All-wheel-drive ship parallel trajectory tracking control method and device based on high-order learning observer

Abstract

The invention discloses an all-wheel-drive ship parallel trajectory tracking control method based on a high-order learning observer. The method comprises the following steps: establishing an all-wheel-drive ship motion model; designing a polynomial basis function to obtain the polynomial basis function; designing a high-order learning observer for outputting estimated values of ship position and course angle, a speed estimated value, an approximation error estimated value and an estimated value of unknown total disturbance; designing a guidance method of the full-drive ship based on the ship position, the yawing angle, the speed information, the estimated value of the high-order learning observer, the estimated value of the unknown total disturbance and the estimated value of the approximation error obtained by the full-drive ship motion model, and generating a guidance signal of the ship; and a parallel trajectory tracking controller is designed based on the guidance signal of the ship, the estimated position and yawing angle output by the high-order learning observer, the estimated speed, the estimated weight and the estimated approximation error information, and is used for generating a control signal of ship driving, so that the control of parallel trajectory tracking driving of the full-drive ship is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship trajectory tracking control, and in particular relates to a method and device for parallel trajectory tracking control of an all-wheel drive ship based on a high-order learning observer. Background Art

[0002] Ships can be equipped with a variety of detection equipment, including multi-beam echosounders, side-scan sonars, and underwater robots, enabling missions such as marine scientific research, resource exploration, maritime rescue, and ocean shipping. Facing the complex environments of hazardous waters and the severe challenges of extreme weather, ships are better able to leverage their unique advantages, providing safe and reliable technical support for marine resource development. With the deep integration of next-generation information technology and ship motion control, demand for its application is increasing. Against this backdrop, achieving accurate ship trajectory tracking in complex and changing marine environments has become a key technical challenge that urgently needs to be addressed.

[0003] Trajectory tracking controller design methods have received widespread attention from scholars at home and abroad in recent years. For all-drive ships in complex ocean environments, Zhohua Peng constructed a general deep neural network to approximate the unknown total disturbance in dynamics, and designed a meta-learning-based trajectory tracking controller, which effectively improved the trajectory tracking performance. Keng PengTee used a feedforward neural network to approximate the uncertainty of the ship and the environmental disturbance, and designed a robust adaptive trajectory tracking controller. Zhen Zhao used a multi-layer neural network to approximate the uncertainty of the ship and studied the trajectory tracking control problem of all-drive ships under input and output constraints. Shude He combined neural networks and specified performance technology to design a preset performance trajectory tracking controller to ensure that the output tracking error meets the specified transient and steady-state performance. In response to the problems of traditional reinforcement learning's strong dependence on the model and poor adaptability, Yuan Zhou proposed a trajectory tracking controller based on deep reinforcement learning, which achieved high-precision trajectory tracking performance for all-drive ships in complex ocean environments. However, the existing ship trajectory tracking controller design methods still have some shortcomings:

[0004] First, existing offline ship motion modeling methods based on meta-learning, reinforcement learning, and other methods rely heavily on historical ship navigation data. However, acquiring and processing historical navigation data is complex, and both data accuracy and completeness can affect modeling effectiveness. Furthermore, when a ship encounters new or unknown marine environments, these methods may not provide sufficient generalization capabilities to adapt to new situations.

[0005] Second, although the existing online ship motion modeling methods based on neural networks and fuzzy logic systems have strong modeling capabilities in approximating nonlinear models related to system states, they have insufficient modeling capabilities for time-related external environmental disturbances such as random noise, ocean wind, wave and current disturbances.

[0006] Third, existing methods for estimating internal and external disturbances in ship dynamics rarely consider potential errors in the estimation, resulting in low model accuracy and reliability. Furthermore, while using high-dimensional basis functions to approximate these disturbances can improve the model's fitting capabilities to a certain extent, the computational cost increases significantly, reducing the model's real-time performance and thus impacting the ship's control performance. Summary of the Invention

[0007] In order to solve the above problems, the technical solution adopted by the present invention is: a parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer, comprising the following steps:

[0008] Establish an all-wheel drive ship motion model;

[0009] Based on the actual ship velocity ν in the hull coordinate system output by the all-wheel drive ship motion model, a polynomial basis function is designed to obtain the polynomial basis function Γ to estimate the unknown total disturbance;

[0010] Based on the all-wheel drive ship motion model and the polynomial basis function Γ, ship control signal τ ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance

[0011] Based on the position, bow angle η, and speed information ν output by the all-wheel drive ship motion model, the estimated value of the high-order learning observer Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships;

[0012] Based on the guidance signal of the ship and the estimated position and heading angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer, a parallel trajectory tracking controller is designed to generate control signals for the ship's movement and realize the control of the all-wheel drive ship to track along a parallel trajectory.

