A method for model identification and evaluation of a twin-propeller unmanned ship model with sparse nonlinear dynamics

Abstract

The present application relates to the field of double propeller unmanned ship model predictive control, and particularly relates to a double propeller unmanned ship model identification evaluation method for sparse nonlinear dynamics identification, comprising the following steps: combining a three-degree-of-freedom maneuvering model with double propeller unmanned ship propeller characteristics to establish a double propeller unmanned ship model; building a double propeller unmanned ship model identification device, collecting control input instructions and state data of the unmanned ship synchronously by autonomously controlling the unmanned ship to run a Z-shaped trajectory motion; establishing a double propeller unmanned ship model candidate function library, and solving to-be-identified parameters of the double propeller unmanned ship model by using a sequential threshold least square method; substituting the to-be-identified parameters of the double propeller unmanned ship model into the double propeller unmanned ship model to perform Z-shaped and spiral experiment verification, and quantitatively evaluating the precision of the double propeller unmanned ship model through root mean square error and correlation quantity. The present application can significantly improve the precision and efficiency of dynamics modeling, and provide reliable technical support for the development of an autonomous control system of a double propeller driven unmanned ship.

Description

Technical Field

[0001] This invention relates to the field of predictive control of dual-propeller unmanned surface vessels (USVs), and more specifically to a method for identifying and evaluating USV models based on sparse nonlinear dynamics. Background Technology

[0002] With the rapid development of intelligent control technology, dual-propeller unmanned surface vessels (USVs) are widely used in water quality sampling, surface rescue, water area cleaning, and patrol monitoring. The control systems of these USVs are essentially multi-input multi-output nonlinear systems, and their control performance is highly dependent on the accuracy of the dynamic model. Chinese patent CN119861550A discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, including the following steps: first, preset path points and obtain real-time coordinates; calculate the actual desired heading angle and the observed bow angle of the ship, subtract them to obtain the heading deviation, and calculate the heading deviation rate; input the heading deviation and heading deviation rate into the fuzzy adaptive PID motion control module, and the fuzzy control algorithm outputs gain parameters; calculate the ship's rudder angle using the gain parameters and the input PID controller; update the motion state using the input motion mathematical model, forming a closed-loop update of the motion state to bring the ship closer to the preset path point; after updating the state information, determine the ship's path. However, this method is difficult to effectively handle the system coupling characteristics and cannot consider the state and input constraints of the USV. In contrast, nonlinear model predictive control can naturally incorporate system constraints, but its control effect is directly limited by the accuracy of the underlying model.

[0003] The core challenge in ship dynamics modeling lies in the accurate identification of hydrodynamic parameters. Another Chinese patent, CN110647041A, discloses a method for accurate identification of all coefficients of an unmanned surface vessel (USV) model. This method utilizes a structure for accurate identification of all coefficients of the USV model, which includes a filter module, an integral filter module, an online parameter estimation module, and a ship model. However, the traditional full-coefficient identification method used in this invention requires extensive towing and boom-arm tests to determine parameters such as added mass and damping coefficients. This results in high experimental costs and a lengthy process. For small USVs, this mechanism-based parameter identification method has significant limitations in terms of both economy and feasibility.

[0004] Another Chinese patent, CN116702320A, discloses a method for identifying parameters of an unmanned surface vessel (USV) response model based on an improved particle swarm optimization (PSO) algorithm. While the improved PSO algorithm avoids reliance on prior knowledge, its genetic operation mechanism leads to an exponential increase in computational complexity with the parameter dimension. Chinese patent CN117951817A discloses a method, device, equipment, and medium for identifying a USV dynamics model. Although this invention improves computational efficiency through gradient descent, it does not introduce sparsity constraints and is prone to overfitting under noise interference. Summary of the Invention

[0005] To address the issues of high redundancy in hydrodynamic parameter calculations and high complexity in data-driven identification models in existing technologies, this invention proposes a model identification method for dual-propeller unmanned surface vessels (USVs) based on sparse nonlinear dynamics identification. By constructing a closed-loop technology encompassing "modeling-data acquisition-parameter identification-verification and evaluation," it achieves high-precision and high-efficiency identification of USV dynamic parameters. This method is particularly suitable for the rapid dynamic modeling needs of small research USVs and demonstrates significant advantages in applications such as water quality monitoring, waterway patrol, and waste disposal. By providing a high-precision, low-cost model identification solution, it powerfully promotes the transition of USV control technology from the laboratory to practical applications.

