CN117131809A - Dynamic positioning ship parameter identification method based on multiple measurement dimension expansion parallel filtering - Google Patents
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Abstract
The invention provides a dynamic positioning ship parameter identification method based on multiple measurement dimension expansion parallel filtering, which belongs to the technical field of ship dynamic positioning and comprises the following steps: s1, establishing a dynamic positioning ship propeller model and a motion model; s2, designing a parameter identification algorithm, wherein the parameter identification algorithm comprises state estimation model dimension expansion, establishment and multi-measurement parallel filtering; s3, designing a parameter identification action and a flow, and completing estimation of all parameters to be identified according to the designed identification action and flow. The invention can realize the identification and acquisition of the hydrodynamic coefficient of the ship and the thrust coefficient of the propeller, thereby providing an accurate control model for the motion state estimation and motion control of the dynamic positioning ship; the convergence of the identification parameters can be improved through algorithm design, and the identification of the hydrodynamic coefficients of the longitudinal direction, the transverse direction and the heading of the dynamic positioning ship and all propulsion thrust systems can be completed in a limited voyage by designing a series of identification action flows, so that the accuracy of parameter identification is improved.
Description
Technical Field
The invention belongs to the technical field of ship dynamic positioning, and particularly relates to a dynamic positioning ship parameter identification method based on repeated measurement and dimension expansion parallel filtering.
Background
The dynamic positioning ship can automatically maintain the position and heading by means of a self-propulsion system, and is widely applied to the field of ocean engineering. The modern dynamic positioning system performs feedback control based on a motion model, and designs a state observer and a controller by establishing a ship low-frequency motion model caused by wind, flow and second-order wave force and a ship high-frequency motion model caused by first-order wave force. In order to obtain better control performance, more accurate model parameters are required, including hydrodynamic coefficients, propeller thrust coefficients and the like.
The method for obtaining the ship model parameters comprises a constraint ship model test method, an empirical formula method and the like, but the methods have the defects of low calculation efficiency and larger error. The system parameter identification is a branch of a control theory, and the research key point is to determine a system model and model parameters, so that various identification methods, such as a least square or Kalman filtering method, are generated at present, but the problems of slow convergence of parameters to be identified, inaccurate identification results and the like may be caused by filtering processing by using single measurement information.
Disclosure of Invention
The invention aims to solve the problem of providing a dynamic positioning ship parameter identification method based on multiple measurement and dimension expansion parallel filtering, which realizes the acquisition of a dynamic positioning ship hydrodynamic coefficient and a propeller thrust coefficient, thereby providing an accurate model for ship motion state estimation and control.
In order to solve the technical problems, the invention adopts the following technical scheme: a dynamic positioning ship parameter identification method based on multiple measurement dimension expansion parallel filtering comprises the following steps:
s1, building a dynamic positioning ship propeller model and a motion model:
the propeller comprises a bow propeller and a stern propeller, wherein the bow propeller adopts a channel propeller, and the stern propeller adopts a full-rotation propeller.
The channel propeller and the full-rotation propeller are used for dynamically positioning the position and heading maintenance of the ship.
The ship propeller model is as follows: the longitudinal thrust, the transverse thrust and the heading moment generated by the propeller can be three-dimensionally vectorExpressed as:
wherein,r represents vector elements which are real numbers; t is a propeller arrangement matrix, K is a thrust coefficient matrix,>for the entered control variable +.>，/>The number of the propellers;
wherein,the rotation speed of each propeller is set; t and->The arrangement position of the individual propellers, the propeller type, the dimension T being 3 x +.>。
K is a diagonal matrix composed of i propeller thrust coefficients:
north position of ship under geodetic coordinate systemEast position->Heading angle->The available vectors are expressed asLongitudinal speed after decomposition in hull coordinate system +.>Lateral speed->Heading angular velocity->The available vector is denoted +.>The following conversion relation exists between the speeds of the ship body coordinate system and the ground coordinate system:
wherein,is the position and heading vector under the geodetic coordinate system,>is the velocity and angular velocity vector under the ship coordinate system, < ->Is the velocity and angular velocity vector under the geodetic coordinate system,/->The elements are ∈ ->The first derivative of the corresponding element in (a).