[0013] Furthermore, the process of establishing the all-wheel drive ship motion model is as follows:

[0014] The kinematic equations and dynamic equations of the controlled all-wheel drive ship are described as follows:

[0015]

[0016] The meanings of the symbols in the above formula are as follows: represents the vector consisting of the ship's position (x, y) and the heading angle ψ in the Earth coordinate system, represents the vector consisting of the longitudinal velocity u, transverse velocity v and yaw angular velocity r of the ship in the hull coordinate system, represents the rotation matrix, m represents the mass of the ship, m x ,m y Represent the additional mass of the ship on the x and y axes, I zz ,J zz They represent the ship’s moment of inertia around the z axis and the additional mass moment of inertia, X (·) ,Y (·) Indicates the force, N (·) represents the applied torque, subscript H represents the damping force, subscript P represents the thrust generated by the propeller, subscript R represents the rudder force, and subscript W represents the environmental force, that is, the external disturbance generated by waves and wind;

[0017] In the dynamic model, the thrust generated by the propeller and the rudder force are combined into the control input vector τ of the ship. ν =[X P +X R ,Y P +Y R ,N P +N R ] T , the external disturbance vector generated by wind, waves and currents in the ocean environment is τ W =[X W ,Y W ,N W ] T , the mass matrix of the ship is in The uncertainty vector from the dynamics internal model is Therefore, the ship's motion model is reconstructed as follows:

[0018]

[0019] Furthermore, the expression of the polynomial basis function Γ is as follows:

[0020]

[0021] Where n0 represents the minimum number of different trigonometric function families that can approximate the external interference, and ω represents the frequency.

[0022] Furthermore: the expression of the high-order learning observer is as follows:

[0023]

[0024] The meanings of the symbols in the above formula are as follows: represents the estimated value of the position and the heading angle η, represents the estimated value of the velocity ν, Represents the approximation error The estimated value of Indicates the introduced intermediate variables The estimated value of represents the estimate of the ideal weight θ, represents the matrix composed of polynomial basis functions Γ, represents a diagonal matrix, is a positive constant.

[0025] Furthermore, the guidance method for designing a fully driven ship uses the following formula to generate the guidance signal for the ship:

[0026]

[0027] The meanings of the symbols in the above formula are as follows: represents the given reference trajectory and yaw angle, Represents the vector η d The derivative of the error vector Represents the difference between the estimated position and yaw angle and the given reference trajectory and yaw angle, k1∈R + represents a positive constant, It's a guidance signal.

[0028] Furthermore, the expression of the parallel trajectory tracking controller is as follows:

[0029]

[0030] The meanings of the symbols in the above formula are as follows: It is a guidance signal The derivative of the error vector represents the difference between the estimated velocity and the guidance signal, k2∈R + Represents a positive constant.

[0031] A parallel trajectory tracking control device for an all-wheel drive ship based on a high-order learning observer, comprising:

[0032] Building module: used to build the all-wheel drive ship motion model;

[0033] Design polynomial basis function module: It is used to design polynomial basis functions based on the actual ship velocity ν in the hull coordinate system output by the all-wheel drive ship motion model, and obtain the polynomial basis function Γ, which is used to estimate the unknown total disturbance;

[0034] Design a high-order learning observer module: for ship motion model based on all-wheel drive and polynomial basis function Γ, ship control signal τ ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance

[0035] Guidance module for all-wheel drive ships: used to estimate the position, bow angle η, speed information ν, and high-order learning observer based on the output of the all-wheel drive ship motion model Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships;

[0036] Parallel trajectory tracking controller module: It is used to design a parallel trajectory tracking controller based on the ship's guidance signal and the estimated position and heading angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer. It is used to generate control signals for the ship's movement and realize the control of the all-wheel drive ship to track along parallel trajectories.

