High-frequency self-adaptive dynamic positioning ship state estimation method

Abstract

The invention relates to the field of ship motion control, and discloses a high-frequency self-adaptive dynamic positioning ship state estimation method, which comprises the following steps: constructing a system model and a measurement model for ship horizontal plane three-degree-of-freedom motion state estimation; performing dimension expansion on the model, and taking the dominant frequency and the relative damping coefficient of the high-frequency motion as state variables and measurement information; acquiring ship position, heading, wind speed and direction and propeller rotating speed information by using a position reference system and a sensor; performing spectral analysis through fast Fourier transform, and extracting a high-frequency motion dominant frequency; and performing state estimation by adopting extended Kalman filtering or unscented Kalman filtering, separating and eliminating high-frequency motion components, and outputting a low-frequency motion state for dynamic positioning control. By dynamically estimating high-frequency parameters and combining time domain and frequency domain analysis, the method improves state estimation precision and convergence, reduces propeller abrasion and energy consumption, adapts to a complex marine environment and improves dynamic positioning control performance.

Description

Technical Field

[0001] This invention relates to the field of ship motion control technology, and in particular to a high-frequency adaptive dynamic positioning method for ship state estimation. Background Technology

[0002] The main function of a surface vessel dynamic positioning system is to achieve automatic control of the three degrees of freedom: position, heading, and trajectory, enabling the vessel to maintain its set position, heading, and trajectory. Due to the complexity of the marine environment, vessels are simultaneously affected by wind, waves, and currents. Wind speed and direction are relatively easy to measure, and wind loads are easily calculated; wind-feedback control strategies are typically used to compensate for the wind's impact on the vessel's position. The force exerted by ocean currents on the vessel is usually assumed to be a slowly varying environmental force; the force of waves makes the vessel's forces and motion extremely complex, involving both first-order and second-order wave forces. The second-order wave force causes the vessel to drift slowly, while the first-order wave force causes it to reciprocate at high frequencies. Therefore, the motion of a vessel at sea is a superposition of high-frequency and low-frequency motions, and the position information measured by the position measurement system is also a mixed high- and low-frequency signal. From the perspective of reducing propeller wear and energy consumption, it is unnecessary for the dynamic positioning system to control the high-frequency position signal. Furthermore, the data measured by the position and heading sensors contains noise signals, which will affect the stability of the entire system. The role of state estimation is to separate high-frequency and low-frequency motion information, filter out high-frequency signals and environmental noise signals from position and heading information, and transmit only low-frequency signals to the controller so that the controller can automatically control the ship's low-frequency position and heading.

[0003] Kalman filtering is a recursive algorithm that estimates a ship's motion state from a mathematical model and noisy sensor data, separating low-frequency and high-frequency signals. Classical Kalman filtering is an optimal state estimation filter for linear systems with known process and measurement noise. However, in reality, process and measurement noise are time-varying under the influence of the marine environment, and dynamic positioning systems are complex nonlinear systems. Therefore, extended Kalman filtering is often used in engineering applications.

[0004] Due to the variability and uncertainty of the working environment of dynamically positioned vessels, parameters such as wave frequency and noise in the motion model are uncertain. This parameter uncertainty can significantly impact filtering accuracy and even cause filter divergence, thereby affecting the performance of the entire dynamic positioning control system. To address the drawbacks of using fixed high-frequency motion model parameters in the filtering algorithm, these parameters need to be introduced as variables to be estimated and measurement information into the state estimation model for real-time updates. This allows for better estimation and elimination of high-frequency motion information, improving the convergence and accuracy of state estimation, and ultimately enhancing the accuracy of the dynamic positioning control system. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a high-frequency adaptive dynamic positioning method for ship state estimation.