[0006] This invention provides a method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics, comprising the following steps:

[0007] Based on the relative positional relationship between the inertial coordinate system and the hull coordinate system, the motion characteristics and hull characteristics of the dual-propeller unmanned vessel are labeled, a three-degree-of-freedom control model is established, and the three-degree-of-freedom control model is combined with the propulsion characteristics of the dual-propeller unmanned vessel to establish a dual-propeller unmanned vessel model.

[0008] A dual-propeller unmanned surface vessel (USV) model identification device was built. The USV was autonomously controlled to move in a Z-shaped trajectory. The control input commands and status data of the USV were collected simultaneously and used as the basis for solving the identification parameters of the dual-propeller USV model.

[0009] A candidate function library for dual-propeller unmanned surface vessel (USV) models containing second-order polynomial terms is established based on a sparse nonlinear dynamics identification framework. The parameters to be identified for the dual-propeller USV model are solved by the sequential threshold least squares method.

[0010] The parameters to be identified in the dual-propeller unmanned surface vessel (USV) model were substituted into the model for Z-shaped and gyroscopic experiments. The accuracy of the USV model was quantitatively evaluated by the root mean square error and correlation coefficient.

[0011] Furthermore, the process of establishing the dual-propeller unmanned surface vessel model includes:

[0012] Using the northeast coordinate system of a standard ship as the inertial coordinate system Constructing the hull coordinate system of the dual-propeller unmanned surface vessel Relative positional relationships, marking the motion characteristics and hull features of the dual-propeller unmanned surface vessel;

[0013] The center of gravity and center of mass of the unmanned vessel coincide. Ignoring wind, wave, and current interference, a three-degree-of-freedom maneuvering model for the dual-propeller unmanned vessel is established:

[0014]

[0015] in Represents the velocity vector. , , These represent the longitudinal velocity, lateral velocity, and bow angular velocity in the body coordinate system, respectively. Represents a position vector. Represents the heading angle in the inertial coordinate system. The rotation moment represents the rotational transformation matrix that transforms the velocity vector from the system frame to the inertial frame. Represents the total mass matrix. Represents the Coriolis centripetal force matrix. Represents the damping matrix. express , directional thrust and Directional torque.

[0016] The propeller's power output is controlled by adjusting the pulse width of the pulse width modulation (PWM) signal. The PWM signal vector controlling the left or right thrust is defined as follows: By combining the three-degree-of-freedom maneuvering model with the characteristics of a dual-propeller unmanned surface vessel (USV), the following dual-propeller USV model is established:

[0017]

[0018] in The derivative of the three-degree-of-freedom velocity state vector is represented. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel, i.e., the bow angular velocity in the body coordinate system. Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the lateral speed of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the yaw rate of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the PWM control input signal for the port thruster. This indicates the PWM control input signal for the starboard thruster. The 15 sets of parameters to be identified represent the hydrodynamic characteristics and overall response relationship of the propulsion system of the dual-propeller unmanned surface vessel model.

[0019] Furthermore, the dual-propeller unmanned vessel model identification device includes an experimental scene, a perception module, and a remote computing processing platform.

[0020] Furthermore, in open experimental water environments such as indoor pools, a motion capture and positioning system is deployed to form a dual-propeller unmanned surface vessel (USV) model identification device. This device autonomously controls the USV to move in a Z-shaped trajectory while simultaneously collecting the USV's status data and control inputs.

[0021] Furthermore, the candidate function library for the dual-propeller unmanned surface vessel model is represented as follows:

[0022]

[0023]

[0024] in The derivative of the three-degree-of-freedom velocity state vector is represented. This represents the constant coefficient matrix consisting of the parameters to be identified in the dual-propeller unmanned surface vessel model. The 15 sets of parameters to be identified represent the dual-propeller unmanned surface vessel model. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel. Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and lateral speed The cross product term, Indicates the lateral speed of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, This indicates the PWM control input signal for the port side thruster of the dual-propeller unmanned surface vessel. This indicates the PWM control input signal for the starboard propeller of the dual-propeller unmanned surface vessel.

