All-wheel-driven unmanned ship parallel path tracking control method and device based on polynomial approximation modeling

Abstract

The invention discloses an all-drive unmanned ship parallel path tracking control method based on polynomial approximation modeling, and the method comprises the following steps: carrying out the parallel path tracking control of a virtual all-drive unmanned ship system based on the position information and yawing angle information of the virtual all-drive unmanned ship system under an earth coordinate system, the speed information of the virtual all-drive unmanned ship system under a ship body coordinate system, and the observed unknown total disturbance information in dynamics; a virtual parallel path tracking controller is established and used for sending a virtual control signal, and the virtual unmanned ship system controls input force and torque, so that control over the virtual full-drive unmanned ship system is achieved; based on the position and the yawing angle of an actual unmanned ship in an earth coordinate system, the speed of the actual unmanned ship in a ship body coordinate system and virtual control signal information sent by a virtual parallel path tracking controller, an actual controller is established and used for actual control signals, namely control force and torque generated in the transverse direction, the longitudinal direction and the rudder angle direction. Accurate control and operation of the actual unmanned ship are realized.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned boat control and relates to a parallel path tracking control method for an all-wheel drive unmanned boat based on polynomial approximation modeling. Background Art

[0002] Against the backdrop of the intelligent development of marine equipment, unmanned vehicles (UAVs), with their lightweight structure, flexible motion, functional scalability, and operational safety, have established a strategic position in marine development. Research on path-tracking control technology is a hot topic in the field of UAV motion control and has attracted significant attention in academia in recent years. Currently, with increasing application requirements, high-precision path-tracking control in complex marine environments has become a pressing technical challenge. L. Liu, addressing the path-tracking control problem of an underactuated UAV with a completely unknown dynamic model, combined a data-driven neural network predictor with integral parallel learning to propose a model-free dynamics controller to achieve path-tracking control of the underactuated UAV under marine environmental disturbances. S. Dai, addressing the path-tracking control problem of a fully actuated UAV, proposed an adaptive neural network output-feedback path-tracking controller, enabling the UAV to track a given reference path even when its speed is unmeasurable. Z. Zheng, addressing the path-tracking problem of an UAV under input saturation constraints, proposed a robust adaptive radial basis function neural network control method. This method adaptively compensates for system uncertainties through neural network adaptation and effectively handles input saturation constraints, achieving stable path-tracking control by combining robust control techniques. L. Liu used a single hidden layer neural network to approximate the system dynamics uncertainty and achieved path tracking control for an underactuated unmanned vehicle. P. Tee used a feedforward neural network to approximate the uncertainty of the ship and environmental disturbances and designed a robust adaptive trajectory tracking controller. Z. Peng used a fuzzy logic system to design a fuzzy predictor to effectively approximate the unknown dynamics for the path tracking control of an unmanned vehicle with unknown dynamics. He then combined this with a neural dynamics optimization algorithm to achieve path tracking under speed constraints.

[0003] However, the existing path tracking control method of unmanned boats still has some shortcomings in the following aspects:

[0004] First, although existing uncertainty approximation methods based on neural networks and fuzzy logic systems can effectively compensate for the nonlinear characteristics related to the system state, the modeling accuracy of time-varying ocean environmental disturbances is insufficient, resulting in distortion of control system model information, which seriously restricts the improvement of control performance.

[0005] Second, existing neural network models suffer from poor interpretability. Neural networks are essentially complex nonlinear mapping functions, whose internal structure consists of a large number of weight parameters and activation functions. These models exhibit typical "black box" characteristics. The model's computational process is highly complex and opaque, making it difficult to accurately analyze how each layer affects the output. Typically, one can only observe the relationship between input and output, but lacks a clear understanding of how the model's internal decisions are made.

[0006] Third, the design of most existing path-tracking controllers relies heavily on the real-time motion state of the actual UAV. While this approach can meet control requirements to a certain extent, it often neglects the in-depth analysis and utilization of the established UAV motion model information, failing to effectively integrate this information into the control strategy, thus limiting further improvements in 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 path tracking control method for an all-wheel drive unmanned boat based on polynomial approximation modeling, comprising the following steps:

[0008] The motion model of the unmanned boat is established based on the external disturbances caused by wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and bow angle directions.

