CN111830989A - Unmanned ship path tracking control method based on internal model control and genetic algorithm - Google Patents

Unmanned ship path tracking control method based on internal model control and genetic algorithm Download PDF

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CN111830989A
CN111830989A CN202010741099.6A CN202010741099A CN111830989A CN 111830989 A CN111830989 A CN 111830989A CN 202010741099 A CN202010741099 A CN 202010741099A CN 111830989 A CN111830989 A CN 111830989A
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unmanned ship
control
los
controller
genetic algorithm
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CN111830989B (en
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杨琛
蒋鑫
胡佳伟
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Shanghai Ocean University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/0206—Control of position or course in two dimensions specially adapted to water vehicles
    • 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
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours

Abstract

The invention discloses an unmanned ship path tracking control method based on internal model control and a genetic algorithm, which comprises the following steps: s1, establishing a motion mathematical model of the unmanned ship; s2, designing an improved LOS guidance law, and adaptively adjusting the forward sight distance by establishing a nonlinear relation between the steering radius and the transverse deviation of the unmanned ship; s3, designing an internal model controller according to the mathematical model of the unmanned ship; and S4, carrying out global optimization on the controller parameters by adopting a genetic algorithm. The self-adaptive adjustment capability of the unmanned ship in the motion process can be enhanced; model errors can be eliminated to a certain degree, so that the path tracking controller is more stable; and parameters of the controller are flexibly adjusted, so that the unmanned ship obtains better dynamic performance in actual motion.

Description

Unmanned ship path tracking control method based on internal model control and genetic algorithm
Technical Field
The invention relates to the technical field of path tracking, in particular to an unmanned ship path tracking control method based on internal model control and genetic algorithm.
Background
The unmanned ship is an intelligent, automatic and unmanned water surface operation platform and is widely applied to multiple fields of military operation reconnaissance, large water area monitoring, ocean resource exploration and the like. The motion control technology is a key part for completing autonomous operation of the unmanned ship in a water area environment, and is also a hotspot problem in the research field of the unmanned ship. The research content mainly comprises navigation speed and course control, track tracking control, path tracking control and the like. The scheme of the invention mainly designs the contents of the path tracking control of the unmanned ship.
In the actual sailing process, the motion state of the unmanned ship is influenced by factors such as external environment wind, waves, currents and the like besides the instability of the system of the unmanned ship, so that the maneuvering performance and the control effect of the unmanned ship are not as good as expected. In order to solve the problems, the main research methods comprise an LOS guidance law control method, sliding mode control, backstepping control, prediction model control, intelligent algorithm control and the like. It should be noted that in practical applications, it is common to improve the system based on a certain method or solve a specific problem by fusing a plurality of methods.
The problems existing in the prior art are as follows:
(1) the existing path tracking scheme depends on an accurate ship model, and when a model error exists and a large course error and a flight path error are caused by external environment disturbance, a ship cannot adjust a motion state in a self-adaptive mode, so that the control effect is not as expected.
(2) The model parameters of the ship or the control parameters of the controller are manually adjusted by adopting an empirical trial and error method, the calculation burden is large, the timeliness is poor, and the actual engineering application is not facilitated.
The invention aims to enhance the anti-interference capability and the self-adaptive capability in the motion control process of the unmanned ship and improve the dynamic performance of the unmanned ship by optimizing the parameters of the controller.
Disclosure of Invention
In order to solve the existing technical problems, the invention aims to design a path tracking control scheme based on an internal model control strategy and genetic algorithm parameter optimization. The internal model control structure is suitable for a large time-lag system, can eliminate model errors to a certain extent under the action of deviation integral, has better anti-interference capability, and simultaneously optimizes the parameters of the controller by utilizing a genetic algorithm global search mechanism so as to improve the dynamic quality of the controller.
