CN112596393A - Control method, system and storage medium for ship path tracking - Google Patents

Control method, system and storage medium for ship path tracking Download PDF

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CN112596393A
CN112596393A CN202011557563.2A CN202011557563A CN112596393A CN 112596393 A CN112596393 A CN 112596393A CN 202011557563 A CN202011557563 A CN 202011557563A CN 112596393 A CN112596393 A CN 112596393A
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state
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CN112596393B (en
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刘佳仑
张培
李诗杰
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Wuhan University of Technology WUT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P.I., P.I.D.

Abstract

The invention discloses a control method, a system and a storage medium for ship path tracking, wherein the method comprises the following steps: applying a PID control law to a vessel controller and storing a first vessel state and first control input data; discretizing the ship motion model in an Euler discrete mode to obtain a linear prediction model; estimating a first ship state and first control input data by adopting a linear regression mode, and constructing a ship estimation model by combining regression variables; calculating to obtain a ship linear time-varying prediction model according to the first ship state, the first control input data, the linear prediction model and the ship estimation model; applying a model predictive control law to the linear time-varying predictive model and storing a second vessel state and second control input data; and calculating a control sequence according to the ship state and the control input data and updating the ship path control state. The invention reduces the calculation amount under the condition of improving the ship path tracking precision. The invention can be applied to the technical field of ship control.

Description

Control method, system and storage medium for ship path tracking
Technical Field
The invention relates to the technical field of ship control, in particular to a ship path tracking control method, a ship path tracking control system and a storage medium.
Background
At present, most cargo ships are under-actuated, and are provided with propellers and rudders to carry out pitch and roll motions, but at present, the ship motion related hydrodynamics is nonlinear, so that no corresponding actuator is used for directly controlling the roll motion, and accurate ship path tracking control is difficult to realize. Wherein the purpose of the path tracking control is to restrict the motion of the object to a specified path in space without temporal parameterization. For model predictive control, it may provide a flexible framework for dealing with complex nonlinear system dynamics and system constraints, however, for a system containing complex higher-order nonlinear terms, applying model predictive control directly to a nonlinear system model may take a large amount of computation, while over-simplification may result in reduced performance and control accuracy.
Disclosure of Invention
To solve the above technical problems, the present invention aims to: provided are a control method, system, and storage medium for ship path tracking, which can reduce the amount of computation and improve the performance and control accuracy when model predictive control is applied to a nonlinear system model.
In a first aspect, an embodiment of the present invention provides:
a method of controlling vessel path tracking, comprising the steps of:
applying a PID control law to a vessel controller and storing a first vessel state and first control input data;
discretizing the ship motion model in an Euler discrete mode to obtain a linear prediction model;
estimating a first ship state and first control input data by adopting a linear regression mode, and constructing a ship estimation model by combining regression variables;
calculating to obtain a ship linear time-varying prediction model according to the first ship state, the first control input data, the linear prediction model and the ship estimation model;
applying a model predictive control law to the linear time-varying predictive model and storing a second vessel state and second control input data;
calculating to obtain a control sequence according to the first ship state, the first control input data, the second ship state and the second control input data;
and updating the ship path control state according to the first element of the control sequence.
According to the control method for ship path tracking of the embodiment of the first aspect of the invention, a PID control law is applied to a ship control system, so that the accurate model of the hydrodynamics of the ship control system is not needed to be concerned, then the ship motion model is discretized in an Euler discrete mode to obtain a linear prediction model, meanwhile, a first ship state is estimated according to a regression variable and a linear regression mode to obtain a ship estimation model, then a linear time-varying prediction model is obtained through calculation, then the model prediction control law is applied to the linear time-varying prediction model, a second ship state and second control input data are generated, a control sequence of a ship is obtained through calculation, the ship control state is updated through a first element of the control sequence, the capacity of processing constraint through the model prediction control law is improved, and under the condition of improving the ship path tracking precision, the calculation amount is reduced.