[0009] Based on the position, pitch angle, velocity, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, as well as the input force and torque for controlling the virtual unmanned boat system, a virtual fully-driven unmanned boat system model is established;

[0010] Based on the position information and pitch angle information of the virtual fully-driven unmanned boat system in the earth coordinate system, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics, a virtual parallel path tracking controller is established to issue virtual control signals and control input forces and torques of the virtual unmanned boat system to realize the control of the virtual fully-driven unmanned boat system.

[0011] Based on the position of the actual unmanned boat in the earth coordinate system, the bow angle, the speed in the hull coordinate system, and the virtual control signal information sent by the virtual parallel path tracking controller, an actual controller is established to use the actual control signal, that is, the control force and torque generated in the lateral, longitudinal, and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

[0012] Furthermore: the unmanned boat motion model is as follows:

[0013] The motion model of the unmanned boat is described by the kinematic and dynamic equations of the unmanned boat as follows:

[0014]

[0015] in represents the vector formed by the actual position (x, y) of the unmanned boat in the earth coordinate system and the bow angle information θ; represents the vector formed by the longitudinal velocity u, transverse velocity v and bow angular velocity r of the actual unmanned boat in the hull coordinate system; are the Coriolis and centripetal force matrices, and is the damping matrix, and (C(v)+D(v)) represents the uncertainty of the internal model of the unmanned boat system; Indicates external environmental interference caused by wind, waves, etc. in a complex ocean environment; represents the control input force of the unmanned boat, τ u is the longitudinal thrust, τ v is the lateral thrust, τ r is the yaw moment; is the derivative of η; is the derivative of ν; is the inertial mass matrix, m u ,m v ,m r are the inertial mass parameters in the longitudinal, transverse and yaw angle directions respectively; The specific form of the rotation matrix is ​​as follows:

[0016]

[0017] Furthermore, the process of establishing a virtual fully-driven unmanned boat system model based on the position, pitch angle, velocity, acceleration, and input force and torque of the actual fully-driven unmanned boat system in the earth coordinate system is as follows:

[0018] The dynamics formula of the fully driven unmanned boat can be re-expressed in the following concise form:

[0019]

[0020] in, represents the nominal value of the inertia matrix M; Δ represents the unknown total disturbance information in the dynamics, and the specific form is as follows:

[0021] Δ=M -1 (-C(ν)-D(ν)+β(t)+τ ν )-(M * ) -1 τ ν (4)

[0022] The uncertainty C(ν)+D(ν) of the internal model of the unmanned boat system is approximated using the Taylor series expansion theorem, and the external total disturbance β(t) is approximated using the Fourier series expansion theorem. Therefore, the polynomial regression vector in the unknown total disturbance fitting term is designed as follows:

[0023]

[0024] By E * To approximate the unknown total disturbance information in dynamics, where n1,ω is a positive constant;

[0025] Based on the above formula (4), the unknown total disturbance in the unmanned boat dynamics is Rewrite it as follows:

[0026]

[0027] where Δ u ,Δ v ,Δ r They represent the longitudinal disturbance, lateral disturbance and bow angle disturbance of the unmanned boat respectively; represents a bounded ideal weight; represents the regression vector matrix; represents the approximation error, Represent the approximation errors in the longitudinal, transverse and yaw angle directions respectively.

[0028] The virtual full-drive unmanned boat system model is designed as follows:

[0029]

[0030] In the formula Indicates the acceleration information of the actual unmanned boat system; represents the estimated position and bow angle information η of the unmanned vehicle; represents the estimated value of the actual unmanned boat's velocity information ν in the hull coordinate system; Represents the approximation error The estimated value of denote the estimated values ​​of the approximation errors in the longitudinal, transverse and yaw angle directions respectively; represents the ideal weight of which is a positive definite matrix, is a positive adaptive gain.