[0025] Furthermore, the parameters to be identified for the dual-propeller unmanned surface vessel model are solved using the sequential threshold least squares method, including:

[0026] Using least squares sparse regression with L1 norm:

[0027]

[0028] in The first constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is represented by the matrix of constant coefficients. Row coefficient, Denotes the L1 norm used for sparsity constraints. The square of the L2 norm is the square of the Euclidean distance. The constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is shown in the figure. The estimated value of the row coefficient, Indicates the first Time series data of the actual measured rate of change of velocity state of each element. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This represents the adjustment parameter that promotes sparsity.

[0029] Furthermore, the constant coefficient matrix The middle part is 0, which is a sparse matrix. Therefore, parameter identification can be performed using least squares sparse regression with L1 norm.

[0030]

[0031] for The The row coefficient determines No. The dynamic expression of an element; for for The Estimated values ​​of row coefficients; No. Time series data of the actual measured rate of change of each element; This refers to the time series data of the actual measured changes; To improve sparsity, adjustment parameters are used. For sparse nonlinear dynamics identification, L1 regularization is applied using the sequential threshold least squares algorithm to obtain the model parameters.

[0032] Furthermore, the parameters to be identified for the dual-propeller unmanned surface vessel model were substituted into the nonlinear dynamic equations for verification. Z-shaped and gyroscopic experiments were used to analyze and process the recorded data and compare the performance of the unmanned surface vessel under different test scenarios.

[0033] Furthermore, the following method is used to quantitatively evaluate the accuracy of the dual-propeller unmanned surface vessel model using root mean square error and correlation coefficient:

[0034] Calculate the root mean square distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. :

[0035]

[0036] in, Indicates a dual-propeller unmanned surface vessel Real-time motion state data Indicates a dual-propeller unmanned surface vessel The state data predicted by the dual-propeller unmanned surface vessel model at any given time. This represents the total number of control operations performed by the model predictive control algorithm during the experiment.

[0037] Calculate the correlation coefficients used to quantitatively evaluate the consistency of dynamic characteristics of the dual-propeller unmanned surface vessel dynamics model. :

[0038]

[0039] in Indicates a dual-propeller unmanned surface vessel Estimated data of motion state at any given time;

[0040] when > or < At that time, a parameter optimization loop is automatically triggered to readjust the candidate function library and the adjustment parameters that promote sparsity until the desired result is achieved. and ,in The root mean square of the Euclidean distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. The standard threshold The correlation coefficient represents the correlation coefficient used to quantitatively evaluate the consistency of the dynamic characteristics of the dynamic model of the dual-propeller unmanned surface vessel. The standard threshold.

[0041] Furthermore, the model identification and evaluation process of the dual-propeller unmanned surface vessel (USV) of this invention adopts a closed-loop iterative optimization design, mainly including four key stages: First, a test scenario is built, including an open experimental water area, a dual-propeller USV, a ground station, and a sensing module; then, a Z-shaped autonomous control experiment is performed, recording the USV's state and control input data in real time until the predetermined trajectory is completed; next, the collected data is preprocessed with Savitzky-Golay filtering, and 15 sets of key model parameters are extracted using a sparse nonlinear dynamics identification method; finally, the accuracy of the parameters is verified through a Z-shaped gyroscope experiment, forming a closed-loop optimization mechanism of "modeling-data acquisition-parameter identification-verification and evaluation". The innovation of this process is reflected in: using a Z-shaped trajectory as an excitation signal to effectively stimulate the three-degree-of-freedom coupling characteristics of the USV's sway, pitch, and yaw; achieving parameter sparsity selection through sequential threshold least squares method, reducing complexity while ensuring model accuracy; and establishing an iterative verification mechanism based on experimental data, automatically triggering a new round of data acquisition and parameter optimization when the gyroscope experiment results do not meet the requirements.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] This invention innovatively introduces a sparse nonlinear dynamics identification method into the field of unmanned vessel dynamics identification. By using L1 regularization constraints, it achieves automatic simplification of the model structure. Compared with the traditional particle swarm optimization algorithm, which only identifies 5 sets of parameters of the unmanned vessel response model, this method can identify 15 sets of parameters of the unmanned vessel dynamics model.