In some embodiments of the invention, the applying the PID control law to the ship controller and storing the first ship state and the first control input data comprises:
after the PID control law is applied to the ship controller, the first ship state and the first control input data are stored after the first preset times of iteration.
In some embodiments of the present invention, discretizing the ship motion model in an euler discretization manner to obtain a linear prediction model includes:
discretizing the ship motion model in an Euler discrete mode to obtain a discrete model;
and performing linear conversion on the discrete model to obtain a linear prediction model.
In some embodiments of the present invention, the estimating the first ship state and the first control input data by using a linear regression method, and constructing the ship estimation model by combining regression variables includes:
estimating the first ship state and the first control input data by adopting a linear regression mode;
obtaining the numerical value of a regression variable;
and constructing a ship estimation model according to the estimation result and the numerical value of the regression variable.
Some embodiments of the present invention further include, after the building of the ship estimation model according to the estimation result and the value of the regression variable, the following steps:
and determining the size of the regression variable by adopting a kernel density estimation function.
In some embodiments of the invention, the applying a model predictive control law to the linear time-varying predictive model and storing the second vessel state and the second control input data includes:
and applying a model prediction control law to the linear time-varying prediction model, and storing a second ship state and second control input data after iterating for a second preset time.
In some embodiments of the present invention, the calculating a control sequence according to the first ship state, the first control input data, the second ship state, and the second control input data specifically includes:
and iterating the third preset times by adopting an LMPC algorithm according to the first ship state, the first control input data, the second ship state and the second control input data to obtain a plurality of control sequences.
Some embodiments of the invention, said updating the vessel path control state according to a first element of the control sequence, comprises:
determining an optimal control sequence of the plurality of control sequences;
and updating the ship path control state according to the first element of the optimal control sequence.
In a second aspect, an embodiment of the present invention provides:
a control system for vessel path tracking, comprising:
at least one memory for storing a program;
at least one processor, configured to load the program to execute the control method for vessel path tracking provided in the embodiment of the first aspect.
In a third aspect, an embodiment of the present invention provides:
a storage medium having stored therein a program executable by a processor, the program executable by the processor being configured to perform the control method for vessel path tracking provided by the embodiments of the first aspect when executed by the processor.
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.
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The invention is further described with reference to the following figures and examples, in which:
FIG. 1 is a flow chart of a method of controlling ship path tracking according to an embodiment of the present invention;
FIG. 2 is a graphical reference frame diagram of a ship path according to one embodiment;
figure 3 is a diagram of an LMPC control framework in accordance with one embodiment.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
First, the terms appearing in the present application are explained:
PID control law: it means that the deviation e is subjected to a comprehensive operation of proportion, integration and differentiation, and the controller generates control input data u which can make the deviation zero or small by adjusting control parameters.
MPC control law: the core of the method is to solve a finite time domain optimization problem, determine a control action through the optimization of a certain performance index, and belong to a rolling optimization process.
LMPC algorithm: according to a data set which is obtained by utilizing a PID control law and an MPC control law and relates to the ship state and control input, online iteration is carried out for a preset number of times, so that the control system gradually reaches a control target.
Referring to fig. 1, an embodiment of the present invention provides a control method for ship path tracking, and the embodiment may be applied to a server, a processor, and a background controller corresponding to various platforms.