[0031] Furthermore, the process of establishing a virtual parallel path tracking controller based on the position information, pitch angle information, speed information in the hull coordinate system, and unknown total disturbance information in the observed dynamics of the virtual fully driven unmanned boat system in the earth coordinate system is as follows:

[0032] Introducing a reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T ;The input signal is the path tracking error e η , reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T , reference path tangent angle ε is the path parameter, the estimate of the UAV position state sent by the virtual UAV system Speed ​​estimation and the unknown total perturbation information in the observed dynamics The output signal is the control signal τ sent to the virtual unmanned boat system and the actual controller ν ;

[0033] Path tracking error e η and its derivative is defined as:

[0034]

[0035] in: It is e η The derivative with respect to time, ν0 represents the path update speed, and ε0 is a designed variable. The specific form is as follows:

[0036]

[0037] in: is a positive constant;

[0038] Design the desired velocity guidance signal φ ν =[φ u ,φ v ,φ r ] T As follows:

[0039]

[0040] Among them: variable

[0041] Define the velocity update error ν e , ν e The time derivative can be expressed as follows:

[0042]

[0043] The virtual parallel path tracking controller is designed as follows:

[0044]

[0045] The variables

[0046] A parallel path tracking control device for an all-wheel drive unmanned boat based on polynomial approximation modeling, comprising:

[0047] The module for establishing the motion model of the unmanned boat is used to establish the motion model of the unmanned boat based on the external disturbances caused by the wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and bow angle directions.

[0048] Establishing a virtual fully-driven unmanned boat system model module: This module is used to establish a virtual fully-driven unmanned boat system model based on the position, bow angle, velocity, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, as well as the input force and torque for controlling the virtual unmanned boat system;

[0049] Establish a virtual parallel path tracking controller module: Based on the position information, bow angle information, speed information in the hull coordinate system, and unknown total disturbance information in the observed dynamics of the virtual fully-driven unmanned boat system in the earth coordinate system, a virtual parallel path tracking controller is established to issue virtual control signals and control input forces and torques of the virtual unmanned boat system to realize the control of the virtual fully-driven unmanned boat system;

[0050] An actual controller module is established to establish an actual controller based on the position of the actual unmanned boat in the earth coordinate system, the bow angle, the speed in the hull coordinate system and the virtual control signal information sent by the virtual parallel path tracking controller. The actual controller is used for the actual control signal, that is, the control force and torque generated in the lateral, longitudinal and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

[0051] 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.

[0052] The present invention provides a parallel path tracking control method for an all-wheel drive unmanned vehicle based on polynomial approximation modeling, which has the following advantages:

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The parallel path tracking control method for a fully driven unmanned boat based on polynomial approximation modeling proposed in this application effectively overcomes the shortcomings of existing unmanned boat control technologies and has the following significant advantages:

[0055] First, compared with the existing online modeling methods based on neural networks and fuzzy logic systems, the present invention designs a modeling method based on polynomial approximation, which overcomes the insufficient ability of neural networks and fuzzy logic systems in expressing the dynamic characteristics of ships, and can simultaneously approximate the ship state and external time-varying environmental disturbances, effectively improving the accuracy of ship motion modeling.

[0056] Second, compared with the existing unmanned vehicle dynamics approximation method based on neural networks, the present invention designs an approximation method based on Fourier series expansion and Taylor series expansion. This method not only achieves the efficient extraction and accurate expression of the key features of the comprehensive information of internal uncertainty of the ship and external environmental disturbance, but also overcomes the inherent limitations of the "black box" characteristics of traditional neural networks and improves the interpretability of the ship model.

[0057] Third, compared with the existing path tracking controller designed based on the actual state of the unmanned boat, the present invention designs a parallel path tracking controller, which fully utilizes the established virtual unmanned boat motion model information and the estimated unknown total disturbance information during the controller design process, thereby improving the anti-interference ability and stability of the unmanned boat path tracking control. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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.