Coordinate transformation matrixThe expression is as follows:
wherein,is a non-singular coordinate transformation matrix, and +.>，
When the dynamic positioning ship is in a low-speed sailing state, the ship motion model is as follows:
wherein M is a quality matrix,damping matrix for wave drift damping and laminar surface friction generation, +.>Is the acceleration and angular acceleration vector of the ship under the coordinate system, < ->The elements are ∈ ->First derivative of the corresponding element in +.>Representing vectors of unknown environmental force composition in three degrees of freedom, longitudinal, lateral, and fore.
Quality matrixThe method comprises the following steps:
wherein,for the quality of the ship->For moment of inertia of the vessel->For the longitudinal coordinate of the center of mass of the ship>For longitudinal hydrodynamic acceleration derivative,/->Is the lateral hydrodynamic acceleration derivative,/->For coupling bow to transverseThe derivative of the acceleration of the combined hydrodynamic force,for the lateral-to-heading coupled hydrodynamic acceleration derivative,/->Is the derivative of hydrodynamic acceleration in the bow direction.
Damping matrixThe method comprises the following steps:
wherein,for longitudinal hydrodynamic speed derivative,/->Is the transverse hydrodynamic speed derivative,/->For the coupling hydrodynamic speed derivative of heading versus lateral, +.>For coupling hydrodynamic speed derivative transverse to heading,/->Is the derivative of hydrodynamic speed in the bow direction.
The geodetic coordinate system defined in the invention is a north-east geodetic system, namely, the north-right direction is a vertical axis, and the east-right direction is a horizontal axis; the ship body coordinate system is a front right lower system, namely, the ship bow direction is a vertical axis, and the starboard direction is a horizontal axis.
S2, designing a parameter identification algorithm:
s21, expanding and establishing a state estimation model:
the method comprises the steps of collecting ship motion measurement data, estimating a motion state in a ship motion model by using a state estimation method, and performing dimension expansion design on the state estimation model, so that parameters to be identified can be contained in a state vector of the state estimation model as states, and specifically comprises the following steps:
determining the parameters to be identified as damping matrixUnknown parameters of->、/>、/>、/>、/>And a thrust coefficient matrix->Unknown parameters of->、/>、…、/>、/>The vector composed of all the unknown parameters is marked as the parameter vector to be identified +.>The following are provided:
establishment ofParameter vector to be identifiedThe state estimation model of (2) is as follows:
wherein,for the parameter vector to be identified->First derivative of>Is->Gaussian white noise column vector of dimension.
The system measurement model is established as follows:
wherein z is a column vector consisting of a north position, an east position and a heading angle measured by a sensor,is zero-mean Gaussian white noise three-dimensional column vector.
Therefore, the established dynamic positioning ship state estimation model is as follows:
wherein,，/>the elements are ∈ ->The first derivative of the corresponding element in (c),、/>、/>the longitudinal environmental force Gaussian white noise and the transverse environmental force Gaussian white noise and the heading environmental moment Gaussian white noise are respectively generated;
order theX represents a state vector that does not include parameters to be identified in the state estimation model, the above formula can be expressed as:
wherein,is->First derivative of>As a function matrix ++>Is a noise vector.
S22, carrying out parallel filtering on multiple measurements, and improving a state estimation model:
the basic idea of the method is to further process the state estimation model after dimension expansion and to perform offline parallel filtering when performing state estimation, and to improve the convergence and accuracy of parameter estimation, the same quantity can be measured for multiple times by adopting different inputs. Under the assumption that the parameter to be estimated is constant, for each measurement,are the same, where p is the number of parameters to be identified. The dynamic positioning vessel state estimation model can then be designed as:
wherein N is the measurement times,for the N-th measurement, the state estimation model does not contain the state vector of the parameter to be identified, ">Is->First derivative of>For the nth input control variable, +.>Noise vector for the nth measurement, < >>For the column vector consisting of north position, east position and heading angle obtained by the Nth measurement,/-> N For the position under the geodetic coordinate system and the heading vector obtained by the Nth measurement, +.> N Zero-mean Gaussian white noise three-dimensional column vector obtained by Nth measurement;
by using multiple measurements, more information can be obtained from the system, which improves the accuracy of the parameter estimation and reduces the likelihood of parameter drift.