Specifically, in the implementation process, the embodiment includes the following steps:
s11, applying the PID control law to the vessel controller and storing the first vessel state and the first control input data. In this step, it is specifically the PID control law
Figure BDA0002855661680000041
And
Figure BDA0002855661680000042
the method is applied to a ship controller, and after the first preset times of iterative processing is carried out in the controller, the ship state is stored
Figure BDA0002855661680000043
As a first vessel state and control input
Figure BDA0002855661680000044
As first control input data. Wherein the first preset number of times may be M1Secondly; delta and n are respectively a rudder angle and a propeller rotating speed;
Figure BDA0002855661680000045
and
Figure BDA0002855661680000046
are all control parameters; e ═ ey,eψ}，eψAnd eyRespectively representing course angle error and transverse distance error between the middle part of the ship and the track central line;
Figure BDA0002855661680000047
indicating an error; state of the vessel
Figure BDA0002855661680000048
Control input
Figure BDA0002855661680000049
u denotes a longitudinal speed of the vessel, v denotes a lateral speed of the vessel, r denotes a yaw rate of the vessel, and s denotes a distance over which the vessel travels along the track. In the above application process, the control target is ey→0、eψ→ 0, the ship motion model shown in equation 1 is constructed according to the above definition:
Figure BDA00028556616800000410
and according to the phase function
Figure BDA00028556616800000411
And calculating the ship control input capable of well keeping the ship motion track from the starting point to the end point when the PID control law is applied.
And S12, discretizing the ship motion model by adopting an Euler discrete mode to obtain a linear prediction model.
Specifically, the method comprises the following steps of firstly discretizing the ship motion model of formula 1 in an Euler discrete mode to obtain a discrete model shown in formula 2:
Figure BDA0002855661680000051
dt in equation 2 is a discrete time. And to obtain the form xk+1＝Axk+ Bu + C linear prediction model, therefore, the discrete model of equation 2 is linearly transformed to obtain the linear prediction model shown in equation 3:
Figure BDA0002855661680000052
and S13, estimating the first ship state and the first control input data by adopting a linear regression mode, and constructing a ship estimation model by combining regression variables.
In this step, first, a linear regression method is used to estimate u, v, and r of the first ship state and the first control input data, and a regression variable is used
Figure BDA0002855661680000053
And based on values of regression variables
Figure BDA0002855661680000054
Constructing a ship estimation model shown in formula 4:
Figure BDA0002855661680000055
wherein the content of the first and second substances,
Figure BDA0002855661680000056
representing a vector rl(x) The ith element of (1).
In the above embodiment, in order to determine the value of Γ, a kernel density estimation function shown in equation 5, namely, an Epanechnikov kernel function, is introduced to determine the magnitude of the regression variable:
Figure BDA0002855661680000057
the purpose of introducing equation 5 is to find Γ for u, v, and ru、ΓvAnd ΓrValue of (A) is Ju、JvAnd JrMinimum, wherein, Ju、JvAnd JrThe calculation process of (a) is shown in equations 6, 7 and 8:
Figure BDA0002855661680000058
Figure BDA0002855661680000061
Figure BDA0002855661680000062
in the formula, parameter
Figure BDA0002855661680000063
Represents a bandwidth;
Figure BDA0002855661680000064
w is a matrix that considers weights of different variables; using a kernel function, Ju、JvAnd JrA quadratic programming problem is formed, and the solution can be carried out through an optimization solver to obtain the gammau、ΓvAnd ΓrThe value of (c).
And S14, calculating according to the first ship state, the first control input data, the linear prediction model and the ship estimation model to obtain a ship linear time-varying prediction model.
Specifically, the step is to obtain a linear time-varying prediction model shown in formula 9 based on the ship state and control input data stored in the PID control law application process and by combining formula 3 and formula 4:
Figure BDA0002855661680000065
wherein the content of the first and second substances,
Figure BDA0002855661680000066
at the ith iteration, the state of the vessel at time t.
S15, applying a model predictive control law to the linear time-varying predictive model and storing a second ship state and second control input data; i.e. applying the MPC control law to a linear time-varying prediction model. In the application process, after the MPC control law is applied to the linear time-varying prediction model, iterating for a second preset time M2And then, the ship can be controlled from the starting point to the end point. In the control process, the MPC controller needs to compute a finite time domain optimization problem at each iteration i, as shown in equation 10:
Figure BDA0002855661680000067
in equation 10, the constraint xk+1＝Axk+BukIs a linear prediction model; x is the number of0＝xsThe initial state of the ship is set;
Figure BDA0002855661680000068
and
Figure BDA0002855661680000069
control constraints for vessel state and input; defining a prediction time domain as N; obtaining an optimal control sequence U ═ U { U } of each sampling time k by calculating an optimal control problem0,…,uN-1Applying the first element of the control sequence to the ship controller, and iterating M2To create another data set.