[0037] A computer device comprises: a processor and a memory, wherein the memory stores a program module, and wherein the program module runs on the processor to implement any one of the methods described above.

[0038] This paper proposes a parallel trajectory tracking control method for an all-wheel-drive ship based on a high-order learning observer. Based on the Fourier series approximation theorem and the Taylor series expansion theorem, combined with backstepping, a high-order learning observer-based parallel trajectory tracking control structure and design method are proposed. This method not only enables rapid estimation of the total disturbance in the ship's dynamic system but also ensures high stability of the control output, providing strong technical support for autonomous navigation and precise trajectory tracking of ships.

[0039] Compared with the existing ship trajectory tracking control method, the present invention has the following advantages:

[0040] First, compared with existing offline ship motion modeling methods based on meta-learning, reinforcement learning, etc., the present invention designs an online modeling method based on polynomial approximation, which does not rely on historical navigation data and can accurately model kinematics and dynamics based only on the current ship status, thereby improving the real-time and adaptability of modeling.

[0041] Second, compared with the existing online ship motion modeling methods based on neural networks and fuzzy logic systems, the present invention designs an approximation method based on Fourier series expansion and Taylor series expansion, which simultaneously realizes the approximation of internal disturbances and external disturbances, overcomes the limitations of neural networks and fuzzy logic systems in expressing the dynamic characteristics of ships and poor interpretability, and improves the accuracy, speed and interpretability of ship motion modeling.

[0042] Third, compared with the existing estimation of internal and external disturbances in ship dynamics, the present invention designs a high-order learning observer that can simultaneously achieve accurate observation of the virtual ship state and modeling errors, and by introducing an intermediate variable to optimize the observer structure, it effectively reduces the computational complexity while ensuring the estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 This is a schematic diagram of the ship trajectory tracking controller structure based on a high-order learning observer;

[0045] Figure 2 It is a schematic diagram of ship trajectory tracking control performance;

[0046] Figure 3 is the curve diagram of ship trajectory tracking error;

[0047] Figure 4 is the tracking error curve of ship guidance signal;

[0048] Figure 5 This is the effect diagram of the high-order learning observer's estimation of the actual position and heading angle; where (a) is the horizontal coordinate in the earth coordinate system, (b) is the vertical coordinate in the earth coordinate system, and (c) is the yaw angle;

[0049] Figure 6 This is a diagram showing the effect of the high-order learning observer on the actual speed estimation; (a) is the forward speed, (b) is the longitudinal speed, and (c) is the yaw angular velocity. DETAILED DESCRIPTION

[0050] It should be noted that, unless there is any conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] A parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer comprises the following steps:

[0053] S1: Establishing an all-wheel drive ship motion model;

[0054] S2: Based on the actual ship velocity ν in the hull coordinate system output by the all-wheel drive ship motion model, a polynomial basis function is designed to obtain a polynomial basis function Γ, which is used to estimate the unknown disturbance;

[0055] S3: Based on the all-wheel drive ship motion model and the polynomial basis function Γ, the ship control signal τ ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance

[0056] S4: Estimated value of the high-order learning observer based on the position, bow angle η, and speed information ν output by the all-wheel drive ship motion model Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships;

[0057] S5: Based on the guidance signal of the ship and the estimated position and bow angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer, a parallel trajectory tracking controller is designed to generate a control signal for the ship's movement and realize the control of the all-wheel drive ship to track along a parallel trajectory.