Filtering algorithm such as EKF or UKF is adopted for the model, and in the following stepsFiltering after multiple measurements to obtain parameters to be identified。
Then toThe parameters in (a) are identified in batches.
S3, parameter identification action and flow design, and the estimation of all parameters to be identified is completed according to the designed identification action and flow:
the identification action and the flow are specifically designed according to the propeller configuration of the ship. In general, decoupling longitudinal from lateral and heading motions is a better strategy from the block diagonal structure of the mass matrix M, the damping matrix D. The invention designs the following three action flows to finish the estimation of all parameters to be identified.
S31, decoupling longitudinally:
the angle of the stern propeller is set to be 0 degrees, the stern propeller synchronously rotates forward and reversely alternately, and only the forward and backward acceleration and deceleration reciprocating motion of the ship on the longitudinal channel is allowed, and the action is repeated for a plurality of times.
S32, coupling transverse direction and heading:
by setting the azimuth angles of the two stern thrusters to 90 degrees and 90 degrees below zero, the ship alternately rotates forward and forwards, so that the ship can reciprocate in transverse and heading channels, and the action is repeated for a plurality of times.
S33, a bow propeller:
the ship alternately generates force in the transverse forward direction and the transverse reverse direction by asynchronous force generation of the bow propeller, so that the ship reciprocates in the transverse direction and the bow channel, the thrust coefficient of the bow propeller is identified, and the action is repeated for a plurality of times.
The three recognition action flows have the following relationships: obtaining a longitudinal damping coefficient and a thrust coefficient of the stern propeller through the action of the step S31; substituting the identified thrust coefficient of the stern propeller into the action of the step S32 to obtain a transverse and heading damping coefficient; and finally substituting the identified transverse and heading damping coefficients into the action of the step S33 to obtain the thrust coefficient of the bow side propeller. And carrying out state estimation of N times of data acquisition and estimation of parameters to be identified by adopting an EKF or UKF algorithm.
By adopting the technical scheme, the invention has the following beneficial effects:
the invention expands the dimension of the ship dynamic positioning state estimation model, and introduces the parameters to be identified into the model, so that the parameters to be identified contained in the state vector of the expanded state estimation model are estimated at the same time of filtering. Meanwhile, the invention carries out parallel filtering processing by utilizing the information of multiple measurements to obtain more information of the system, so that the parameter estimation precision is improved, and the possibility of parameter drift is reduced. The invention also designs an action flow of parameter identification, realizes targeted batch identification, and improves the accuracy of parameter identification.
Therefore, the invention can realize the identification and acquisition of the hydrodynamic coefficient of the ship and the thrust coefficient of the propeller, thereby providing an accurate control model for the motion state estimation and motion control of the dynamic positioning ship; and the convergence of the identification parameters can be improved through algorithm design.
According to the invention, a series of identification action flows are designed, so that the identification of hydrodynamic coefficients of the longitudinal direction, the transverse direction and the heading of the dynamic positioning ship and all propulsion systems can be realized in a limited voyage; and the coupling effect of each channel is avoided as much as possible, so that the accuracy of parameter identification is improved.
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The advantages and the manner of carrying out the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which the content shown is meant to illustrate, but not to limit, the invention in any sense, and wherein:
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of three parallel filtering according to the present invention.
FIG. 3 is a flow chart of the identification operation of the present invention.
Detailed Description
As shown in fig. 1, the dynamic positioning ship parameter identification method based on multi-measurement dimension expansion parallel filtering comprises the following steps:
s1, building a dynamic positioning ship propeller model and a motion model:
the propeller is divided into a channel propeller and a full-rotation propeller, and the channel propeller and the full-rotation propeller are used for dynamically positioning the position and maintaining the heading of the ship. Taking a certain type of power positioning ship as an example, two bow-side propellers are arranged on the bow of the ship (the channel propellers are arranged on the bow side of the ship), and two stern full-rotation propellers are arranged on the stern of the ship, wherein the total number of the propellers is 4.