[0059] Figure 1 This is a design diagram of the parallel path tracking control method for a fully driven unmanned boat;

[0060] Figure 2 This is a simulation diagram of the path tracking of a fully driven unmanned boat;

[0061] Figure 3 This is the simulation result of tracking error of the fully driven unmanned boat;

[0062] Figure 4 This is the estimated diagram of the actual position and heading angle of the proposed virtual unmanned boat model; (a) the actual value of the unmanned boat's horizontal coordinate and the estimated value of the unmanned boat's horizontal coordinate, (b) the actual value of the unmanned boat's vertical coordinate and the estimated value of the unmanned boat's vertical coordinate, (c) the actual value of the unmanned boat's bow pitch angle and the estimated value of the unmanned boat's bow pitch angle;

[0063] Figure 5 Figure 1 shows the estimated actual speed of the proposed virtual unmanned boat model, including (a) the actual value of the unmanned boat's lateral speed and the estimated value of the unmanned boat's lateral speed, (b) the actual value of the unmanned boat's longitudinal speed and the estimated value of the unmanned boat's longitudinal speed, and (c) the actual value of the unmanned boat's bow angular velocity and the estimated value of the unmanned boat's bow angular velocity.

[0064] Figure 6 Figure 3 is the estimated diagram of the unknown total disturbance of the proposed virtual unmanned boat model, including (a) the actual value of the unknown total disturbance in the lateral direction of the unmanned boat and the estimated value of the unknown total disturbance in the lateral direction of the unmanned boat, (b) the actual value of the unknown total disturbance in the longitudinal direction of the unmanned boat and the estimated value of the unknown total disturbance in the longitudinal direction of the unmanned boat, and (c) the actual value of the unknown total disturbance in the bow swing direction of the unmanned boat and the estimated value of the unknown total disturbance in the bow swing direction of the unmanned boat. DETAILED DESCRIPTION

[0065] 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.

[0066] 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.

[0067] A parallel path tracking control method for an all-wheel drive unmanned watercraft based on polynomial approximation modeling includes the following steps:

[0068] S1: Establish a motion model of the unmanned boat based on the external disturbances caused by wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and yaw directions;

[0069] S2: Based on the position and pitch angle information, velocity information, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system output by the unmanned boat motion model, as well as the input force and torque for controlling the virtual unmanned boat system, a virtual fully-driven unmanned boat system model is established;

[0070] S3: Based on the position information and pitch angle information of the virtual fully-driven unmanned boat system in the earth coordinate system, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics, a virtual parallel path tracking controller is established to issue a virtual control signal and control input force and torque of the virtual unmanned boat system to realize control of the virtual fully-driven unmanned boat system;

[0071] S4: Based on the actual unmanned boat position in the earth coordinate system, bow angle, speed in the hull coordinate system output by the unmanned boat motion model and the virtual control signal information sent by the virtual parallel path tracking controller, an actual controller is established for the actual control signal, that is, the control force and torque generated in the lateral, longitudinal and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

[0072] Steps S1 / S2 / S3 / S4 are performed sequentially;

[0073] Furthermore: the unmanned boat motion model is as follows:

[0074] The motion model of the unmanned boat is described by the kinematic and dynamic equations of the unmanned boat as follows:

[0075]

[0076] in represents the vector formed by the actual position (x, y) of the unmanned boat in the earth coordinate system and the bow angle information θ; represents the vector formed by the longitudinal velocity u, transverse velocity v and bow angular velocity r of the actual unmanned boat in the hull coordinate system; are the Coriolis and centripetal force matrices, and is the damping matrix, and (C(v)+D(v)) represents the uncertainty of the internal model of the unmanned boat system; Indicates external environmental interference caused by wind, waves, etc. in a complex ocean environment; represents the control input force of the unmanned boat, τ u is the longitudinal thrust, τ v is the lateral thrust, τ r is the yaw moment; is the derivative of η; is the derivative of ν; is the inertial mass matrix, m u ,m v ,m r are the inertial mass parameters in the longitudinal, transverse and yaw angle directions respectively; The specific form of the rotation matrix is ​​as follows:

[0077]

[0078] The input signal of the virtual unmanned boat system is the position and bow angle information of the actual full-drive unmanned boat system η=[x,y,θ] T , velocity information ν, acceleration information and the control input force and torque τ generated by the virtual parallel path tracking controller ν The output signal is the position and pitch angle information of the virtual unmanned boat system in the earth coordinate system. Velocity information in the hull coordinate system and the observed value of the unknown total disturbance

[0079] The process of establishing a virtual fully-driven unmanned boat system model based on the position and pitch angle information, velocity information, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, and the input force and torque for controlling the virtual unmanned boat system is as follows:

[0080] The dynamics formula of the fully driven unmanned boat can be re-expressed in the following concise form:

[0081]

[0082] in, represents the nominal value of the inertia matrix M; Δ represents the unknown total disturbance information in the dynamics, and the specific form is as follows:

[0083] Δ=M -1 (-C(ν)-D(ν)+β(t)+τ ν )-(M * ) -1 τ ν (4)

[0084] The uncertainty C(ν)+D(ν) of the internal model of the unmanned boat system is approximated using the Taylor series expansion theorem, and the external total disturbance β(t) is approximated using the Fourier series expansion theorem. Therefore, the polynomial regression vector in the unknown total disturbance fitting term is designed as follows:

[0085]

[0086] By E * To approximate the unknown total disturbance information in dynamics, where n1,ω is a positive constant;

[0087] Based on the above formula (4), the unknown total disturbance in the unmanned boat dynamics is Rewrite it as follows:

[0088]

[0089] where Δ u ,Δv ,Δ r They represent the longitudinal disturbance, lateral disturbance and bow angle disturbance of the unmanned boat respectively; represents a bounded ideal weight; represents the regression vector matrix; represents the approximation error, Represent the approximation errors in the longitudinal, transverse and yaw angle directions respectively.

[0090] The virtual full-drive unmanned boat system model is designed as follows:

[0091]

[0092] In the formula Indicates the acceleration information of the actual unmanned boat system; represents the estimated position and bow angle information η of the unmanned vehicle; represents the estimated value of the actual unmanned boat's velocity information ν in the hull coordinate system; Represents the approximation error The estimated value of denote the estimated values ​​of the approximation errors in the longitudinal, transverse and yaw angle directions respectively; represents the ideal weight of which is a positive definite matrix, is a positive adaptive gain.

[0093] The process of establishing a virtual parallel path tracking controller based on the position information and pitch angle information of the virtual fully driven unmanned boat system in the earth coordinate system, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics is as follows:

[0094] Introducing a reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T ;The input signal is the path tracking error e η , reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T , reference path tangent angle ε is the path parameter, the estimate of the UAV position state sent by the virtual UAV system Speed ​​estimation and the unknown total perturbation information in the observed dynamics The output signal is the control signal τ sent to the virtual unmanned boat system and the actual controllerν ;

[0095] Path tracking error e η and its derivative is defined as:

[0096]

[0097] in: It is e η The derivative with respect to time, ν0 represents the path update speed, and ε0 is a designed variable. The specific form is as follows:

[0098]

[0099] in: is a positive constant;

[0100] Design the desired velocity guidance signal φ ν =[φ u ,φ v ,φ r ] T As follows:

[0101]

[0102] Among them: variable

[0103] Define the velocity update error ν e , ν e The time derivative can be expressed as follows:

[0104]

[0105] The virtual parallel path tracking controller is designed as follows:

[0106]

[0107] The variables

[0108] A parallel path tracking control device for an all-wheel drive unmanned boat based on polynomial approximation modeling, comprising:

[0109] The module for establishing the motion model of the unmanned boat is used to establish the motion model of the unmanned boat based on the external disturbances caused by the wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and bow angle directions.

[0110] Establishing a virtual fully-driven unmanned boat system model module: This module is used to establish a virtual fully-driven unmanned boat system model based on the position, pitch angle, velocity, and acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system output by the unmanned boat motion model module, as well as the input force and torque for controlling the virtual unmanned boat system;

[0111] Establish a virtual parallel path tracking controller module: It is used to establish the virtual full-drive unmanned boat system model module based on the position information of the virtual full-drive unmanned boat system in the earth coordinate system, the bow angle information, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics. The virtual parallel path tracking controller is established to send virtual control signals and control the input force and torque of the virtual unmanned boat system to achieve control of the virtual full-drive unmanned boat system.