The invention is realized by the following technical scheme:
an unmanned ship path tracking control method based on internal model control and genetic algorithm comprises the following steps:
s1, establishing a state space type unmanned ship linear mathematical model:
Figure BDA0002606742700000021
v, r, ψ is a state variable, where ψ is a heading angle, which is a control input amount, i.e., a propeller deflection angle;
s11, converting the formula (1) into a second-order transfer function model, namely a Nomoto model:
Figure BDA0002606742700000031
Figure BDA0002606742700000032
Figure BDA0002606742700000033
k, T in the formula (2) are all the maneuverability indexes of the unmanned ship, which reflect the response characteristic of heading change to control, and are convenient for analyzing the rule that the unmanned ship moves along with the change of model parameters, and the static gain from(s) to r(s) is called as the gyration index. The time constant T is a followability index;
s12, defining the environmental interference as w1、w2、w3Approximately describes the environmental interference, and satisfies:
Figure BDA0002606742700000034
wherein alpha is1、α2And alpha1The weight parameters of the interference of wind, wave and flow on the plane three degrees of freedom respectively represent rand1(t), rand2(t) and rand3(t) as [ -1,1]A uniform random number of;
s13, obtaining a space type unmanned ship motion mathematical model under the interference environment:
Figure BDA0002606742700000035
s2, designing an LOS guidance law, and adaptively adjusting the forward sight distance by establishing a nonlinear relation between the steering radius and the transverse deviation of the unmanned ship so as to improve the tracking effect of the unmanned ship on the expected track;
step S21.(x, y) is the current ship' S position, (x)k,yk) Representing a certain track point on the expected track, wherein the LOS vector points to the LOS coordinate from the real-time coordinate of the unmanned ship, and the included angle between the vector and the expected track is an LOS angle;
s22, taking the current position (x, y) of the unmanned ship as the center of a circle, and taking nL as the radius and the path PkPk-1Intersect at two points and get close to (x)k,yk) The intersection point of (A) is LOS coordinate (x)Los,yLos) Thereby obtaining a LOS vector;
ψLOS＝tan2(ye,Δ),Δ＞0 (7)
ψd＝ψp-ψLOS(8)
in the formula (7), yeIs a lateral deviation, Delta is a forward-looking distance, and in equation (8), phipIs the azimuth angle, ψ, of the desired trackdIs an ideal heading angle;
(xLos,yLos) Is calculated as:
(xLos-x)2+(yLos-y)2＝R2(9)
Figure BDA0002606742700000041
step S23. in order to enable the unmanned ship to be in yeWhen larger, it converges rapidly to the desired trajectory at yeWhen smaller, the desired heading psidThe fluctuation is not too big, reduces the oscillation number (effect) effectively, proposes:
Figure BDA0002606742700000042
that is, the turning radius is defined as a nonlinear function of the lateral deviation, L in the formula (11) is the ship length, n > is 1,
s24, carrying out a contrast experiment on the improved LOS guidance law and the traditional LOS guidance law, and setting n to be 2, L to be 1 and L to be 5;
and S3, designing an internal model controller, wherein the internal model control is a control strategy of connecting a control object and an actual object mathematical model in parallel and designing the controller by adopting a phase cancellation method. The deviation integral action of the internal model control structure and the added filtering link ensure that the controller has disturbance resistance and control capability on a large time-lag system, and the parameters of the controller are clear and visible, thereby being convenient for analyzing and adjusting the performance of the controller;
step S31. controller G is arranged in the dotted line boxc(s)，GIMC(s) is a transfer function of the transfer function,
Figure BDA0002606742700000051
is an object model, R(s) is an input set point, Gp(s) is the control object, D(s) is the disturbance parameter, Y(s) is the system output, then controller Gc(s) can be expressed as:
Figure BDA0002606742700000052
Figure BDA0002606742700000053
Figure BDA0002606742700000054
in the above formula, the first and second carbon atoms are,
Figure BDA0002606742700000055
is that
Figure BDA0002606742700000056
F(s) is a filter, f(s) affects the closed-loop performance and robustness of the system, the time constant in the filterTfIs a gain parameter;
s32, obtaining a control object model according to a ship mathematical model formula (2):
Figure BDA0002606742700000057
combining the formula (13) and the formula (14) to obtain
Figure BDA0002606742700000058
Taking r in the equation (16) to be 3, the internal model controller can be expressed as
Figure BDA0002606742700000059
S4, optimizing controller parameters by adopting a genetic algorithm, and performing global optimization on the control parameters by adopting the genetic algorithm so as to enable the unmanned ship to obtain better dynamic characteristics in the actual motion process;
s41, parameter coding and population initialization;
s42, a target function and a fitness function are obtained;
s43, selecting, crossing and mutating;
and S44, repeating the steps S42 and S43 until the expected effect of convergence or parameter optimization is achieved.