[0044] In terms of engineering implementation, this invention uses a Savitzky-Golay filter for data preprocessing, which effectively solves the noise amplification problem caused by numerical differentiation. This method can reduce the estimation error of the state derivative and lay a data foundation for high-precision parameter identification.

[0045] To address the unique nonlinear coupling characteristics of dual-propeller unmanned vessels, this invention designs a second-order candidate function library that includes key physical terms such as Coriolis force and damping force. This design not only preserves physical interpretability but also avoids the overfitting problem common in traditional methods by introducing sparse constraints, and can still maintain stable identification under unknown noise interference.

[0046] The "modeling-data acquisition-parameter identification-verification and evaluation" system framework proposed in this invention innovatively combines model identification with control system development. Through a closed-loop verification mechanism, it achieves full-process optimization from parameter identification to control performance evaluation and verification, shortening the development cycle compared to existing technologies.

[0047] Based on the aforementioned innovations, this invention is particularly suitable for the rapid dynamic modeling needs of small scientific research unmanned vessels, demonstrating significant advantages in applications such as water quality monitoring, waterway patrol, and waste cleanup. By providing a high-precision, low-cost model identification solution, this invention powerfully promotes the transition of unmanned vessel control technology from the laboratory to practical applications. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition, as described in this invention.

[0050] Figure 2 The coordinate system modeling of the dual-propeller unmanned vessel is used for the model identification and evaluation method of the dual-propeller unmanned vessel for sparse nonlinear dynamics identification in this invention.

[0051] Figure 3 This invention relates to a dual-propeller unmanned vessel model identification device, which is part of a method for identifying and evaluating dual-propeller unmanned vessel models based on sparse nonlinear dynamics recognition.

[0052] Figure 4 This is a schematic diagram illustrating the identification of a dual-propeller unmanned surface vessel (USV) model using a sparse nonlinear dynamics identification method according to the present invention.

[0053] Figure 5 This invention provides a method for identifying and evaluating dual-propeller unmanned surface vessel (USV) models based on sparse nonlinear dynamics recognition. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] This invention provides the following technical solutions:

[0057] like Figure 1 The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics, as shown, specifically includes the following steps:

[0058] S1: Based on the relative positional relationship between the inertial coordinate system and the hull coordinate system, mark the motion characteristics and hull characteristics of the dual-propeller unmanned vessel, establish a three-degree-of-freedom control model, and combine the three-degree-of-freedom control model with the propulsion characteristics of the dual-propeller unmanned vessel to establish a dual-propeller unmanned vessel model.

[0059] In one embodiment, such as Figure 2As shown, the process of establishing the dual-propeller unmanned surface vessel model includes:

[0060] Using the northeast coordinate system of a standard ship as the inertial coordinate system Constructing the hull coordinate system of the dual-propeller unmanned surface vessel Relative positional relationships, marking the motion characteristics and hull features of the dual-propeller unmanned surface vessel;

[0061] The dual-propeller unmanned surface vessel (USV) model established in this invention is based on the following key assumptions: First, it is assumed that the hull mass distribution is uniform, ensuring that the center of gravity of the USV coincides perfectly. This simplification effectively eliminates the additional torque term caused by uneven mass distribution, making the dynamic equations more concise. Second, the influence of environmental disturbances such as wind, waves, and currents on the hull motion is temporarily disregarded during model construction; that is, it is assumed that the experiment is conducted in an ideal still water environment. Based on these reasonable assumptions, the mathematical model of the maneuvering motion of the dual-propeller USV can be simplified into a three-degree-of-freedom (sway, roll, and bow) rigid body dynamic system, thus establishing a three-degree-of-freedom maneuvering model for the dual-propeller USV.

[0062]

[0063] in Represents the velocity vector. , , These represent the longitudinal velocity, lateral velocity, and bow angular velocity in the body coordinate system, respectively. Represents a position vector. Represents the heading angle in the inertial coordinate system. The rotation moment represents the rotational transformation matrix that transforms the velocity vector from the system frame to the inertial frame. Represents the total mass matrix. Represents the Coriolis centripetal force matrix. Represents the damping matrix. express , directional thrust and Directional torque.