The ship propeller model is as follows: the longitudinal thrust, transverse thrust and heading moment generated by the propeller can be used as three-dimensional vectorsExpressed as:
wherein,r represents vector elements which are real numbers; t is a propeller arrangement matrix, K is a thrust coefficient matrix,>for the entered control variable +.>，/>For the number of propellers>=4；
Wherein,、/>、/>、/>the rotation speed of each propeller is set; t is related to the arrangement position of 4 propellers and the type of the propellers, and the dimension of T is 3×4.
The thrust coefficient matrix K is a diagonal matrix and consists of 4 propeller thrust coefficients:
the propeller arrangement matrix T may be expressed as:
wherein,azimuth angle of left side full-rotation propeller, +.>Azimuth angle of right-side full-rotation propeller, +.>(i=1, 2,3, 4) is the longitudinal moment arm of each propeller relative to the centre of gravity of the vessel, +.>(i=1, 2,3, 4) is the transverse moment arm of each propeller relative to the center of gravity of the vessel.
The geodetic coordinate system defined in the invention is a north-east geodetic system, namely, the north-right direction is a vertical axis, and the east-right direction is a horizontal axis; the ship body coordinate system is a front right lower system, namely, the ship bow direction is a vertical axis, and the starboard direction is a horizontal axis.
North position of ship under geodetic coordinate systemEast position->Heading angle->The available vectors are expressed asLongitudinal speed after decomposition in hull coordinate system +.>Lateral speed->Heading angular velocity->The available vector is denoted +.>The following conversion relation exists between the speeds of the ship body coordinate system and the ground coordinate system:
wherein,is the position and heading vector under the geodetic coordinate system,>is the velocity and angular velocity vector under the ship coordinate system, < ->Is the velocity and angular velocity vector under the geodetic coordinate system,/->The elements are ∈ ->The first derivative of the corresponding element in (a).
Coordinate transformation matrixThe expression is as follows:
wherein,is a non-singular coordinate transformation matrix, and +.>，
When the dynamic positioning ship is in a low-speed sailing state, the ship motion model is as follows:
wherein M is a quality matrix,damping matrix for wave drift damping and laminar surface friction generation, +.>Is the acceleration and angular acceleration vector of the ship under the coordinate system, < ->The elements are ∈ ->First derivative of the corresponding element in +.>Representing an unknown environment in three degrees of freedom, longitudinal, transverse, and foreA vector of forces;
quality matrixThe method comprises the following steps:
wherein,for the quality of the ship->For moment of inertia of the vessel->For the longitudinal coordinate of the center of mass of the ship>For longitudinal hydrodynamic acceleration derivative,/->Is the lateral hydrodynamic acceleration derivative,/->For the derivative of hydrodynamic acceleration coupled heading to lateral, +.>For the lateral-to-heading coupled hydrodynamic acceleration derivative,/->Is the derivative of hydrodynamic acceleration in the bow direction.
Damping matrixThe method comprises the following steps:
wherein,for longitudinal hydrodynamic speed derivative,/->Is the transverse hydrodynamic speed derivative,/->For the coupling hydrodynamic speed derivative of heading versus lateral, +.>For coupling hydrodynamic speed derivative transverse to heading,/->Is the derivative of hydrodynamic speed in the bow direction.
S2, designing a parameter identification algorithm:
s21, expanding and establishing a state estimation model:
the method comprises the steps of collecting ship motion measurement data, estimating a motion state in a ship motion model by using a state estimation method, and performing dimension expansion design on the state estimation model, so that parameters to be identified can be contained in a state vector of the state estimation model as states, and specifically comprises the following steps:
determining the parameters to be identified as damping matrixUnknown parameters of->、/>、/>、/>、/>Thrust coefficient matrix->Unknown parameters of->、/>、/>、/>Wherein->、/>、/>、/>、/>Respectively isCorresponding elements of the first row, the second column, the second row, the third column, the third row, the second column and the third row and the third column, and the vector formed by all unknown parameters is marked as a parameter vector to be identified +.>The method comprises the following steps:
establishing a parameter vector to be identifiedThe state estimation model of (2) is as follows:
wherein,for the parameter vector to be identified->First derivative of>Is a 9-dimensional gaussian white noise column vector.