Further, α in the step S141≥0，α2≥0，α3≥0。
Further, in the step S32, a suitable gain parameter T is selected in consideration of the expected performance and the robustness of the systemfEspecially critical, smaller TfThe value can enable the system to obtain faster response capability and better interference suppression capability, and larger TfThe values can make the control system more stable and robust.
Further, in the step S41, the parameters that the controller needs to adjust include the maneuverability index K, T and the gain parameter TfPair K, T and T by genetic algorithmfAnd (6) coding is carried out. In order to improve the searching speed and the optimization effect of the algorithm, a searching space of each parameter is defined as follows: [0,1]、[0,10]、[0，10]Generating a given controller parameter value according to the initial state of the unmanned ship, generating thirty individuals by the initial population based on the given parameter value reorganization and combining the following formula, evolving fifty generations, and in each optimization next, keeping the optimal individuals to be used for generating the next suboptimal initial population:
Figure BDA0002606742700000061
Piis an individual of the starting population, KjIs a given set of parameter values, rand (t) for generating [ -0.1,0.1]The random number of (2).
Further, in step S42, the objective function is a performance index for evaluating a system control effect, the fitness function is a measure of the quality of each population, the lateral error and the heading error are both actual expressions of the unmanned ship control effect, and the following formula is established as the objective function:
Figure BDA0002606742700000071
where T' is the sampling period, #eThe course error is shown, the smaller the objective function value is, the better the system control performance is, the reciprocal of the objective function is selected as the corresponding fitness function according to the maximization principle:
Figure BDA0002606742700000072
further, in the step S43, good individuals are selected by using a roulette selection method according to the fitness value of each individual, and next generation individuals are obtained by using arithmetic crossover and uniform variation to generate a next generation population, wherein the best individuals in each generation are directly retained to the next generation.
Further, in step S43, the crossover probability is set to 0.8, and the mutation probability is set to 0.1.
The principle of the application is as follows: according to the scheme, a guidance link is separated from a heading control link, the guidance link is formed based on an improved LOS guidance law, and the heading control link is formed based on an internal model control structure and a genetic algorithm. In a guidance link, reference coordinate information on an expected path is an input signal, and an output signal is an ideal heading of the current unmanned ship; in the heading control link, an ideal heading is used as an input signal, control parameters are optimized through a genetic algorithm, an output signal is a deflection angle of a propeller, and the deflection angle is used as a motion instruction of the unmanned ship to be executed.
The invention has the beneficial effects that:
1. based on the traditional LOS guidance law, an improved LOS guidance law with the steering radius changing along with the transverse error in a nonlinear mode is provided, the track error caused by the external environment is effectively reduced, and the self-adaptive adjustment capability of the unmanned ship in the movement process is enhanced;
2. the controller designed based on the advantages of the internal model control structure can eliminate model errors to a certain extent, so that the path tracking controller is more stable.
3. The parameters of the controller are optimized by adopting a genetic algorithm, so that the parameters of the controller can be flexibly adjusted, and the unmanned ship can obtain better dynamic performance.
Drawings
Fig. 1 is a schematic diagram of a path tracking control module according to the present application.