In MPC control law application, based on phase cost function
Figure BDA00028556616800000610
And calculating the ship control input capable of well keeping the ship motion track from the starting point to the end point of the ship.
And S16, calculating to obtain a control sequence according to the first ship state, the first control input data, the second ship state and the second control input data.
Specifically, the step is based on a first ship state and first control input data stored by applying a PID control law, and a second ship state and second control input data stored by applying an MPC control law, and adopts an LMPC algorithm to iterate for a third preset number of times M3Next, the process is carried out. Wherein when i<M3If the ship does not reach the end point, the ship state is initialized in the ith iteration, and a prediction model shown in a formula 11 is established by adopting the offline data ship state and the control input data stored by a PID control law and an MPC control law:
Figure BDA0002855661680000071
in formula 11, xi+1|k＝Axt|k+But|kA linear ship prediction model;
Figure BDA0002855661680000072
the initial state of the ship is set; x is the number oft|k∈χ,ut|kE is V as the state and input constraint of the ship; x is the number ofk+N|k∈SSi-1To ensure that the final state of the vessel belongs to the safety set SSi-1(ii) a In each iteration i, the controller starts from the previous i-NsSelecting N in the sub-iterationssPoint to create a security set SSiWherein N isssAnd NsAre all control parameters, safety set SS of the ith iterationiConsisting of all successful traces performed in the previous iteration i times,
Figure BDA0002855661680000073
Qi(x) The learning cost is expressed, and the calculation process is shown in equation 12:
Figure BDA0002855661680000074
in the formula 12, the first and second groups of the formula,
Figure BDA0002855661680000075
and S17, updating the ship path control state according to the first element of the control sequence. Specifically, this step is to select an optimal control sequence from the control sequences calculated in step S16
Figure BDA0002855661680000076
The first element of the optimal control sequence is applied to the vessel and the vessel state is updated.
And then waiting for the next sampling and setting the sampling index k to k +1, and circulating the steps S11-S17 until the ship controller converges.
It can be seen that the above embodiments fully utilize the MPC processing constraint capability while ensuring the iterative performance, while minimizing the total cost; in each iteration, the ship controller calculates a track for steering the ship from the starting point to the end point, and data obtained in each iteration is used for improving the performance of the ship controller until the controller converges, so that the under-actuated ship can run along the center line of the restricted channel from the starting point to the end point.
In some embodiments, the above embodiments are applied to a specific ship control process, which specifically includes:
as shown in fig. 2, a curvilinear reference of the vessel path is provided to describe the motion of the vessel in the channel by the curvilinear reference frame, so that a reference path does not need to be generated in advance. In this reference system, the channel is divided into a series of straight lines and curves, where information about the length, the safe distance to the river bank, is represented by the curvature of these straight lines. e.g. of the typeψAnd eyRespectively representing course angle error and transverse distance error between the middle part of the ship and the track central line, s represents the distance of the ship sailing along the track, and the ship control target is ey→0、eψ→ 0, wherein eψ、eyAnd s is expressed as a ship motion model shown in equation 1:
Figure BDA0002855661680000081
the euler discretization is adopted for formula 1 to obtain a discrete model shown in formula 2:
Figure BDA0002855661680000082
dt in equation 2 is a discrete time. And to obtain the form xk+1＝Axk+ Bu + C linear prediction model, therefore, the discrete model of equation 2 is linearly transformed to obtain the linear prediction model shown in equation 3:
Figure BDA0002855661680000083
on the basis of the LMPC control framework shown in FIG. 3, the steps when the method is applied to the limited water area under-actuated ship comprise:
step 1, controlling PID
Figure BDA0002855661680000084
And
Figure BDA0002855661680000085
applied to ship controllers and iterated M1Next, the ship state is stored
Figure BDA0002855661680000086
And control input
Figure BDA0002855661680000087
And according to the phase function
Figure BDA0002855661680000091
And calculating the ship control input capable of well keeping the ship motion track from the starting point to the end point when the PID control law is applied.