[0058] Steps S1 / S2 / S3 / S4 / S5 are performed sequentially;

[0059] Furthermore, the process of establishing the all-wheel drive ship motion model is as follows:

[0060] The kinematic equations and dynamic equations of the controlled all-wheel drive ship are described as follows:

[0061]

[0062] The meanings of the symbols in the above formula are as follows: represents the vector consisting of the ship's position (x, y) and the heading angle ψ in the Earth coordinate system, represents the vector consisting of the longitudinal velocity u, transverse velocity v and yaw angular velocity r of the ship in the hull coordinate system, represents the rotation matrix, m represents the mass of the ship, m x ,m y Represent the additional mass of the ship on the x and y axes, I zz ,J zz They represent the ship’s moment of inertia around the z axis and the additional mass moment of inertia, X (·) ,Y (·) Indicates the force, N (·) represents the applied torque, subscript H represents the damping force, subscript P represents the thrust generated by the propeller, subscript R represents the rudder force, and subscript W represents the environmental force, that is, the external disturbance generated by waves and wind;

[0063] In the dynamic model, the thrust generated by the propeller and the rudder force are combined into the control input vector τ of the ship. ν =[X P +X R ,Y P +Y R ,N P +N R ] T , the external disturbance vector generated by wind, waves and currents in the ocean environment is τ W =[X W ,Y W ,N W ] T , the mass matrix of the ship is in The uncertainty vector from the dynamics internal model is Therefore, the ship's motion model is reconstructed as follows:

[0064]

[0065] Furthermore, the input signal of the polynomial basis function is the actual speed ν of the ship in the hull coordinate system, and the output signal is the polynomial basis function Γ.

[0066] On the one hand, the external disturbance τ generated by the combined action of natural factors such as wind, waves, and currents W =[X W ,Y W ,N W ] T , whose characteristics can be characterized as a time-varying vector. According to the Fourier series theorem, this time-varying vector can be decomposed into a set of weighted series based on trigonometric functions.

[0067] On the other hand, the uncertainty of the dynamic internal model It can be regarded as a vector related to the velocity state ν. According to Taylor's expansion theorem, this vector can be expanded into a linear combination of states u, v, and r in a small neighborhood of ν at each moment.

[0068] The expression of the polynomial basis function Γ is as follows:

[0069]

[0070] Where n0 represents the minimum number of different trigonometric function families that can approximate the external interference, and ω represents the frequency.

[0071] Furthermore, the input signal of the high-order learning observer is the control input τ output by the parallel trajectory tracking controller module. ν The ship model outputs the position and heading angle η, speed information ν and polynomial basis function Γ, and the output signal is the estimated value of the position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance

[0072] The kinematic formula of the all-wheel drive ship is reformulated as follows:

[0073]

[0074] The meanings of the symbols in the above formula are as follows: is the mass matrix The nominal matrix, Represents the unknown total disturbance. The expression of the high-order learning observer is as follows:

[0075]

[0076] The meanings of the symbols in the above formula are as follows: represents the estimated value of the position and the heading angle η, represents the estimated value of the velocity ν, Represents the approximation error The estimated value of Indicates the introduced intermediate variables The estimated value of represents the estimate of the ideal weight θ, represents the matrix composed of polynomial basis functions Γ, represents a diagonal matrix, is a positive constant.

[0077] Furthermore, based on the input signals: position and yaw angle η, speed information ν, the estimated value of the high-order learning observer Estimated value of the unknown total disturbance and an estimate of the approximation error The output signal is the control force and torque τ ν The guidance method for designing a fully driven ship and generating a guidance signal for the ship adopts the following formula:

[0078]

[0079] The meanings of the symbols in the above formula are as follows: represents the given reference trajectory and yaw angle, Represents the vector η d The derivative of the error vector Represents the difference between the estimated position and yaw angle and the given reference trajectory and yaw angle, k1∈R + represents a positive constant, It's a guidance signal.

[0080] Furthermore, the expression of the parallel trajectory tracking controller is as follows:

[0081]

[0082] The meanings of the symbols in the above formula are as follows: It is a guidance signal The derivative of the error vector represents the difference between the estimated velocity and the guidance signal, k2∈R + Represents a positive constant.