[0006] A high-frequency adaptive dynamic positioning method for ship state estimation. S1. Construct a system model and a measurement model for estimating the three-degree-of-freedom motion state of a dynamically positioned ship in the horizontal plane. The system model includes a low-frequency motion model, a high-frequency motion model, a thruster model, and a wind load model. The measurement model is used to describe the measurement information of the ship's position and heading. S2, the system model and measurement model are expanded in dimension by adding the dominant longitudinal, lateral and bow directions and relative damping coefficients in the high-frequency motion model as state variables to the system model, and adding the measured values ​​of the dominant frequencies as measurement information to the measurement model, thus obtaining the expanded system model and measurement model. S3, collect the ship's real-time position, heading, relative wind speed and direction, and propeller speed information, and generate measurement data. The acquisition of the measurement data is achieved through a position reference system and a sensor system. S4. Within a fixed time interval, based on the measurement data collected in step S3, extract the position and heading data within the time window, perform spectral analysis of the longitudinal, lateral, and heading three-degree-of-freedom motion, and extract the dominant frequency of the high-frequency motion as the dominant frequency measurement value. S5, based on the expanded system model and measurement model from step S2, uses the Kalman filter algorithm, combined with the measurement data from step S3 and the dominant frequency measurement values ​​from step S4, to perform state estimation, separate and remove high-frequency motion components, and obtain low-frequency motion state estimation information.

[0007] Furthermore, the system model in step S1 includes: Low-frequency motion model, describing the low-frequency motion state of a ship in the longitudinal, lateral, and bow directions; A high-frequency motion model describes the high-frequency reciprocating motion caused by first-order wave force. Thruster thrust model, describing the longitudinal, lateral, and bow thrust generated by the thruster; Wind load model, describing wind loads calculated based on relative wind speed and wind direction; The measurement model includes ship position and heading information measured based on a position reference system and a sensor system.

[0008] Furthermore, in step S2, the expanded system model extends the state vector to include low-frequency motion state, high-frequency motion state, environmental interference load, and the dominant frequency and relative damping coefficient of high-frequency motion. The measurement model includes measurement information of position, heading, and dominant frequency.

[0009] Furthermore, the position reference system in step S3 includes at least one of a satellite navigation system, an underwater acoustic positioning system, a laser positioning system, or a microwave positioning system; the sensor system includes at least one of a compass, a vertical plane reference system, or an anemometer.

[0010] Furthermore, the measurement data collected in step S3 includes the ship's longitudinal position, lateral position, heading angle, relative wind speed, relative wind direction, and the rotational speed of each propeller.

[0011] Furthermore, the spectral analysis in step S4 employs the Fast Fourier Transform method, specifically including: Spectral analysis is performed on the position and heading data collected in step S3 to obtain the power spectrum distributions in the longitudinal, lateral, and heading directions; The power spectrum distribution is sorted, and the frequency corresponding to the maximum power spectrum value is extracted as the dominant frequency measurement value in the longitudinal, lateral, and bow directions.

[0012] Furthermore, the length of the time window in step S4 is set according to the ship's motion characteristics and marine environmental conditions, and is dynamically updated within a fixed time interval.

[0013] Furthermore, the Kalman filtering algorithm in step S5 is either extended Kalman filtering or unscented Kalman filtering, and the state estimation includes estimation of low-frequency motion state, environmental interference load, high-frequency motion dominant frequency, and relative damping coefficient.

[0014] Furthermore, the high-frequency motion components separated in step S5 include high-frequency reciprocating motion caused by first-order wave forces, and the low-frequency motion state estimation information includes the ship's longitudinal position, lateral position, heading angle, and corresponding speed information.

[0015] Furthermore, it also includes, S6, output the low-frequency motion state estimation information obtained in step S5 to the dynamic positioning control system for the ship's position and heading control; The low-frequency motion state estimation information output in step S6 is used to generate propeller control commands to achieve position maintenance and heading stability of the ship in complex marine environments.

[0016] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: 1. This invention proposes a high-frequency adaptive dynamic positioning ship state estimation method, which can separate the high-frequency and low-frequency motion information of the ship, thereby filtering out high-frequency signals and environmental noise signals from the position and heading information, and transmitting only low-frequency signals to the controller, so that the controller can automatically control the ship's low-frequency position and heading.