FIG. 2 is a schematic diagram of an improved LOS guidance law in the present application.
FIG. 3 is a diagram illustrating the non-linear relationship between lateral error and turning radius in the present application.
Fig. 4 is a schematic diagram of an internal mold control structure according to the present application.
Fig. 5 is a diagram illustrating the optimization result of the controller parameters according to the present application.
Figure 6 is a schematic diagram of the linear tracking control based on the conventional scheme,
wherein 6a is a schematic diagram of the linear tracking control effect,
and 6b is a schematic diagram of errors of the position and the heading angle.
Figure 7 is a schematic diagram of a linear tracking control according to the inventive solution,
wherein, 7a is a schematic diagram of the linear tracking control effect,
7b position and heading angle error diagram.
Fig. 8 is a diagram illustrating the optimization result of the controller parameters according to the present application.
Figure 9 is a schematic diagram of a circular trajectory tracking control based on a conventional scheme,
wherein, 9a is a schematic diagram of the tracking control effect of the circular track,
9b position and heading angle error diagram.
Figure 10 is a schematic diagram of a circular trajectory tracking control in accordance with aspects of the present invention,
wherein, 10a is a schematic diagram of the effect of circular track tracking control,
10b position and heading angle error diagram.
Fig. 11 is a flow chart of a path tracking control scheme in the present application.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the unmanned ship firstly obtains coordinate point information of a desired path, outputs an ideal heading angle of the unmanned ship through an improved LOS guidance law in combination with current position information of the unmanned ship, and uses the heading angle as input of the internal model controller. Meanwhile, the parameters of the internal model controller are optimized by combining the current transverse error and the course error by using a genetic algorithm. And finally, outputting the deflection angle of the unmanned ship propeller as a motion instruction of the unmanned ship through the internal model controller, and controlling the unmanned ship to track the path.
As shown in fig. 2 and3, an LOS guidance law is designed, the forward sight distance is adaptively adjusted by establishing a nonlinear relation between the steering radius and the lateral deviation of the unmanned ship, and therefore the tracking effect of the unmanned ship on the expected track is improved. (x, y) is the current ship position, (x)k,yk) Indicating a track point on the desired trackThe LOS vector points from the real-time coordinates of the unmanned ship to the LOS coordinates, and the angle between the vector and the expected track is the LOS angle. Taking the current position (x, y) of the unmanned ship as the center of a circle, nL as the radius and the path PkPk-1Intersect at two points and get close to (x)k,yk) The intersection point of (A) is LOS coordinate (x)Los,yLos) Thereby obtaining a LOS vector;
ψLOS＝tan2(ye,Δ),Δ＞0 (8)
ψd＝ψp-ψLOS(9)
in the formula (8), yeIs a lateral deviation, Delta is a forward-looking distance, and in equation (9), phipIs the azimuth angle, ψ, of the desired trackdIs an ideal heading angle;
(xLos,yLos) Is calculated as:
(xLos-x)2+(yLos-y)2＝R2(10)
Figure BDA0002606742700000101
in order to enable the unmanned ship to be in yeWhen larger, it converges rapidly to the desired trajectory at yeWhen smaller, the desired heading psidThe fluctuation is not too big, reduces the oscillation number effectively, proposes:
Figure BDA0002606742700000102
that is, the turning radius is defined as a nonlinear function of the lateral deviation, L in the formula (12) is the ship length, n > is 1,
carrying out a comparison experiment on the improved LOS guidance law and the traditional LOS guidance law, and setting n to be 2, L to be 1 and L to be 5;
as shown in FIG. 4, the internal model controller is designed, and the controller G is arranged in the dotted line boxc(s)，GIMC(s) is a transfer function of the transfer function,
Figure BDA0002606742700000103
is an objectModel, R(s) is the input set point, Gp(s) is the control object, D(s) is the disturbance parameter, Y(s) is the system output, then controller Gc(s) can be expressed as:
Figure BDA0002606742700000104
Figure BDA0002606742700000105
Figure BDA0002606742700000106
in the above formula, the first and second carbon atoms are,
Figure BDA0002606742700000107
is that
Figure BDA0002606742700000108
F(s) is a filter, f(s) affects the closed-loop performance and robustness of the system, with a time constant T in the filterfIs a gain parameter;
the control object model can be obtained by the ship mathematical model formula (3):
Figure BDA0002606742700000111
combining equation (14) and equation (15) can be obtained
Figure BDA0002606742700000112
Taking r in equation (17) to be 3, the internal model controller can be expressed as
Figure BDA0002606742700000113
The unmanned ship tracking controller respectively carries out tracking tests on a linear track and a circular track to verify the effectiveness of the path tracking controller. In the simulation process, the tracking effect of the path tracking controller adopting the internal model control structure based on the given parameters is compared with the tracking effect of the self-adaptive path tracking controller provided by the invention, so that the superiority of the controller designed by the invention is shown.