[0083] A parallel trajectory tracking control device for an all-wheel drive ship based on a high-order learning observer comprises: a building module, a polynomial basis function module, a high-order learning observer module, a guidance module, and a parallel trajectory tracking controller; wherein:

[0084] Establishment module: used to establish the all-wheel drive ship motion model; receive external disturbance information caused by wind, waves, currents, etc. and the control input signal sent by the parallel trajectory tracking controller to establish the all-wheel drive ship motion model

[0085] Polynomial basis function module: It is used to design polynomial basis functions based on the actual ship's position, bow angle, and velocity ν in the hull coordinate system output by the all-wheel drive ship motion model, and obtain the polynomial basis function Γ, which is used to estimate the unknown total disturbance;

[0086] High-order learning observer module: used to calculate the actual position, bow angle, speed information, polynomial basis function Γ, ship control signal τ based on the output of the all-wheel drive ship motion model ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance

[0087] Guidance module: used to estimate the position, bow angle η, speed information ν based on the output of the all-wheel drive ship motion model, and the high-order learning observer Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships;

[0088] Parallel trajectory tracking controller: It is used to design a parallel trajectory tracking controller based on the guidance signal of the ship and the estimated position and heading angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer. It is used to generate the control signal for the ship's movement and realize the control of the all-wheel drive ship to track along the parallel trajectory.

[0089] A computer device comprises: a processor and a memory, wherein the memory stores a program module, and wherein the program module runs on the processor to implement any one of the methods described above.

[0090] The present invention designs a parallel path tracking controller for a fully driven unmanned boat based on polynomial approximation modeling. The system architecture is as follows: Figure 1 As shown in Figure 2, consider a simulation scenario where a large fully driven ship follows a time-varying reference trajectory. The initial state of the fully driven ship is defined as η0 = [230 m, -5 m, 3π / 5 rad] T and ν0=[6m / s,0.5m / s,0rad / s] T , and the given time-varying reference trajectory and yaw angle is η d (t)=[200cos(0.025t),200sin(0.025t),0.025t+π / 2] T .

[0091] Other simulation-related parameters are selected as follows: the observation gain of the high-order learning observer is set to ρ1 = ρ2 = diag{10,10,10}, ρ3 = diag{6,6,6}, the adaptive gain is set to ρ4 = 0.15, ρ5 = 5. The control gain is set to k1 = 0.3, k2 = 5.

[0092] The simulation results are as follows Figure 2-5 shown.

[0093] Figure 2 A schematic diagram of the motion trajectory of the fully driven ship under the method proposed in the present invention is given. It can be seen that the ship quickly tracks the given reference trajectory after a short transient process.

[0094] Figure 3 The tracking error curves of the three degrees of freedom, longitudinal, lateral and yaw angle, all converge asymptotically to the neighborhood of zero, verifying the stability of the method proposed in this invention.

[0095] Figure 4 The tracking error curves between the actual longitudinal velocity, lateral velocity, and yaw angular velocity and the guided velocity signal are displayed, indicating that the actual velocity of the all-drive ship can effectively track the designed guided velocity signal under the action of the proposed method.

[0096] Figure 5 This is the effect diagram of the high-order learning observer's estimation of the actual position and heading angle; where (a) is the horizontal coordinate in the earth coordinate system, (b) is the vertical coordinate in the earth coordinate system, and (c) is the yaw angle; Figure 6 This is a diagram showing the effect of the high-order learning observer on the actual speed estimation; (a) is the forward speed, (b) is the longitudinal speed, and (c) is the yaw angular velocity.

[0097] Figure 5 and Figure 6 The estimation effect of the improved order learning observer is given, which shows that the position, bow angle and speed of the actual all-wheel drive ship can be accurately estimated in both transient and steady-state processes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer, characterized by: The following steps are involved: Establish an all-wheel drive ship motion model; Based on the actual ship velocity ν in the hull coordinate system obtained from the all-wheel drive ship motion model, a polynomial basis function is designed to obtain the polynomial basis function Γ, which is used to fit the unknown total disturbance; Based on the all-wheel drive ship motion model and the polynomial basis function Γ, ship control signal τ ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance The ship position, bow angle η, and speed information ν obtained based on the all-wheel drive ship motion model, and the estimated value of the high-order learning observer Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships; Based on the guidance signal of the ship, the estimated position and bow angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer, a parallel trajectory tracking controller is designed to generate control signals for the ship's movement and realize the control of the all-wheel drive ship to track along a parallel trajectory.