The system measurement model is established as follows:
wherein z is a column vector consisting of a north position, an east position and a heading angle measured by a sensor,is zero-mean Gaussian white noise three-dimensional column vector.
Therefore, the established dynamic positioning ship state estimation model is as follows:
wherein,，/>the elements are ∈ ->The first derivative of the corresponding element in (c),、/>、/>the longitudinal environmental force Gaussian white noise and the transverse environmental force Gaussian white noise and the heading environmental moment Gaussian white noise are respectively generated;
order theX represents a state vector that does not include parameters to be identified in the state estimation model, the above formula can be expressed as:
wherein,is->First derivative of>As a function matrix ++>Is a noise vector;
wherein,、/>、/>（/>) Respectively for the propeller arrangement matrix>First, second and third rowsAn element; />（/>) Is->Thrust coefficients of the individual propellers; />（/>) Is a column vectorMiddle->Element(s)>；/>、/>、/>Is an unknown environmental force vector->Three elements of (2); />For quality matrix->First column element of the first row, +.>For quality matrix->Second column element of the second row, +.>For quality matrix->The second row and the third column elements of +.>For quality matrix->The third row of the second column element, +.>Is a quality matrixA third column element of the third row.
S22, carrying out parallel filtering on multiple measurements, and improving a state estimation model:
the basic idea of the method is to further process the state estimation model after dimension expansion and to perform offline parallel filtering when performing state estimation, and to improve the convergence and accuracy of parameter estimation, the same quantity can be measured for multiple times by adopting different inputs. Under the assumption that the parameter to be estimated is constant, for each measurement,are the same, where p is the number of parameters to be identified. The dynamic positioning vessel state estimation model can then be designed as:
wherein N is the measurement times,for the N-th measurement, the state estimation model does not contain the state vector of the parameter to be identified, ">Is->First derivative of>For the nth input control variable, +.>For the noise vector, z, obtained for the nth measurement N For the column vector consisting of north position, east position and heading angle obtained by the Nth measurement,/-> N For the position under the geodetic coordinate system and the heading vector obtained by the Nth measurement, +.> N Zero-mean Gaussian white noise three-dimensional column vector obtained by Nth measurement;
when n=3, as shown in fig. 2, three parallel filtering may be employed, i.e., by using three measurements, more information of the system may be obtained, which improves the accuracy of parameter estimation and reduces the possibility of parameter drift.
Filtering algorithms such as EKF or UKF are adopted for the model, and filtering processing is carried out after multiple measurements, so that parameters to be identified can be obtained。
Then toThe parameters in (a) are identified in batches.
S3, parameter identification action and flow design, and the estimation of all parameters to be identified is completed according to the designed identification action and flow:
the identification action and the flow are specifically designed according to the propeller configuration of the ship. In general, decoupling longitudinal from lateral and heading motions is a better strategy from the block diagonal structure of the mass matrix M, the damping matrix D. The present invention designs the following three operation flows to complete the estimation of all the parameters to be identified, as shown in fig. 3.
S31, decoupling longitudinally:
setting the angle of two stern full-rotation propellers to be 0 DEG, enabling the stern full-rotation propellers to synchronously rotate forward and backward alternately, only allowing the ship to reciprocate in a longitudinal channel in a front-back acceleration and deceleration way, repeating the motion for 3 times to ensure parameter estimation precision, and identifying after the motion to obtain a longitudinal damping coefficientAnd the thrust coefficient of two stern full-rotation propellers +.>、/>。
S32, coupling transverse direction and heading:
by arranging two stern full-rotation propellers with azimuth angles of 90 degrees and-90 degrees and asynchronous force generation, the ship alternately rotates forward, the reciprocating motion of the ship in transverse and heading channels is realized, the motion is repeated for 3 times to ensure the parameter estimation precision, and the transverse damping coefficient is obtained by identification after the motion、/>And the heading damping coefficient->、/>。
S33, a bow propeller:
the two bow thrusters are used for asynchronously exerting force, so that the two bow thrusters alternately exert force in the transverse direction and the transverse direction, the ship reciprocates in the transverse direction and the bow channel, the thrust coefficient of the bow thrusters is identified, and the motion is repeated for 3 times to ensure the parameter estimation precision. After the action, the thrust coefficient of the bow propeller is obtained through identification、/>。
The three recognition action flows have the following relationships: by the action of step S31, the longitudinal damping coefficient is obtainedThrust coefficient of stern full-rotation propeller>、/>The method comprises the steps of carrying out a first treatment on the surface of the Then substituting the identified thrust coefficient of the stern full-rotation propeller into the action of the step S32 to obtain a transverse damping coefficient +.>、/>And the heading damping coefficient->、/>The method comprises the steps of carrying out a first treatment on the surface of the Finally substituting the identified transverse and heading damping coefficients into the action of the step S33 to obtain the thrust coefficient of the bow side propeller +.>、/>。
As shown in fig. 3, an EKF or UKF algorithm is used in the process to perform state estimation of 3 times data acquisition and estimation of parameters to be identified.