Setting the ship length L to be 1m, and setting an environmental interference weight value alpha according to the initial state of the unmanned ship1＝0.05，α2＝0.1,α3Setting the parameter value of the controller to be K-0.1312, T-2.5501 and T-0.1f5.07. Table 1 below lists the initial parameters in the unmanned ship tracking straight line trajectory and circular trajectory tests.
TABLE 1 initial parameters of unmanned ship in different expected trajectory tests
Desired path Initial position/(m, m) Initial angle/(rad) Speed/(m/s)
Straight line path (1.5，-4) π/3 0.2
Circular path (12,3) π2/3 0.2
(1) Straight line path tracking
The starting position of the desired path is (1,0), the angle is π/3rad, and the optimized controller parameters are shown in FIG. 5 and Table 2 below.
TABLE 2 comparison of controller parameter values for the conventional scheme and the inventive scheme
Control scheme K T Tf
Conventional solutions 0.1312 2.5501 5.07
Scheme of the invention 0.2506 2.1699 6.25
And taking the approximate value of the optimized controller parameter to compare the test data. Compared with the traditional control scheme, the control parameters in the control scheme designed by the invention can be adaptively adjusted along with the change of the motion state of the unmanned ship. When the tracking error of the unmanned ship before 20s is larger, the gain parameter is lower, the gyration index is gradually increased, the system response is faster, and the turning performance is enhanced, so that the unmanned ship is more quickly close to the expected path. As the error gradually decreases and tends to converge, the gain parameter increases to around 6.25 and the followability index tends to stabilize.
As shown in fig. 6 and 7, the path tracking control scheme provided by the invention can enable the unmanned ship to accurately track the expected path, the system response is faster, and the control effect is more stable.
(2) Circular path tracking
The starting position of the desired path is (0, -10) and the angle is π/2 rad. The optimized controller parameters are shown in fig. 8 and table 3 below.
TABLE 3 comparison of controller parameter values while tracing different paths
Desired path K T Tf
Straight line path 0.2506 2.169 6.25
Circular path 0.6562 1.2707 3.25
The gain parameter T is compared to the parameter during tracking of a straight path during tracking of a circlefIs small; the rotation index K is larger, the following index T is smaller, and the result shows that when the path tracking controller tracks different paths, the actual motion control effect can be improved by adjusting parameters.
As shown in fig. 9 and 10, when the path becomes complex, the unmanned ship motion state in the solution of the present application is affected to a certain extent, but can be adjusted in a shorter time than the conventional solution, and finally converges to the desired path, which shows the robustness and stability of the system.