Step 2, estimating u, v and r of ship state and control input by adopting a linear regression modeCounting, simultaneous referencing of regression variables
Figure BDA0002855661680000092
And based on values of regression variables
Figure BDA0002855661680000093
Constructing a ship estimation model shown in formula 4:
Figure BDA0002855661680000094
wherein the content of the first and second substances,
Figure BDA0002855661680000095
representing a vector rl(x) The ith element of (1).
Step 3, introducing an Epanechnikov kernel function shown in a formula 5 to determine the size of the regression variable gamma:
Figure BDA0002855661680000096
the purpose of introducing equation 5 is to find Γ for u, v, and ru、ΓvAnd ΓrValue of (A) is Ju、JvAnd JrMinimum, wherein, Ju、JvAnd JrThe calculation process of (a) is shown in equations 6, 7 and 8:
Figure BDA0002855661680000097
Figure BDA0002855661680000098
Figure BDA00028556616800000913
in the formula, parameter
Figure BDA0002855661680000099
Represents a bandwidth;
Figure BDA00028556616800000910
w is a matrix that considers weights of different variables; using a kernel function, Ju、JvAnd JrA quadratic programming problem is formed, and the solution can be carried out through an optimization solver to obtain the gammau、ΓvAnd ΓrThe value of (c).
Step 4, based on the ship state and control input data stored in the PID control law application process, and combining the formula 3 and the formula 4, obtaining a linear time-varying prediction model shown in the formula 9:
Figure BDA00028556616800000911
wherein the content of the first and second substances,
Figure BDA00028556616800000912
at the ith iteration, the state of the vessel at time t.
Step 5, applying MPC control law on linear time-varying prediction model, and iterating M2Then, defining the time domain as N, the MPC controller needs to compute a finite time domain optimization problem at each iteration i, as shown in equation 10:
Figure BDA0002855661680000101
in equation 10, the constraint xk+1＝Axk+BukIs a linear prediction model; x is the number of0＝xsThe initial state of the ship is set;
Figure BDA0002855661680000102
and
Figure BDA0002855661680000103
control constraints for vessel state and input; defining a prediction time domain as N; obtaining an optimal control sequence U ═ U { U } of each sampling time k by calculating an optimal control problem0,…,uN-1Applying the first element of the control sequence to the ship controller, and iterating M2To create another data set.
Step 6, according to the stage cost function
Figure BDA0002855661680000104
And calculating the ship control input capable of well keeping the ship motion track from the starting point to the end point of the ship.
Step 7, according to the off-line data stored by the PID control law and the MPC control law, adopting an LMPC algorithm to iterate M3Next, the process is carried out. Wherein when i<M3If the ship does not reach the end point, the ship state is initialized in the ith iteration, and a prediction model shown in a formula 11 is established by adopting the offline data ship state and the control input data stored by a PID control law and an MPC control law:
Figure BDA0002855661680000105
in formula 11, xi+1|k＝Axt|k+But|kA linear ship prediction model;
Figure BDA0002855661680000106
the initial state of the ship is set; x is the number oft|k∈χ,ut|kE is V as the state and input constraint of the ship; x is the number ofk+N|k∈SSi-1To ensure that the final state of the vessel belongs to the safety set SSi-1(ii) a In each iteration i, the controller starts from the previous i-NsSelecting N in the sub-iterationssPoint to create a security set SSiWherein N isssAnd NsAre all control parameters, safety set SS of the ith iterationiConsisting of all successful traces performed in the previous iteration i times,
Figure BDA0002855661680000107
Qi(x) The learning cost is expressed, and the calculation process is shown in equation 12:
Figure BDA0002855661680000111
in the formula 12, the first and second groups of the formula,
Figure BDA0002855661680000112
step 8, calculating an optimal control sequence
Figure BDA0002855661680000113
The first element of the optimal control sequence is applied to the vessel and the vessel state is updated.