[0017] 2. Based on fully considering the advantages of filtering algorithms, this invention expands the dimension of the time-domain state estimation model, introduces high-frequency model parameters as the state variables to be estimated, and uses the Fast Fourier Transform (FFT) method in the frequency domain to perform spectral analysis on the mixed high- and low-frequency motion information. The extracted dominant frequency is used as part of the measurement information in the time-domain state estimation measurement model. The combination of time-domain and frequency-domain methods greatly improves the global adaptability of the algorithm, suppresses filter divergence, and adapts to changes in the dominant frequency caused by complex marine environmental changes, thus enabling high-frequency motion information components to be extracted and eliminated as much as possible. Furthermore, this invention can accurately estimate the ship's motion state and environmental forces, and has high practical value.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying 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 this invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 This is a flowchart of the high-frequency adaptive dynamic positioning ship state estimation method provided in the embodiments of the present invention.

[0021] Figure 2 This is a comparison chart of longitudinal position and velocity estimation provided in an embodiment of the present invention.

[0022] Figure 3 This is a comparison chart of lateral position and velocity estimation provided in an embodiment of the present invention.

[0023] Figure 4 This is a comparison diagram of the estimated heading angle and angular velocity provided in the embodiments of the present invention.

[0024] Figure 5 This is a comparison chart of environmental disturbance load estimation provided in the embodiments of the present invention.

[0025] Figure 6 This is a comparison chart of the dominant frequency output results provided in the embodiments of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but should not be used to limit the scope of this invention.

[0027] This invention addresses the variability and uncertainty of the working environment of dynamically positioned vessels, and the problems of low filtering accuracy or even filtering divergence caused by the uncertainty of parameters such as wave frequency and noise in the motion model. It proposes a high-frequency adaptive state estimation method for dynamically positioned vessels. In the time domain, the state estimation model is expanded by introducing high-frequency model parameters as state variables to be estimated, and the expanded state estimation is performed based on extended Kalman filtering or unscented Kalman filtering algorithms. In the frequency domain, the fast Fourier transform method is used to perform spectral analysis on the mixed high- and low-frequency motion information, and the extracted dominant frequency is used as part of the measurement information in the time-domain state estimation measurement model. The combination of time-domain and frequency-domain methods improves the convergence and accuracy of state estimation.

[0028] like Figure 1 As shown, a flowchart of a high-frequency adaptive dynamic positioning ship state estimation method is presented.

[0029] High-frequency adaptive dynamic positioning ship state estimation methods include: S1: Construct a system model and a measurement model for estimating the three-degree-of-freedom motion state of a dynamically positioned vessel in the horizontal plane. The system model includes a mathematical model of the three-degree-of-freedom motion of the dynamically positioned vessel in the horizontal plane at low frequency, high frequency, and thruster, as well as a model of wind load. Includes the following steps: B1: The mathematical model of the three-degree-of-freedom low-frequency motion of a dynamically positioned ship in the horizontal plane under marine environment is established as Equation (1): (1) in, The longitudinal position of the ship in the hull coordinate system. Horizontal position and bow State vector, i.e. ; U is the ship's velocity and angular velocity state vector in the ship's coordinate system. , For longitudinal velocity, For lateral velocity, The forward angular velocity; b represents the environmental disturbance load in the three degrees of freedom: north, east, and bow. It is a three-dimensional diagonal matrix containing a time constant. This is a three-dimensional diagonal matrix representing the amplitude of environmental disturbance loads. It is a vector of zero-mean Gaussian white noise; This is the coordinate transformation matrix. T represents matrix transpose; M is the ship's inertia matrix. , For ship quality, For the ship's moment of inertia, For the longitudinal coordinate of the ship's center of mass, For the longitudinal hydrodynamic acceleration derivative, For the derivative of the lateral hydrodynamic acceleration, For the bow-to-lateral coupled hydrodynamic acceleration derivative, For the derivative of the coupled hydrodynamic acceleration laterally with respect to the bow direction, The derivative of the bow-head hydrodynamic acceleration; For the ship's damping matrix, , For the longitudinal hydrodynamic velocity derivative, For the derivative of the lateral hydrodynamic velocity, For the coupled hydrodynamic velocity derivative from bow to lateral, For the lateral coupled hydrodynamic velocity derivative with respect to the bow direction, The derivative of the bow hydrodynamic velocity; For thrust vector, , , , These represent the three degrees of freedom thrust (moment) generated by the propeller in the longitudinal, transverse, and bow directions of the hull. For wind load vector, , , , These are the wind loads in the longitudinal, transverse, and bow directions of the hull, respectively. This is a three-dimensional diagonal matrix representing the amplitude of process noise. It is a zero-mean Gaussian white noise vector.