As shown in fig. 11, the flow of the path tracking control scheme of the present invention includes:
step 1: establishing an unmanned ship motion mathematical model;
step 2: initializing the position and posture information of the unmanned ship, and loading expected path information;
and step 3: acquiring the position and posture information of the current unmanned ship;
and 4, step 4: judging whether a track error exists, if so, performing the step 4, and if not, performing the step 8;
and 5: calculating an expected heading angle by utilizing an improved LOS guidance law according to the current position and the expected path information of the unmanned ship; meanwhile, optimizing controller parameters by adopting a genetic algorithm according to the real-time pose state of the unmanned ship;
step 6: outputting the deflection angle of the propeller by using an internal model controller according to the expected heading angle;
and 7: the propeller drives the unmanned ship to track the path according to the deflection instruction;
and 8: judging whether the terminal is reached, and if so, ending the path tracking task; if not, go back to step 3.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (7)

1. An unmanned ship path tracking control method based on internal model control and genetic algorithm is characterized by comprising the following steps:
s1, establishing a state space type unmanned ship linear mathematical model:
Figure FDA0002606742690000011
v, r, ψ is a state variable, where ψ is a heading angle, which is a control input amount, i.e., a propeller deflection angle;
s11, converting the formula (1) into a second-order transfer function model, namely a Nomoto model:
Figure FDA0002606742690000012
Figure FDA0002606742690000013
Figure FDA0002606742690000014
k, T in the formula (2) are all maneuverability indexes of the unmanned ship, which shows the response characteristic of heading change to control and is convenient for analyzing the rule that the unmanned ship moves along with the change of model parameters, K is the static gain from(s) to r(s) and is called as a gyration index, and a time constant T is a following index;
s12, defining the environmental interference as w1、w2、w3Approximately describes the environmental interference, and satisfies:
Figure FDA0002606742690000015
wherein alpha is1、α2And alpha1The weight parameters of the interference of wind, wave and flow on the plane three degrees of freedom respectively represent rand1(t), rand2(t) and rand3(t) as [ -1,1]A uniform random number of;
s13, obtaining a space type unmanned ship motion mathematical model under the interference environment:
Figure FDA0002606742690000021
s2, designing an LOS guidance law, and adaptively adjusting the forward sight distance by establishing a nonlinear relation between the steering radius and the transverse deviation of the unmanned ship so as to improve the tracking effect of the unmanned ship on the expected track;
step S21.(x, y) is the current ship' S position, (x)k,yk) Representing a certain track point on the expected track, wherein the LOS vector points to the LOS coordinate from the real-time coordinate of the unmanned ship, and the included angle between the vector and the expected track is an LOS angle;
s22, taking the current position (x, y) of the unmanned ship as the center of a circle, and taking nL as the radius and the path PkPk-1Intersect at two points and get close to (x)k,yk) The intersection point of (A) is LOS coordinate (x)Los,yLos) Thereby obtaining a LOS vector;
ψLOS＝tan2(ye,Δ),Δ＞0 (7)
ψd＝ψp-ψLOS(8)
in the formula (7), yeIs a lateral deviation, Delta is a forward-looking distance, and in equation (8), phipIs the azimuth angle, ψ, of the desired trackdIs an ideal heading angle;
(xLos,yLos) Is calculated as:
(xLos-x)2+(yLos-y)2＝R2(9)
Figure FDA0002606742690000022
step S23. in order to enable the unmanned ship to be in yeWhen larger, it converges rapidly to the desired trajectory at yeWhen smaller, the desired heading psidThe fluctuation is not too big, reduces the oscillation number (effect) effectively, proposes:
Figure FDA0002606742690000031
that is, the turning radius is defined as a nonlinear function of the lateral deviation, L in the formula (11) is the ship length, n > is 1,
s24, carrying out a contrast experiment on the improved LOS guidance law and the traditional LOS guidance law, and setting n to be 2, L to be 1 and L to be 5;
s3, designing an internal model controller, wherein the internal model controller is used for connecting a control object and an actual object mathematical model in parallel, and a control strategy of controller design is carried out by adopting a phase cancellation method, so that the controller has disturbance resistance and control capability on a large-time-lag system due to the deviation integral action and the added filtering link of the internal model control structure, and the parameters of the controller are clear and visible, thereby being convenient for analyzing and adjusting the performance of the controller;