Step 9, waiting for next sampling, setting a sampling index k equal to k +1, and circulating the steps 1 to 8 until the ship controller converges, and ending; the motion-controlled brake is used as
Figure BDA0002855661680000114
In addition, an embodiment of the present invention provides a control system for ship path tracking, including:
at least one memory for storing a program;
at least one processor for loading the program to perform the control method of vessel path tracking shown in fig. 1.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
An embodiment of the present invention provides a storage medium in which a processor-executable program is stored, the processor-executable program being configured to perform the control method for vessel path tracking shown in fig. 1 when executed by a processor.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A method of controlling vessel path tracking, comprising the steps of:
applying a PID control law to a vessel controller and storing a first vessel state and first control input data;
discretizing the ship motion model in an Euler discrete mode to obtain a linear prediction model;
estimating a first ship state and first control input data by adopting a linear regression mode, and constructing a ship estimation model by combining regression variables;
calculating to obtain a ship linear time-varying prediction model according to the first ship state, the first control input data, the linear prediction model and the ship estimation model;
applying a model predictive control law to the linear time-varying predictive model and storing a second vessel state and second control input data;
calculating to obtain a control sequence according to the first ship state, the first control input data, the second ship state and the second control input data;
and updating the ship path control state according to the first element of the control sequence.
2. The method according to claim 1, wherein the applying a PID control law to a vessel controller and storing a first vessel state and first control input data comprises:
after the PID control law is applied to the ship controller, the first ship state and the first control input data are stored after the first preset times of iteration.
3. The method for controlling ship path tracking according to claim 1, wherein discretizing the ship motion model in an euler discrete mode to obtain a linear prediction model comprises:
discretizing the ship motion model in an Euler discrete mode to obtain a discrete model;
and performing linear conversion on the discrete model to obtain a linear prediction model.
4. The method for controlling ship path tracking according to claim 1, wherein the estimating the first ship state and the first control input data by using a linear regression method and building a ship estimation model by combining regression variables comprises:
estimating the first ship state and the first control input data by adopting a linear regression mode;
obtaining the numerical value of a regression variable;
and constructing a ship estimation model according to the estimation result and the numerical value of the regression variable.
5. The method for controlling ship path tracking according to claim 4, further comprising the following steps after the ship estimation model is constructed according to the estimation result and the numerical value of the regression variable:
and determining the size of the regression variable by adopting a kernel density estimation function.
6. The method according to claim 1, wherein the applying a model predictive control law to the linear time-varying predictive model and storing a second vessel state and second control input data comprises:
and applying a model prediction control law to the linear time-varying prediction model, and storing a second ship state and second control input data after iterating for a second preset time.
7. The control method for vessel path tracking according to claim 1, wherein the control sequence calculated from the first vessel state, the first control input data, the second vessel state and the second control input data is specifically:
and iterating the third preset times by adopting an LMPC algorithm according to the first ship state, the first control input data, the second ship state and the second control input data to obtain a plurality of control sequences.
8. The method of claim 7, wherein updating the ship path control state according to the first element of the control sequence comprises:
determining an optimal control sequence of the plurality of control sequences;
and updating the ship path control state according to the first element of the optimal control sequence.
9. A control system for vessel path tracking, comprising:
at least one memory for storing a program;
at least one processor for loading the program to perform the method of controlling vessel path tracking according to any one of claims 1 to 8.
10. A storage medium in which a processor-executable program is stored, wherein the processor-executable program is configured to perform the control method for vessel path tracking according to any one of claims 1 to 8 when executed by a processor.
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