[0030] B2: Establish the mathematical model of high-frequency motion of the ship as Equation (2): (2) in: ( =1,2,3) represents wave intensity; ( =1,2,3) represents the relative damping coefficient; ( =1,2,3) represents the dominant wave frequency. The transfer function represents the model. Represents a dummy variable. =1,2,3 represent the longitudinal, lateral, and yaw degrees of freedom, respectively; Equation (2) can be expressed in state-space form as equation (3): (3) In the formula, This represents the ship's high-frequency state vector. This indicates high-frequency oscillation. Indicates the position of high-frequency oscillation. Indicates the high-frequency heading angle. express The points, express The points, express The points, It is a vector of zero-mean Gaussian white noise; These are three-dimensional vectors, representing the high-frequency motion's sway position, lateral sway position, and heading angle, respectively. , , These are the coefficient matrices; ; ; ; in, ; ; ; I is a 3×3 identity matrix.

[0031] B3: Establish a thrust model for dynamically positioned ship propellers.

[0032] The thrust and torque generated by all propulsion units can be vectorized. (Longitudinal force, lateral force, and bow moment) are expressed as equation (4): (4) c is the control input. (i is the number of thrusters) is the input control variable. , The rotational speed of each thruster; The thrust coefficient matrix K is a diagonal matrix consisting of i thrust coefficients of the thrusters. The thrust coefficient is generally obtained by calculating the thrust curve or by identifying it on a real ship. B is the control matrix describing the thruster configuration, which is related to the arrangement position and type of the i thrusters.

[0033] B4: Establish the wind load model as equation (5): (5) in: , , These are the longitudinal, transverse, and bow wind loads of the hull, respectively. , and These are the dimensionless wind load coefficients for the longitudinal, transverse, and bow directions of the hull, respectively, which are generally obtained through wind tunnel tests or CFD simulation analysis; Relative wind direction angle; Relative wind speed; air density; and These are the frontal and lateral projected areas of the hull, respectively. The total length of the hull.

[0034] B5: Establish the state estimation measurement model (6): (6) In the formula It is a three-dimensional vector of zero-mean Gaussian white noise.

[0035] B6: Combining the above models, we obtain the nonlinear mathematical model (7) for dynamic positioning ship state estimation: (7) Equation (7) can be expressed in state-space form to obtain the dynamic positioning ship state estimation model (8): (8) In the formula: Indicates system measurement, State vector It is a 15-dimensional state vector; Nonlinear state transition function ; E is the noise figure matrix. ; It is a Gaussian white noise vector; For the observation matrix, .

[0036] S2: Expand the dimensions of the system model and the measurement model. Add the dominant longitudinal, lateral, and bow directions and the relative damping coefficients from the high-frequency motion mathematical model in the system model as part of the state estimation vector into the system model. Add the measurements of the dominant longitudinal, lateral, and bow directions as part of the measurement model into the measurement model. This will result in the expanded system model and measurement model for the three-degree-of-freedom motion state estimation of the dynamic positioning ship in the horizontal plane. Includes the following steps: C1: Establish the mathematical model formula (9) for the longitudinal, lateral, and bow dominant frequencies and relative damping coefficients in the high-frequency motion mathematical model: (9) in, As the dominant frequency vector, It is a three-dimensional diagonal matrix containing a time constant. It is a three-dimensional diagonal matrix representing the amplitude of the dominant frequency. It is a zero-mean Gaussian white noise vector.