[0112] An actual controller module is established to establish an actual controller based on the position of the actual unmanned boat in the earth coordinate system, the bow angle, and the speed in the hull coordinate system output by the unmanned boat motion model module and the virtual control signal information output by the virtual parallel path tracking controller module. The actual controller is used for actual control signals, that is, the control force and torque generated in the lateral, longitudinal, and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

[0113] The unmanned boat motion model establishment module receives external disturbances caused by wind, waves and currents in the ocean environment and the control forces and torques generated by the actual controller in the lateral, longitudinal and pitch angle directions; the unmanned boat motion model establishment module sends the position information, pitch angle information and speed information and acceleration information of the actual unmanned boat in the earth coordinate system and the hull coordinate system to the virtual full-drive unmanned boat system model establishment module;

[0114] The module for establishing a model of a virtual fully-driven unmanned boat system receives position information, pitch angle information, and speed information and acceleration information in a hull coordinate system of the actual fully-driven unmanned boat system, and control forces and torques generated in the lateral, longitudinal, and pitch angle directions by a virtual parallel path tracking controller from the module for establishing a model of a virtual fully-driven unmanned boat system; the module for establishing a model of a virtual fully-driven unmanned boat system sends the position information, pitch angle information, speed information in a hull coordinate system, and unknown total disturbance information in the observed dynamics of the virtual unmanned boat to the module for establishing a virtual parallel path tracking controller;

[0115] The virtual parallel path tracking controller module receives the position information of the virtual unmanned boat in the earth coordinate system, the pitch angle information, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics from the virtual unmanned boat system, and sends the control instructions to the virtual unmanned boat system, and at the same time sends the processed reference information to the actual controller module;

[0116] The actual controller module receives reference information from the virtual parallel path tracking controller, and also receives information such as the actual unmanned boat's position in the earth coordinate system, bow angle, and speed in the hull coordinate system fed back by the actual unmanned boat system; the actual controller module sends the control force and torque generated in the lateral, longitudinal, and bow angle directions to the actual unmanned boat system to achieve precise control and operation of the actual unmanned boat.

[0117] 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.

[0118] 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 First, the actual unmanned boat system will collect the position and heading angle η, speed ν and acceleration of the unmanned boat in real time. Then, in the virtual space, based on the actual speed information of the unmanned boat, a polynomial regression vector E is constructed using the polynomial fitting method. * Then, by combining the polynomial regression vector and the state information of the actual unmanned boat, and designing an appropriate adaptive weight update law, a virtual unmanned boat model that can reflect the motion characteristics of the actual unmanned boat in real time is established in the virtual space. Finally, the virtual controller integrates the position and heading angle information from the virtual unmanned boat model. Speed ​​information Total disturbance information And the reference trajectory information η c (ε), generating the parallel path control signal τ ν ,This signal acts on the virtual unmanned boat system and the actual controller at the same time, thereby realizing high-precision path tracking control of the fully driven unmanned boat.

[0119] Consider a simulation scenario where an unmanned vehicle follows a given reference path. The initial state of the unmanned vehicle is defined as η0 = [5m, 2m, 3π / 4rad] T and ν0=[0m / s,0m / s,0rad / s] T , and the given parameterized path is: η c(ε)=[0.2ε,30sin(0.01ε),atan2(0.3cos(0.01ε),0.2)] T .

[0120] Other simulation-related parameters are selected as follows: the observation gain of the virtual unmanned vehicle system is set to ρ1 = ρ2 = diag{10, 10, 10}, ρ3 = diag{6, 6, 6}, the adaptive gain is set to ρ4 = 0.3, and the control gain is set to k1 = diag{0.1, 0.1, 0.1}, σ1 = 1, k2 = diag{5, 10, 3}, and σ2 = 1.

[0121] The simulation results are as follows Figures 2 to 6 . Figure 2 The path tracking performance of the unmanned boat under the method proposed in this invention is demonstrated, and it can be seen that the actual trajectory of the unmanned boat is consistent with the given reference path.

[0122] Figure 3 The longitudinal, lateral, and path tangential angle tracking error curves are presented. Further analysis of the tracking errors shows that the tracking errors in the three directions converge to near zero after a transient process, further demonstrating the effectiveness of the proposed method. Figure 4 This is the estimated diagram of the actual position and heading angle of the proposed virtual unmanned boat model; (a) the actual value of the unmanned boat's horizontal coordinate and the estimated value of the unmanned boat's horizontal coordinate, (b) the actual value of the unmanned boat's vertical coordinate and the estimated value of the unmanned boat's vertical coordinate, (c) the actual value of the unmanned boat's bow pitch angle and the estimated value of the unmanned boat's bow pitch angle; Figure 4 It can be seen that the position and heading angle of the actual unmanned boat (indicated by the solid line) and the virtual unmanned boat (indicated by the dotted line) are basically the same.