The foregoing describes the embodiments of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by this patent.
Claims (9)
1. A dynamic positioning ship parameter identification method based on multiple measurement dimension expansion parallel filtering is characterized by comprising the following steps of: the method comprises the following steps:
s1, building a dynamic positioning ship propeller model and a motion model:
the ship propeller model is as follows: three-dimensional vector for longitudinal thrust, transverse thrust and heading moment generated by propellerExpressed as:
wherein,r represents vector elements which are real numbers; t is a propeller arrangement matrix, K is a thrust coefficient matrix,>for the entered control variable +.>，/>The number of the propellers;
the ship motion model is as follows:
wherein M is a quality matrix,damping matrix for wave drift damping and laminar surface friction generation, +.>Is the acceleration and angular acceleration vector of the ship under the coordinate system, < ->Is the velocity and angular velocity vector under the ship coordinate system, < ->The elements are ∈ ->First derivative of the corresponding element in +.>A vector representing an unknown environmental force composition in three degrees of freedom, longitudinal, lateral, and fore;
s2, designing a parameter identification algorithm:
s21, expanding and establishing a state estimation model:
acquiring ship motion measurement data, estimating a motion state in a ship motion model by using a state estimation method, performing dimension expansion design on the state estimation model, and taking parameters to be identified as states to be contained in a state vector of the state estimation model;
s22, parallel filtering of multiple measurements:
further processing the state estimation model after the dimension expansion, performing offline parallel filtering when performing state estimation, and establishing a dynamic positioning ship state estimation model;
s3, parameter identification action and flow design, and the estimation of all parameters to be identified is completed according to the designed identification action and flow: from the block diagonal structure of the mass matrix M and the damping matrix D, the longitudinal and transverse and heading movements are decoupled.
2. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 1, wherein the method is characterized by comprising the following steps of: in step S1, the condition for the establishment of the ship motion model is when the dynamic positioning ship is in a low-speed sailing state.
3. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S1 of the process,
wherein,for propeller speed, the dimension T is 3 x +.>；
K is a diagonal matrix, composed ofThe thrust coefficient of each propeller is composed of:
；
north position of ship under geodetic coordinate systemEast position->Heading angle->Expressed as vectorsLongitudinal speed after decomposition in hull coordinate system +.>Lateral speed->Heading angular velocity->Expressed as +.>The following conversion relation exists between the speeds of the ship body coordinate system and the ground coordinate system:
wherein,is the position and heading vector under the geodetic coordinate system,>is the velocity and angular velocity vector under the geodetic coordinate system,/->The elements are ∈ ->First derivatives of corresponding elements in (a);
coordinate transformation matrixThe expression is as follows:
wherein,is a non-singular coordinate transformation matrix, and +.>。
4. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 3, wherein the method comprises the following steps of: in the step S1 of the process,
quality matrixThe method comprises the following steps:
wherein,for the quality of the ship->For moment of inertia of the vessel->For the longitudinal coordinate of the center of mass of the ship>For longitudinal hydrodynamic acceleration derivative,/->Is the lateral hydrodynamic acceleration derivative,/->For the derivative of hydrodynamic acceleration coupled heading to lateral, +.>For the lateral-to-heading coupled hydrodynamic acceleration derivative,/->The derivative is the hydrodynamic acceleration of the bow;
damping matrixThe method comprises the following steps:
wherein,for longitudinal hydrodynamic speed derivative,/->Is the transverse hydrodynamic speed derivative,/->For the coupling hydrodynamic speed derivative of heading versus lateral, +.>For coupling hydrodynamic speed derivative transverse to heading,/->Is the derivative of hydrodynamic speed in the bow direction.
5. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 4, wherein the method comprises the following steps: in step S21, the specific method for expanding and building the state estimation model is as follows:
determining the parameters to be identified as damping matrixUnknown parameters of->、/>、/>、/>、/>And a thrust coefficient matrix->Unknown parameters of->、/>、…、/>、/>Damping matrix->And thrust coefficient matrix->The vector composed of unknown parameters in (a) is marked as a parameter vector to be identified +.>：
Establishing a parameter vector to be identifiedThe state estimation model of (2) is as follows:
wherein,for the parameter vector to be identified->First derivative of>Is->A gaussian white noise column vector of dimensions;
the system measurement model is established as follows:
wherein z is a column vector consisting of a north position, an east position and a heading angle,a zero-mean Gaussian white noise three-dimensional column vector;
the established dynamic positioning ship state estimation model is as follows:
wherein,，/>the elements are ∈ ->First derivative of the corresponding element in +.>、/>、The longitudinal environmental force Gaussian white noise and the transverse environmental force Gaussian white noise and the heading environmental moment Gaussian white noise are respectively generated;
order theX represents a state vector which does not contain parameters to be identified in the state estimation model, and the above formula is expressed as follows:
wherein,is->First derivative of>As a function matrix ++>Is a noise vector.
6. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 5, wherein the method is characterized by comprising the following steps of: in step S22, the dynamic positioning ship state estimation model is:
wherein N is the measurement times,for the N-th measurement, the state estimation model does not contain the state vector of the parameter to be identified, ">Is->First derivative of>For the nth input control variable, +.>For the noise vector, z, obtained for the nth measurement N For the column vector consisting of north position, east position and heading angle obtained by the Nth measurement,/-> N For the position under the geodetic coordinate system and the heading vector obtained by the Nth measurement, +.> N Zero-mean Gaussian white noise three-dimensional column vector obtained by Nth measurement;
filtering algorithm is adopted for the state estimation model, and filtering processing is carried out after multiple measurements to obtain parameters to be identifiedThen->The parameters in (a) are identified in batches.
7. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 6, wherein the method is characterized by comprising the following steps of: the propeller is divided into a bow propeller and a stern propeller.
8. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 7, wherein the method comprises the following steps of: in step S3, three operation flows are included to complete the estimation of all the parameters to be identified,
s31, decoupling longitudinally:
setting the angle of the stern propeller to be 0 degrees, enabling the stern propeller to synchronously rotate forward and reversely alternately, and only allowing the ship to reciprocate in a longitudinal channel in a forward and backward acceleration and deceleration way, wherein the action is repeated for a plurality of times;
s32, coupling transverse direction and heading:
by setting the azimuth angles of the two stern thrusters to 90 degrees and 90 degrees below zero, the two stern thrusters are forced asynchronously to rotate forward alternately, so that the ship can reciprocate in transverse and heading channels, and the action is repeated for a plurality of times;
s33, a bow propeller:
the bow propeller generates force asynchronously, so that the bow propeller alternately generates force in the transverse forward direction and the transverse reverse direction, the ship reciprocates in the transverse direction and the bow direction, the thrust coefficient of the bow propeller is identified, and the action is repeated for a plurality of times;
obtaining a longitudinal damping coefficient and a thrust coefficient of the stern propeller through the action of the step S31; substituting the identified thrust coefficient of the stern propeller into the action of the step S32 to obtain a transverse and heading damping coefficient; and finally substituting the identified transverse and heading damping coefficients into the action of the step S33 to obtain a thrust coefficient of the bow propeller, and carrying out state estimation of N times of data acquisition and estimation of parameters to be identified by adopting an EKF or UKF algorithm.
9. The dynamic positioning ship parameter identification method based on multi-measurement dimension-expansion parallel filtering according to claim 8, wherein the method is characterized by comprising the following steps of: the bow propeller adopts a channel propeller, and the stern propeller adopts a full-rotation propeller.
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