[0037] This is the vector of relative damping coefficients. This is a three-dimensional diagonal matrix, representing the magnitude of the relative damping coefficient. It is a zero-mean Gaussian white noise vector.

[0038] C2: Establish the measurement model formula (10) for the longitudinal, lateral, and bow dominant frequencies in the high-frequency motion mathematical model: (10) In the formula, Represents the dominant frequency measurement vector; It is a three-dimensional vector of zero-mean Gaussian white noise.

[0039] C3: Add the system model and measurement model established in C1 and C2 to the model equation (8) in step S1, thereby expanding the dimension of the state estimation model to obtain the expanded state estimation model equation (11): (11) In the formula: This represents the expanded Gaussian white noise vector; This indicates the measurement noise after dimensional expansion; This represents the system measurement after dimensional expansion. Expanded state vector It is a 21-dimensional state vector; Nonlinear state transition function ; The noise coefficient matrix after dimension expansion. ; The observation matrix after dimension expansion. .

[0040] S3: Use the position reference system and sensor system to obtain information on the ship's position, heading, and relative wind speed and direction, and measure and obtain the rotational speed information of each of the ship's propellers; Position reference systems typically include, but are not limited to, satellite navigation systems such as BeiDou, GPS, and GLONASS, as well as underwater acoustic positioning systems and relative position measurement systems such as lasers and microwaves; sensor systems typically include, but are not limited to, compasses, vertical plane reference systems, and anemometers.

[0041] S4: The position and heading measurement information within the time window from the current time to a previous time is updated at fixed time intervals, and the longitudinal, lateral, and heading three-degree-of-freedom motion spectrum analysis is performed to extract the dominant frequencies of the longitudinal, lateral, and heading high-frequency motion as the dominant frequency measurement values. Includes the following steps: D1: At fixed time intervals, the Fast Fourier Transform method is used to perform spectrum analysis on the high and low frequency mixed motion measurement information of the ship's longitudinal, lateral and bow directions to obtain the longitudinal, lateral and bow power spectrum distributions corresponding to different frequencies; D2: Sort the power spectra in the longitudinal, transverse, and bow directions using a sorting algorithm, and take the frequency at the maximum power spectrum as the dominant frequency of each dimension.

[0042] S5: Use extended Kalman filtering or unscented Kalman filtering to estimate the state, separate and eliminate high-frequency motion, and then use the optimal low-frequency motion state estimation information for dynamic positioning motion control.

[0043] Includes the following steps: E1: Calculate the thrust of each propeller and the resultant forces (moments) in the longitudinal, transverse, and bow directions of the ship: Calculated using equation (4) .

[0044] E2: Calculate the wind loads on the ship in the longitudinal, transverse, and bow directions: Calculated using equation (5) .

[0045] E3: Using the extended Kalman filter or unscented Kalman filter method, the high- and low-frequency motion states of three degrees of freedom, environmental disturbance loads, high-frequency dominant motion frequencies, and relative damping coefficients are estimated and output, thus completing the state vector. The estimated output transmits the low-frequency motion state estimate and environmental disturbance load to the next level control system.

[0046] S6 outputs the low-frequency motion state estimation information obtained in step S5 to the dynamic positioning control system for the ship's position and heading control.

[0047] The low-frequency motion state estimation information output to the dynamic positioning control system is used to generate propeller control commands to achieve position holding and heading stability of the ship in complex marine environments.

[0048] Figure 2 , Figure 3 , Figure 4 The comparison results of longitudinal position, longitudinal velocity, lateral position, lateral velocity, heading, and heading angle estimations are shown. It can be seen that using this method can eliminate more high-frequency motion components, resulting in smoother estimation results, which is beneficial for the next stage of motion control and can thus better reduce propeller wear.

[0049] Figure 5 The comparison results of environmental disturbance load estimation are shown. It is clear that the environmental disturbance load estimated using this method is smoother, which is beneficial to the next stage of motion control and can better reduce thruster wear.

[0050] Figure 6 The comparison results of the dominant frequency output are shown. It can be seen that the dominant frequency estimated by this method is smoother and reduces the jitter of the dominant frequency output by the spectrum analysis method.