[0123] Figure 5 Figure 1 shows the estimated actual speed of the proposed virtual unmanned boat model, including (a) the actual value of the unmanned boat's lateral speed and the estimated value of the unmanned boat's lateral speed, (b) the actual value of the unmanned boat's longitudinal speed and the estimated value of the unmanned boat's longitudinal speed, and (c) the actual value of the unmanned boat's bow angular velocity and the estimated value of the unmanned boat's bow angular velocity.

[0124] Figure 5 It can be seen that the forward speed, lateral speed, and pitch angular velocity of the actual unmanned boat (represented by the solid line) and the virtual unmanned boat (represented by the dotted line) are basically the same. This shows that the virtual unmanned boat model technology constructed by this invention can achieve ship-shore state synchronization.

[0125] Figure 6Figure 3 is the estimated diagram of the unknown total disturbance of the proposed virtual unmanned boat model, including (a) the actual value of the unknown total disturbance in the lateral direction of the unmanned boat and the estimated value of the unknown total disturbance in the lateral direction of the unmanned boat, (b) the actual value of the unknown total disturbance in the longitudinal direction of the unmanned boat and the estimated value of the unknown total disturbance in the longitudinal direction of the unmanned boat, and (c) the actual value of the unknown total disturbance in the bow swing direction of the unmanned boat and the estimated value of the unknown total disturbance in the bow swing direction of the unmanned boat.

[0126] Figure 6 It is shown that the proposed method can effectively estimate the unknown total disturbance.

[0127] 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 path tracking control method for an all-wheel drive unmanned vehicle based on polynomial approximation modeling, characterized by: The following steps are involved: The motion model of the unmanned boat is established based on the external disturbances caused by wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and bow angle directions. Based on the position, pitch angle, velocity, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, as well as the input force and torque for controlling the virtual unmanned boat system, a virtual fully-driven unmanned boat system model is established; Based on the position information and pitch angle information of the virtual fully-driven unmanned boat system in the earth coordinate system, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics, a virtual parallel path tracking controller is established to issue virtual control signals and control input forces and torques of the virtual unmanned boat system to realize the control of the virtual fully-driven unmanned boat system. Based on the position of the actual unmanned boat in the earth coordinate system, the bow angle, the speed in the hull coordinate system, and the virtual control signal information sent by the virtual parallel path tracking controller, an actual controller is established to use the actual control signal, that is, the control force and torque generated in the lateral, longitudinal, and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

2. The parallel path tracking control method for an all-wheel drive unmanned vehicle based on polynomial approximation modeling according to claim 1, characterized in that: The unmanned boat motion model is as follows: The motion model of the unmanned boat is described by the kinematic and dynamic equations of the unmanned boat as follows: in represents the vector formed by the actual position (x, y) of the unmanned boat in the earth coordinate system and the bow angle information θ; represents the vector formed by the longitudinal velocity u, transverse velocity v and bow angular velocity r of the actual unmanned boat in the hull coordinate system; are the Coriolis and centripetal force matrices, and is the damping matrix, and (C(v)+D(v)) represents the uncertainty of the internal model of the unmanned boat system; Indicates external environmental interference caused by wind, waves, etc. in a complex ocean environment; represents the control input force of the unmanned boat, τ u is the longitudinal thrust, τ v is the lateral thrust, τ r is the yaw moment; is the derivative of η; is the derivative of ν; is the inertial mass matrix, m u ,m v ,m r are the inertial mass parameters in the longitudinal, transverse and yaw angle directions respectively; The specific form of the rotation matrix is ​​as follows:

3. The parallel path tracking control method for an all-wheel drive unmanned vehicle based on polynomial approximation modeling according to claim 1, characterized in that: The process of establishing a virtual fully-driven unmanned boat system model based on the position and pitch angle information, velocity information, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, and the input force and torque for controlling the virtual unmanned boat system is as follows: The dynamics formula of the fully driven unmanned boat can be re-expressed in the following concise form: in, represents the nominal value of the inertia matrix M; Δ represents the unknown total disturbance information in the dynamics, and the specific form is as follows: Δ=M -1 (-C(ν)-D(ν)+β(t)+τ ν )-(M * ) -1 t ν (4) The uncertainty C(ν)+D(ν) of the internal model of the unmanned boat system is approximated using the Taylor series expansion theorem, and the external total disturbance β(t) is approximated using the Fourier series expansion theorem. Therefore, the polynomial regression vector in the unknown total disturbance fitting term is designed as follows: By E * To approximate the unknown total disturbance information in dynamics, where n1,ω is a positive constant; Based on the above formula (4), the unknown total disturbance in the unmanned boat dynamics is Rewrite it as follows: where Δ u ,Δ v ,Δ r They represent the longitudinal disturbance, lateral disturbance and bow angle disturbance of the unmanned boat respectively; represents a bounded ideal weight; represents the regression vector matrix; represents the approximation error, Represent the approximation errors in the longitudinal, transverse and yaw angle directions respectively. The virtual full-drive unmanned boat system model is designed as follows: In the formula Indicates the acceleration information of the actual unmanned boat system; represents the estimated position and bow angle information η of the unmanned vehicle; represents the estimated value of the actual unmanned boat's velocity information ν in the hull coordinate system; Represents the approximation error The estimated value of denote the estimated values ​​of the approximation errors in the longitudinal, transverse and yaw angle directions respectively; represents the ideal weight of which is a positive definite matrix, is a positive adaptive gain.

4. The parallel path tracking control method for an all-wheel drive unmanned vehicle based on polynomial approximation modeling according to claim 1, characterized in that: The process of establishing a virtual parallel path tracking controller based on the position information and pitch angle information of the virtual fully driven unmanned boat system in the earth coordinate system, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics is as follows: Introducing a reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T ; The input signal is the path tracking error e η , reference path η c (ε)=[x c (ε),y c (ε),θ c (ε)] T , reference path tangent angle ε is the path parameter, the estimate of the UAV position state sent by the virtual UAV system Speed ​​estimation and the unknown total perturbation information in the observed dynamics The output signal is the control signal τ sent to the virtual unmanned boat system and the actual controller ν ; Path tracking error e η and its derivative is defined as: in: It is e η The derivative with respect to time, ν0 represents the path update speed, and ε0 is a designed variable. The specific form is as follows: in: is a positive constant; Design the desired velocity guidance signal φ ν =[φ u ,φ v ,φ r ] T As follows: Among them: variable Define the velocity update error ν e , ν e The time derivative can be expressed as follows: The virtual parallel path tracking controller is designed as follows: The variables 5. A parallel path tracking control device for an all-wheel drive unmanned boat based on polynomial approximation modeling, characterized by: include: The module for establishing the motion model of the unmanned boat is used to establish the motion model of the unmanned boat based on the external disturbances caused by the wind, waves and currents in the marine environment, as well as the control forces and torques generated by the actual fully-driven unmanned boat system in the lateral, longitudinal and bow angle directions. Establishing a virtual fully-driven unmanned boat system model module: This module is used to establish a virtual fully-driven unmanned boat system model based on the position, bow angle, velocity, acceleration information of the actual fully-driven unmanned boat system in the earth coordinate system, as well as the input force and torque for controlling the virtual unmanned boat system; Establish a virtual parallel path tracking controller module: Based on the position information of the virtual fully-driven unmanned boat system in the earth coordinate system, the bow angle information, the speed information in the hull coordinate system, and the unknown total disturbance information in the observed dynamics, establish a virtual parallel path tracking controller to issue virtual control signals and control the input force and torque of the virtual unmanned boat system to achieve control of the virtual fully-driven unmanned boat system; An actual controller module is established to establish an actual controller based on the position of the actual unmanned boat in the earth coordinate system, the bow angle, the speed in the hull coordinate system and the virtual control signal information sent by the virtual parallel path tracking controller. The actual controller is used for the actual control signal, that is, the control force and torque generated in the lateral, longitudinal and bow angle directions, to achieve precise control and operation of the actual unmanned boat.

6. 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 4.

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