[0064] The propeller's power output is controlled by adjusting the pulse width of the pulse width modulation (PWM) signal. The PWM signal vector controlling the left or right thrust is defined as follows: By combining the three-degree-of-freedom maneuvering model with the characteristics of a dual-propeller unmanned surface vessel (USV), the following dual-propeller USV model is established:

[0065]

[0066] in The derivative of the three-degree-of-freedom velocity state vector is represented. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel, i.e., the bow angular velocity in the body coordinate system. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the lateral speed of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the yaw rate of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the PWM control input signal for the port thruster. This indicates the PWM control input signal for the starboard thruster. The 15 sets of parameters to be identified represent the hydrodynamic characteristics and overall response relationship of the propulsion system of the dual-propeller unmanned surface vessel model.

[0067] S2: Build a dual-propeller unmanned vessel model identification device. By autonomously controlling the unmanned vessel to move in a Z-shaped trajectory, the device simultaneously collects the control input commands and status data of the unmanned vessel, and uses these as the basis for solving the identification parameters of the dual-propeller unmanned vessel model.

[0068] In one embodiment, in an open experimental aquatic environment such as an indoor pool, a motion capture and positioning system is deployed to form a dual-propeller unmanned surface vessel (USV) model identification device. The USV autonomously controls the USV to move in a Z-shaped trajectory and simultaneously collects the USV's status data and control inputs.

[0069] In one embodiment, such as Figure 3As shown, the constructed dual-propeller unmanned surface vessel (USV) model identification device comprises an experimental scenario, a perception module, and a remote computing platform. The experimental scenario can be an indoor experimental pool, an outdoor lake, river, or nearshore open water area. For indoor scenarios, 10 high-precision motion capture cameras (1-10) are evenly distributed around the perimeter to form a 360-degree coverage positioning network. For outdoor scenarios, GPS positioning is used. The USV and ground station interact via a router or P900 module to exchange hull status and control information, forming the perception module of the identification device. A remote computing platform for the USV is located on the side of the experimental scenario; this platform is the ground station, and its computer contains the identification software. The system's data processing hub consists of a main control computer, a router, and a data recording module, interacting with the USV in real-time via a wireless communication interface. The identification device includes a controller and a communication interface, programmed to implement autonomous yaw and Z-shaped model predictive control programs. The communication interface includes common data exchange protocols such as serial port / CAN / SBUS / TCP / UDP, providing identification and verification methods for model recognition. The ingenious feature of this unmanned surface vessel (USV) model recognition device is that the motion capture cameras are arranged in a circular array, ensuring that the USV is always within the common field of view of at least three cameras when performing Z-shaped trajectory and turning experiments, effectively solving the occlusion problem of traditional monocular vision systems.

[0070] S3: Based on the sparse nonlinear dynamics identification framework, a candidate function library for dual-propeller unmanned surface vessel models containing second-order polynomial terms is established, and the parameters to be identified for the dual-propeller unmanned surface vessel model are solved by the sequential threshold least squares method.

[0071] In one embodiment, the candidate function library for the dual-propeller unmanned surface vessel model is represented as follows:

[0072]

[0073]

[0074] in The derivative of the three-degree-of-freedom velocity state vector is represented. This represents the constant coefficient matrix consisting of the parameters to be identified in the dual-propeller unmanned surface vessel model. The 15 sets of parameters to be identified represent the dual-propeller unmanned surface vessel model. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel. Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and lateral speed The cross product term, Indicates the lateral speed of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, This indicates the PWM control input signal for the port side thruster of the dual-propeller unmanned surface vessel. This indicates the PWM control input signal for the starboard propeller of the dual-propeller unmanned surface vessel.

[0075] In one embodiment, the parameters to be identified for the dual-propeller unmanned surface vessel model are solved using the sequential threshold least squares method, including:

[0076] Using least squares sparse regression with L1 norm:

[0077]

[0078] in The first constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is represented by the matrix of constant coefficients. Row coefficient, Denotes the L1 norm used for sparsity constraints. The square of the L2 norm is the square of the Euclidean distance. The constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is shown in the figure. The estimated value of the row coefficient, Indicates the first Time series data of the actual measured rate of change of velocity state of each element. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This represents the adjustment parameter that promotes sparsity.