step S31. controller G is arranged in the dotted line boxc(s)，GIMC(s) is a transfer function of the transfer function,
Figure FDA0002606742690000032
is an object model, R(s) is an input set point, Gp(s) is the control object, D(s) is the disturbance parameter, Y(s) is the system output, then controller Gc(s) can be expressed as:
Figure FDA0002606742690000033
Figure FDA0002606742690000034
Figure FDA0002606742690000035
in the above formula, the first and second carbon atoms are,
Figure FDA0002606742690000036
is that
Figure FDA0002606742690000037
F(s) is a filter, f(s) affects the closed-loop performance and robustness of the system, with a time constant T in the filterfIs a gain parameter;
s32, obtaining a control object model according to a ship mathematical model formula (2):
Figure FDA0002606742690000038
combining equation (13) and equation (14) yields
Figure FDA0002606742690000041
Taking r in the equation (16) to be 3, the internal model controller can be expressed as
Figure FDA0002606742690000042
S4, optimizing controller parameters by adopting a genetic algorithm, and performing global optimization on the control parameters by adopting the genetic algorithm so as to enable the unmanned ship to obtain better dynamic characteristics in the actual motion process;
s41, parameter coding and population initialization;
s42, a target function and a fitness function are obtained;
s43, selecting, crossing and mutating;
and S44, repeating the steps S42 and S43 until the expected effect of convergence or parameter optimization is achieved.
2. The unmanned ship path tracking control method based on internal model control and genetic algorithm as claimed in claim 1, wherein α in step S141≥0，α2≥0，α3≥0。
3. The unmanned ship path tracking control method based on internal model control and genetic algorithm as claimed in claim 1, wherein the selection is made in consideration of expected performance and robust performance of the systemTaking appropriate gain parameter TfEspecially critical, smaller TfThe value can enable the system to obtain faster response capability and better interference suppression capability, and larger TfThe values can make the control system more stable and robust.
4. The method for controlling unmanned ship path tracking based on internal model control and genetic algorithm as claimed in claim 1, wherein the parameters to be adjusted by the controller in step S41 include a maneuverability index K, T and a gain parameter TfPair K, T and T by genetic algorithmfCoding is carried out, and in order to improve the searching speed of the algorithm, the searching space according to each defined parameter is as follows: [0,1]、[0,10]、[0，10]Generating a given controller parameter value according to the initial state of the unmanned ship, generating thirty individuals by the initial population based on the given parameter value reorganization and combining the following formula, evolving fifty generations, and in each optimization next, keeping the optimal individuals to be used for generating the next suboptimal initial population:
Figure FDA0002606742690000051
Piis an individual of the starting population, KjIs a given set of parameter values, rand (t) for generating [ -0.1,0.1]The random number of (2).
5. The unmanned ship path tracking control method based on the internal model control and genetic algorithm as claimed in claim 1, wherein in step S42, the objective function is a performance index for evaluating a system control effect, the fitness function is a measure of the goodness of a population of individuals, the lateral error and the heading error are both actual performances of the unmanned ship control effect, and the following formula is established as the objective function:
Figure FDA0002606742690000052
where T' is the sampling period, #eThe course error is shown, the smaller the objective function value is, the better the system control performance is, the reciprocal of the objective function is selected as the corresponding fitness function according to the maximization principle:
Figure FDA0002606742690000053
。
6. the unmanned ship path tracking control method based on internal model control and genetic algorithm as claimed in claim 1, wherein in step S43, good individuals are selected by roulette selection method according to fitness value of each individual, and next generation individuals are obtained by using arithmetic crossover and uniform variation to generate next generation population, wherein the best good individuals in each generation are directly retained to next generation.
7. The unmanned ship path tracking control method based on internal model control and genetic algorithm as claimed in claim 1, wherein in step S43, the cross probability is set to 0.8, and the mutation probability is set to 0.1.
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