2. The parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer according to claim 1, characterized in that: The establishment process of the all-wheel drive ship motion model is as follows: The kinematic equations and dynamic equations of the controlled all-wheel drive ship are described as follows: The meanings of the symbols in the above formula are as follows: represents the vector consisting of the ship's position (x, y) and the heading angle ψ in the Earth coordinate system, represents the vector consisting of the longitudinal velocity u, transverse velocity v and yaw angular velocity r of the ship in the hull coordinate system, represents the rotation matrix, m represents the mass of the ship, m x ,m y Represent the additional mass of the ship on the x and y axes, I zz ,J zz They represent the ship’s moment of inertia around the z axis and the additional mass moment of inertia, X (·) ,Y (·) Indicates the force, N (·) represents the applied torque, subscript H represents the damping force, subscript P represents the thrust generated by the propeller, subscript R represents the rudder force, and subscript W represents the environmental force, that is, the external disturbance generated by waves and wind; In the dynamic model, the thrust generated by the propeller and the rudder force are combined into the control input vector τ of the ship. ν =[X P +X R ,Y P +Y R ,N P +N R ] T , the external disturbance vector generated by wind, waves and currents in the ocean environment is τ W =[X W ,Y W ,N W ] T , the mass matrix of the ship is in The uncertainty vector from the dynamics internal model is Therefore, the ship's motion model is reconstructed as follows:

3. The parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer according to claim 1, characterized in that: The expression of the polynomial basis function Γ is as follows: Where n0 represents the minimum number of different trigonometric function families that can approximate the external interference, and ω represents the frequency.

4. The parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer according to claim 1, characterized in that: The expression of the high-order learning observer is as follows: The meanings of the symbols in the above formula are as follows: represents the estimated value of the position and the heading angle η, represents the estimated value of the velocity ν, Represents the approximation error The estimated value of Indicates the introduced intermediate variables The estimated value of represents the estimate of the ideal weight θ, represents the matrix composed of polynomial basis functions Γ, represents a diagonal matrix, is a positive constant.

5. The parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer according to claim 1, characterized in that: The guidance method for designing a fully driven ship and generating a guidance signal for the ship are designed using the following formula: The meanings of the symbols in the above formula are as follows: represents the given reference trajectory and yaw angle, Represents the vector η d The derivative of the error vector Represents the difference between the estimated position and yaw angle and the given reference trajectory and yaw angle, k1∈R + represents a positive constant, It's a guidance signal.

6. The parallel trajectory tracking control method for an all-wheel drive ship based on a high-order learning observer according to claim 1, characterized in that: The expression of the parallel trajectory tracking controller is as follows: The meanings of the symbols in the above formula are as follows: It is a guidance signal The derivative of the error vector represents the difference between the estimated velocity and the guidance signal, k2∈R + Represents a positive constant.

7. A parallel trajectory tracking control device for an all-wheel drive ship based on a high-order learning observer, characterized by: include: Building module: used to build the all-wheel drive ship motion model; Design polynomial basis function module: It is used to design polynomial basis function based on the actual ship speed ν in the hull coordinate system output by the all-wheel drive ship motion model, and obtain the polynomial basis function Γ to estimate the unknown total disturbance. Design high-order learning observer module: It is used to estimate the unknown total disturbance based on the all-wheel drive ship motion model and the polynomial basis function Γ and the ship control signal τ ν , design a high-order learning observer to output the estimated values ​​of the ship's position and heading angle Speed ​​estimate Approximation error estimate and an estimate of the unknown total disturbance Guidance module for all-wheel drive ships: used to estimate the position, bow angle η, speed information ν, and high-order learning observer based on the output of the all-wheel drive ship motion model Estimated value of the unknown total disturbance and an estimate of the approximation error Design guidance methods for fully driven ships and generate guidance signals for ships; Parallel trajectory tracking controller module: It is used to design a parallel trajectory tracking controller based on the ship's guidance signal and the estimated position and heading angle, estimated speed, estimated weight, and estimated approximation error information output by the high-order learning observer. It is used to generate control signals for the ship's movement and realize the control of the all-wheel drive ship to track along parallel trajectories.

8. A computer device comprising: A processor and a memory, wherein the memory stores a program module, wherein the program module runs on the processor to implement the method according to any one of claims 1 to 6.

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