[0051] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-frequency adaptive dynamic positioning method for ship state estimation, characterized in that, S1. Construct a system model and a measurement model for estimating the three-degree-of-freedom motion state of a dynamically positioned ship in the horizontal plane. The system model includes a low-frequency motion model, a high-frequency motion model, a thruster model, and a wind load model. The measurement model is used to describe the measurement information of the ship's position and heading. S2, the system model and measurement model are expanded in dimension by adding the dominant longitudinal, lateral and bow directions and relative damping coefficients in the high-frequency motion model as state variables to the system model, and adding the measured values ​​of the dominant frequencies as measurement information to the measurement model, thus obtaining the expanded system model and measurement model. S3, collect the ship's real-time position, heading, relative wind speed and direction, and propeller speed information, and generate measurement data. The acquisition of the measurement data is achieved through a position reference system and a sensor system. S4. Within a fixed time interval, based on the measurement data collected in step S3, extract the position and heading data within the time window, perform spectral analysis of the longitudinal, lateral, and heading three-degree-of-freedom motion, and extract the dominant frequency of the high-frequency motion as the dominant frequency measurement value. S5, based on the expanded system model and measurement model from step S2, uses the Kalman filter algorithm, combined with the measurement data from step S3 and the dominant frequency measurement values ​​from step S4, to perform state estimation, separate and remove high-frequency motion components, and obtain low-frequency motion state estimation information.

2. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The system model in step S1 includes: Low-frequency motion model, describing the low-frequency motion state of a ship in the longitudinal, lateral, and bow directions; A high-frequency motion model describes the high-frequency reciprocating motion caused by first-order wave force. Thruster thrust model, describing the longitudinal, lateral, and bow thrust generated by the thruster; Wind load model, describing wind loads calculated based on relative wind speed and wind direction; The measurement model includes ship position and heading information measured based on a position reference system and a sensor system.

3. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, In step S2, the expanded system model extends the state vector to include low-frequency motion state, high-frequency motion state, environmental interference load, and the dominant frequency and relative damping coefficient of high-frequency motion. The measurement model includes measurement information of position, heading, and dominant frequency.

4. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The position reference system in step S3 includes at least one of a satellite navigation system, an underwater acoustic positioning system, a laser positioning system, or a microwave positioning system; the sensor system includes at least one of a compass, a vertical plane reference system, or an anemometer.

5. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 4, characterized in that, The measurement data collected in step S3 includes the ship's longitudinal position, lateral position, heading angle, relative wind speed, relative wind direction, and the rotational speed of each propeller.

6. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The spectrum analysis in step S4 employs the Fast Fourier Transform method, specifically including: Spectral analysis is performed on the position and heading data collected in step S3 to obtain the power spectrum distributions in the longitudinal, lateral, and heading directions; The power spectrum distribution is sorted, and the frequency corresponding to the maximum power spectrum value is extracted as the dominant frequency measurement value in the longitudinal, lateral, and bow directions.

7. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 6, characterized in that, The length of the time window in step S4 is set according to the ship's motion characteristics and marine environmental conditions, and is dynamically updated within a fixed time interval.

8. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, The Kalman filtering algorithm in step S5 is either extended Kalman filtering or unscented Kalman filtering. The state estimation includes estimation of low-frequency motion state, environmental interference load, high-frequency motion dominant frequency, and relative damping coefficient.

9. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 8, characterized in that, The high-frequency motion components separated in step S5 include high-frequency reciprocating motion caused by first-order wave forces, and the low-frequency motion state estimation information includes the ship's longitudinal position, lateral position, heading angle, and corresponding speed information.

10. The high-frequency adaptive dynamic positioning ship state estimation method according to claim 1, characterized in that, It also includes, S6, output the low-frequency motion state estimation information obtained in step S5 to the dynamic positioning control system for the ship's position and heading control; The low-frequency motion state estimation information output in step S6 is used to generate propeller control commands to achieve position maintenance and heading stability of the ship in complex marine environments.

Images