[0079] In one embodiment, the constant coefficient matrix The middle part is 0, which is a sparse matrix. Therefore, parameter identification can be performed using least squares sparse regression with L1 norm.

[0080]

[0081] for The The row coefficient determines No. The dynamic expression of an element; for for The Estimated values ​​of row coefficients; No. Time series data of the actual measured rate of change of each element; This refers to the time series data of the actual measured changes; To improve sparsity, adjustment parameters are used. For sparse nonlinear dynamics identification, L1 regularization is applied using the sequential threshold least squares algorithm to obtain the model parameters.

[0082] In one embodiment, the parameters of the dual-propeller unmanned surface vessel model are shown in the table below:

[0083] Table 1. Parameters to be identified for the dual-propeller unmanned surface vessel model

[0084]

[0085] S4: Substitute the parameters to be identified from the dual-propeller unmanned vessel model into the dual-propeller unmanned vessel model for Z-shaped and gyroscopic experiments for verification, and evaluate the accuracy of the dual-propeller unmanned vessel model by quantitatively using root mean square error and correlation coefficient.

[0086] In one embodiment, the following method is used to quantitatively evaluate the accuracy of the dual-propeller unmanned surface vessel model using root mean square error and correlation coefficient:

[0087] Calculate the root mean square distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. :

[0088]

[0089] in, Indicates a dual-propeller unmanned surface vessel Real-time motion state data Indicates a dual-propeller unmanned surface vessel The state data predicted by the dual-propeller unmanned surface vessel model at any given time. This represents the total number of control operations performed by the model predictive control algorithm during the experiment.

[0090] Calculate the correlation coefficients used to quantitatively evaluate the consistency of dynamic characteristics of the dual-propeller unmanned surface vessel dynamics model. :

[0091]

[0092] in Indicates a dual-propeller unmanned surface vessel Estimated data of motion state at any given time;

[0093] when > or < At that time, a parameter optimization loop is automatically triggered to readjust the candidate function library and the adjustment parameters that promote sparsity until the desired result is achieved. and ,in The root mean square of the Euclidean distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. The standard threshold The correlation coefficient represents the correlation coefficient used to quantitatively evaluate the consistency of the dynamic characteristics of the dynamic model of the dual-propeller unmanned surface vessel. The standard threshold.

[0094] This invention proposes a model identification method for dual-propeller unmanned vessels based on sparse nonlinear dynamics identification, which can significantly improve the accuracy and efficiency of dynamics modeling and provide reliable technical support for the development of autonomous control systems for dual-propeller driven unmanned vessels.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions 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 method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics, characterized in that, Includes the following steps: Based on the relative positional relationship between the inertial coordinate system and the hull coordinate system, the motion characteristics and hull characteristics of the dual-propeller unmanned vessel are labeled, a three-degree-of-freedom control model is established, and the three-degree-of-freedom control model is combined with the propulsion characteristics of the dual-propeller unmanned vessel to establish a dual-propeller unmanned vessel model. A dual-propeller unmanned surface vessel (USV) model identification device was built. The USV was autonomously controlled to move in a Z-shaped trajectory. The control input commands and status data of the USV were collected simultaneously and used as the basis for solving the identification parameters of the dual-propeller USV model. A candidate function library for dual-propeller unmanned surface vessel (USV) models containing second-order polynomial terms is established based on a sparse nonlinear dynamics identification framework. The parameters to be identified for the dual-propeller USV model are solved by the sequential threshold least squares method. The parameters to be identified in the dual-propeller unmanned vessel model were substituted into the model for Z-shaped and gyroscopic experiments. The accuracy of the dual-propeller unmanned vessel model was quantitatively evaluated by the root mean square error and correlation coefficient. The process of establishing the dual-propeller unmanned surface vessel model includes: Using the northeast coordinate system of a standard ship as the inertial coordinate system Constructing the hull coordinate system of the dual-propeller unmanned surface vessel Relative positional relationships, marking the motion characteristics and hull features of the dual-propeller unmanned surface vessel; The center of gravity and the center of gravity of the unmanned ship coincide. The three-degree-of-freedom maneuvering model of the dual-propeller unmanned ship is established by ignoring wind, wave and current interference. The propeller's power output is controlled by adjusting the pulse width of the pulse width modulation (PWM) signal. The PWM signal vector controlling the left or right thrust is defined as follows: By combining the three-degree-of-freedom maneuvering model with the characteristics of a dual-propeller unmanned surface vessel (USV), the following dual-propeller USV model is established: in The derivative of the three-degree-of-freedom velocity state vector is represented. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the lateral speed of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the yaw rate of the dual-propeller unmanned surface vessel. Rate of change in direction This indicates the PWM control input signal for the port side thruster. This indicates the PWM control input signal for the starboard thruster. The 15 sets of parameters to be identified represent the hydrodynamic characteristics and overall response relationship of the propulsion system of the dual-propeller unmanned surface vessel model.

2. The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition according to claim 1, characterized in that, The dual-propeller unmanned vessel model identification device includes an experimental scene, a perception module, and a remote computing processing platform.

3. The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition according to claim 1, characterized in that, The candidate function library for the dual-propeller unmanned surface vessel model is represented as follows: in The derivative of the three-degree-of-freedom velocity state vector is represented. This represents the constant coefficient matrix consisting of the parameters to be identified in the dual-propeller unmanned surface vessel model. The 15 sets of parameters to be identified represent the dual-propeller unmanned surface vessel model. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. This indicates the lateral velocity of the dual-propeller unmanned surface vessel. This represents the yaw rate of the dual-propeller unmanned surface vessel. Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and lateral speed The cross product term, Indicates the lateral speed of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, Indicates the longitudinal velocity of the dual-propeller unmanned surface vessel. and yaw rate The cross product term, This indicates the PWM control input signal for the port side thruster of the dual-propeller unmanned surface vessel. This indicates the PWM control input signal for the starboard propeller of the dual-propeller unmanned surface vessel.

4. The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition according to claim 1, characterized in that, The parameters to be identified for the dual-propeller unmanned surface vessel model are obtained using the sequential threshold least squares method, including: Using least squares sparse regression with L1 norm: in The first constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is represented by the matrix of constant coefficients. Row coefficient, Denotes the L1 norm used for sparsity constraints. The square of the L2 norm is the square of the Euclidean distance. The constant coefficient matrix representing the parameters to be identified in the dual-propeller unmanned surface vessel model is shown in the figure. The estimated value of the row coefficient, Indicates the first Time series data of the actual measured rate of change of velocity state of each element. This represents a candidate function library for dual-propeller unmanned surface vessel models. Indicates the three-degree-of-freedom velocity state. This represents the PWM signal vector that controls whether the signal is pushed left or right. This represents the adjustment parameter that promotes sparsity.

5. The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition according to claim 1, characterized in that, The parameters to be identified in the dual-propeller unmanned surface vessel model were substituted into the nonlinear dynamic equation for verification. Z-shaped and gyroscopic experiments were used to analyze and process the recorded data and compare the performance of the unmanned surface vessel under different test scenarios.

6. The method for identifying and evaluating a dual-propeller unmanned surface vessel model based on sparse nonlinear dynamics recognition according to claim 1, characterized in that, The following method is used to quantitatively evaluate the accuracy of the dual-propeller unmanned surface vessel model using root mean square error and correlation coefficient: Calculate the root mean square distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. : in, Indicates a dual-propeller unmanned surface vessel Real-time motion state data Indicates a dual-propeller unmanned surface vessel The state data predicted by the dual-propeller unmanned surface vessel model at any given time. This represents the total number of control operations performed by the model predictive control algorithm during the experiment. Calculate the correlation coefficients used to quantitatively evaluate the consistency of dynamic characteristics of the dual-propeller unmanned surface vessel dynamics model. : in Indicates a dual-propeller unmanned surface vessel Estimated data of motion state at any given time; when or At that time, a parameter optimization loop is automatically triggered to readjust the candidate function library and the adjustment parameters that promote sparsity until the desired result is achieved. and ,in The root mean square of the Euclidean distance between the actual position and the reference position of the dual-propeller unmanned surface vessel model at each moment. The standard threshold The correlation coefficient represents the correlation coefficient used to quantitatively evaluate the consistency of the dynamic characteristics of the dynamic model of the dual-propeller unmanned surface vessel. The standard threshold.

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