, Transmission of stationary nonlinear optical pulses in dispersive dielectric fibers. Ⅱ. Normal dispersion, Appl. Phys. Lett., 23 (1973), 171–172. https://doi.org/10.1063/1.1654836 doi: 10.1063/1.1654836
    [41] A. Ebaid, S. M. Khaled, New types of exact solutions for nonlinear Schrödinger equation with cubic nonlinearity, J. Comput. Appl. Math., 235 (2011), 1984–1992. https://doi.org/10.1016/j.cam.2010.09.024 doi: 10.1016/j.cam.2010.09.024
    [42] A. Demir, M. A. Bayrak, E. Ozbilge, New approaches for the solution of space-time fractional Schrödinger equation, Adv. Differ. Equations, 2020 (2020), 1–21. https://doi.org/10.1186/s13662-020-02581-5 doi: 10.1186/s13662-020-02581-5
    [43] N. A. Kudryashov, Method for finding optical solitons of generalized nonlinear Schrödinger equations, Opt. Int. J. Light Electron. Opt., 261 (2022), 169163. https://doi.org/10.1016/j.ijleo.2022.169163 doi: 10.1016/j.ijleo.2022.169163
    [44] B. Ghanbari, K. S. Nisar, M. Aldhaifallah, Abundant solitary wave solutions to an extended nonlinear Schrödinger equation with conformable derivative using an efficient integration method, Adv. Differ. Equations, 328 (2020), 1–25. https://doi.org/10.1186/s13662-020-02787-7 doi: 10.1186/s13662-020-02787-7
    [45] M. T. Darvishi, M. Najafi, A. M. Wazwaz, Conformable space-time fractional nonlinear (1+1)- dimensional Schrödinger-type models and their traveling wave solutions, Chaos Solitons Fractals, 150 (2021), 111187. https://doi.org/10.1016/j.chaos.2021.111187 doi: 10.1016/j.chaos.2021.111187
    [46] T. Mathanaranjan, Optical singular and dark solitons to the (2+1)-dimensional time-space fractional nonlinear Schrödinger equation, Results Phys., 22 (2021), 103870. https://doi.org/10.1016/j.rinp.2021.103870 doi: 10.1016/j.rinp.2021.103870
    [47] Y. X. Chen, X. Xiao, Z. L. Mei, Optical soliton solutions of the (1+1)-dimensional space-time fractional single and coupled nonlinear Schrödinger equations, Results Phys., 18 (2020), 103211. https://doi.org/10.1016/j.rinp.2020.103211 doi: 10.1016/j.rinp.2020.103211
    [48] E. K. Jaradat, O. Alomari, M. Abudayah, A. Al-Faqih, An approximate analytical solution of the nonlinear Schrödinger equation with harmonic oscillator using homotopy perturbation method and Laplace-Adomian decomposition method, Adv. Math. Phys., 2018 (2018), 1–11. https://doi.org/10.1155/2018/6765021 doi: 10.1155/2018/6765021
    [49] M. G. Sakar, F. Erdogan, A. Yzldzrzm, Variational iteration method for the time-fractional Fornberg-Whitham equation, Comput. Math. Appl., 63 (2012), 1382–1388. https://doi.org/10.1016/j.camwa.2012.01.031 doi: 10.1016/j.camwa.2012.01.031
    [50] G. Jumarie, Table of some basic fractional calculus formulae derived from a modified Riemann-Liouville derivative for non-differentiable functions, Appl. Math. Lett., 22 (2009), 378–385. https://doi.org/10.1016/j.aml.2008.06.003 doi: 10.1016/j.aml.2008.06.003
    [51] B. J. Hong, D. C. Lu, W. Chen, Exact and approximate solutions for the fractional Schrödinger equation with variable coefficients, Adv. Differ. Equations, 2019 (2019), 1–10. https://doi.org/10.1186/s13662-019-2313-z doi: 10.1186/s13662-019-2313-z
    [52] B. J. Hong, D. C. Lu, New exact solutions for the generalized variable-coefficient Gardner equation with forcing term, Appl. Math. Comput., 219 (2012), 2732–2738 https://doi.org/10.1016/j.amc.2012.08.104 doi: 10.1016/j.amc.2012.08.104
    [53] B. J. Hong, New exact Jacobi elliptic functions solutions for the generalized coupled Hirota-Satsuma KdV system, Appl. Math. Comput., 217 (2010), 472–479. https://doi.org/10.1016/j.amc.2010.05.079 doi: 10.1016/j.amc.2010.05.079
    [54] J. H. He, S. K. Elagan, Z. B. Li, Geometrical explanation of the fractional complex transform and derivative chain rule for fractional calculus, Phys. Lett. A, 376 (2012), 257–259. https://doi.org/10.1016/j.physleta.2011.11.030 doi: 10.1016/j.physleta.2011.11.030
    [55] Z. B. Li, J. H. He, Fractional complex transformation for fractional differential equation, Math. Comput. Appl., 15 (2010), 970–973. https://doi.org/10.3390/mca15050970 doi: 10.3390/mca15050970
    [56] A. Ebaid, E. H. Aly, Exact solutions for the transformed reduced Ostrovsky equation via the F-expansion method in terms of Weierstrass-elliptic and Jacobian-elliptic functions, Wave Motion, 49 (2012), 296–308. https://doi.org/10.1016/j.wavemoti.2011.11.003 doi: 10.1016/j.wavemoti.2011.11.003
    [57] M. H. Bashar, S. M. R. Islam, Exact solutions to the (2+1)-Dimensional Heisenberg ferromagnetic spin chain equation by using modifified simple equation and improve F-expansion methods, Phys. Open, 5 (2020), 100027. https://doi.org/10.1016/j.physo.2020.100027 doi: 10.1016/j.physo.2020.100027
    [58] C. Scipio, An invariant set in energy space for supercritical NLS in 1D, J. Math. Anal. Appl., 352 (2009), 634–644. https://doi.org/10.1016/j.jmaa.2008.11.023 doi: 10.1016/j.jmaa.2008.11.023
    [59] L. J. Zhang, P. Y. Yuan, J. L. Fu, C. M. Khalique, Bifurcations and exact traveling wave solutions of the Zakharov-Rubenchik equation, Discrete Contin. Dyn. Syst. S, 13 (2018), 2927–2939. https://doi.org/10.3934/dcdss.2020214 doi: 10.3934/dcdss.2020214
    [60] G. A. Xu, Y. Zhan, J. B. Li, Exact solitary wave and periodic-peakon solutions of the complex Ginzburg-Landau equation: Dynamical system approach, Math. Comput. Simul., 191 (2022), 157–167. https://doi.org/10.1016/j.matcom.2021.08.007 doi: 10.1016/j.matcom.2021.08.007
  • This article has been cited by:

    1. Yang Zhang, Liang Zhao, Jyotindra Narayan, Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method, 2025, 20, 1932-6203, e0318601, 10.1371/journal.pone.0318601
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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

Metrics

Article views(1639) PDF downloads(70) Cited by(2)

Figures and Tables

Figures(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog