Intelligent path planning and control method for ships sailing in ice areas

Abstract

The present invention discloses a method for intelligent ship path planning and control for ice navigation missions. The expected heading angle of the ship is obtained through the status of threatening floating ice in the ship path planning area, so as to design the ship control law and obtain the actual control input of the ship to complete the ship intelligent path planning and control for ice navigation missions. The present invention aims at the problem of autonomous navigation planning and path tracking control of ships in polar ice areas. Through the floating ice obstacle area planning technology, the obstacle area is reasonably planned for extremely low floating ice of irregular geometry, which plays an important role in subsequent path planning; at the same time, according to the designed control law, the ship can quickly and safely leave the ice area. In the present invention, the large ship itself has the characteristics of large inertia, long time lag, strong coupling, etc., and the control accuracy of path tracking in polar floating ice areas is high.

Description

Technical Field

[0001] The present invention relates to the technical field of ship motion control, and in particular to an intelligent path planning and control method for ships for ice navigation tasks. Background Art

[0002] During polar navigation, large ships usually inevitably enter ice areas. Without the guidance of icebreakers, they are prone to ice entrapment, collisions and other dangers, which may cause irreversible damage to the hull. Therefore, large ships need to consider obstacle avoidance measures for floating ice when autonomously navigating in polar regions. Polar floating ice varies in size. If obstacle avoidance measures are taken for each piece of floating ice, it will lead to additional consumption of time, energy and other costs. In addition, the underwater part of most polar floating ice is much larger than the above-water part. For polar navigation, the complex hydrological conditions increase the requirements for the safety of ship path planning and the accuracy of path tracking control.

[0003] Based on the above analysis, the existing polar autonomous navigation technology has the following three defects:

[0004] With regard to the irregular geometry of polar ice floes, there is no suitable standard to classify the size of the ice floes, and there is no scientific method to plan the obstacle areas, which is not conducive to subsequent path planning.

[0005] Polar ice floes are dense and randomly distributed. Existing path planning algorithms have poor planning effects in areas with high-density obstacles and cannot enable ships to leave the ice area quickly and safely.

[0006] Polar ice floes have high requirements for the control accuracy of path tracking, which requires the actual ship to converge to the reference path with high precision. At the same time, large ships themselves have characteristics such as large inertia, long time lag, and strong coupling. The control algorithm used needs to overcome the above difficulties. Summary of the invention

[0007] The present invention discloses a ship intelligent path planning and control method for ice zone navigation missions to overcome the above technical problems.

[0008] In order to achieve the above object, the technical solution of the present invention is:

[0009] A method for intelligent ship path planning and control for ice navigation missions includes the following steps:

[0010] S1: Establish a three-degree-of-freedom nonlinear mathematical model of the ship;

[0011] S2: Determine the threatening floating ice in the ship path planning area according to the minimum cross-sectional area of ​​the threatening floating ice pyramid at sea level to obtain the expected heading angle of the ship;

[0012] S3: establishing a logical virtual ship to obtain the coordinates of the logical virtual ship according to the expected heading angle of the ship to obtain a guidance law signal;

[0013] S4: Based on the three-degree-of-freedom nonlinear mathematical model of the ship and the coordinate and guidance law signals of the logical virtual ship, the position tracking error and heading angle tracking error of the ship are constructed to obtain the virtual control law corresponding to the position tracking error and the virtual control law corresponding to the heading tracking error; then based on the dynamic surface technology, the first-order filter corresponding to the position tracking error and the first-order filter corresponding to the heading tracking error are obtained;

[0014] S5: constructing the swell acceleration kinematic error and the yaw angular velocity kinematic error according to the first-order filter corresponding to the position tracking error and the first-order filter corresponding to the heading tracking error based on the dynamic surface control technology;

[0015] S6: According to the kinematic error of the longitudinal surge acceleration and the kinematic error of the bow angular velocity, obtain the estimated value of the adaptive parameter corresponding to the kinematic error of the longitudinal surge acceleration, the estimated value of the adaptive parameter corresponding to the kinematic error of the bow angular velocity, the estimated value of the control law corresponding to the kinematic error of the longitudinal surge acceleration, the estimated value of the control law corresponding to the kinematic error of the bow angular velocity, the intermediate control law corresponding to the kinematic error of the longitudinal surge acceleration, and the control law corresponding to the kinematic error of the bow angular velocity, so as to obtain the actual control input of the ship and complete the intelligent path planning and control of the ship for ice navigation missions.

[0016] Beneficial effects: The present invention provides a ship intelligent path planning and control method for ice navigation missions, which obtains the expected heading angle of the ship according to the conditions of threatening floating ice in the ship path planning area, and then obtains the guidance law signal, and constructs a virtual control law corresponding to the position tracking error and a virtual control law corresponding to the heading tracking error. Based on the dynamic surface technology, a first-order filter corresponding to the position tracking error and a first-order filter corresponding to the heading tracking error are obtained, and according to the calculated swell acceleration kinematic error and the bow angular velocity kinematic error, an estimated value of an adaptive parameter corresponding to the swell acceleration kinematic error, an estimated value of an adaptive parameter corresponding to the bow angular velocity kinematic error, an estimated value of a control law corresponding to the swell acceleration kinematic error, an estimated value of a control law corresponding to the swell acceleration kinematic error, an intermediate control law corresponding to the swell acceleration kinematic error, and a control law corresponding to the bow angular velocity kinematic error are obtained, so as to obtain the actual control input of the ship and complete the ship intelligent path planning and control for ice navigation missions. The present invention aims at the problem of autonomous navigation planning and path tracking control of ships in polar ice areas. Through the floating ice obstacle area planning technology, the obstacle area is reasonably planned for extremely low floating ice with irregular geometry, which plays an important role in subsequent path planning. At the same time, according to the designed control law, the ship can quickly and safely leave the ice area. In the present invention, the control accuracy of path tracking in polar floating ice areas is high, considering that large ships themselves have large inertia, long time lag, strong coupling and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 This is a flow chart of the method for intelligent path planning and control of a ship sailing in ice areas according to the present invention;

[0019] Figure 2 This is a schematic diagram of floating ice obstacle area planning in an embodiment of the present invention;

[0020] Figure 3 This is a structural diagram of the large ship autonomous navigation technology in ice areas in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the guidance principle of a virtual ship for autonomous navigation in ice areas in an embodiment of the present invention;

[0022] Figure 5 A schematic diagram of a path planning method with an automatic steering mechanism in an embodiment of the present invention;

[0023] Figure 6 Path planning diagrams under different ice floe densities in an embodiment of the present invention;

[0024] Figure 7 It is an ice area path tracking trajectory diagram under the adaptive iterative learning control in an embodiment of the present invention;

[0025] Figure 8 is a tracking error curve diagram under the adaptive iterative learning control in an embodiment of the present invention;

[0026] Fig. 9 is a graph of actual control input under adaptive iterative learning control in an embodiment of the present invention;

[0027] Fig.10 is a graph of actuator gain estimation under adaptive iterative learning control in an embodiment of the present invention;

[0028] Fig.11 It is a curve diagram of the actual ship trajectory in the comparative experiment in the embodiment of the present invention;

[0029] Fig.12 A tracking error curve diagram of a comparative experiment in an embodiment of the present invention;

[0030] Fig.13 This is a graph of actual control input in a comparative experiment in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] This embodiment introduces a method for intelligent ship path planning and control for ice navigation tasks. Figure 1 As shown,

[0033] S1: Establish a three-degree-of-freedom nonlinear mathematical model of the ship;

[0034] Preferably, the three-degree-of-freedom nonlinear mathematical model of the ship is established as follows:

[0035]

[0036] Where: x k Indicates the horizontal coordinate of the ship's position; y k Indicates the vertical coordinate of the ship's position; u kIndicates the forward speed of the ship; v k represents the lateral speed of the ship; ψ k Indicates the heading angle of the ship; r k Indicates the ship's pitch angular velocity; m u It represents the hydrodynamic additional mass of the ship in the forward degree of freedom; m v Indicates the hydrodynamic additional mass of the ship in the drifting degree of freedom; m r represents the hydrodynamic additional mass of the ship in the pitching degree of freedom; f u (v) represents the nonlinear term of the ship's forward freedom; f v (v) Nonlinear term of the ship in the drift degree of freedom; f r (v) represents the nonlinear term of the ship's pitching degree of freedom; n represents the main engine speed through the steering gear servo system; δ represents the rudder angle through the steering gear servo system; T u (·) represents the actuator gain corresponding to the host speed, F r (·) represents the actuator gain corresponding to the rudder angle; d wu (v) is the disturbance force and torque of the external environment acting on the forward freedom of the ship; wv (v) is the disturbance force and moment of the external environment acting on the ship's drift freedom; d wr (v) is the disturbance force and moment of the external environment acting on the ship's pitching freedom; v = [u k ,v k ,r k ] T Indicates the motion acceleration of the ship in the three degrees of freedom direction; X |u|u The hydrodynamic coefficient related to the longitudinal motion of the ship in the longitudinal freedom degree; X vr represents the hydrodynamic coefficient related to the ship's sway and pitch motion in the longitudinal freedom degree; X vv represents the hydrodynamic coefficient related to the ship's swaying motion in the longitudinal freedom degree; X rr It represents the hydrodynamic coefficient related to the ship's bow rolling motion in the longitudinal freedom degree;

[0037] Y v Y represents the hydrodynamic coefficient related to the first power of the ship's swaying speed in the swaying degree of freedom; |v|v The hydrodynamic coefficient related to the second power of the ship's swaying speed in the swaying degree of freedom; Y r Y represents the hydrodynamic coefficient related to the first power of the ship's bow speed in the swaying degree of freedom; |r|r Y represents the hydrodynamic coefficient related to the second power of the ship's bow speed in the swaying degree of freedom; vvr The hydrodynamic coefficient of the sway degree of freedom is related to the second power of the ship's sway velocity and the first power of the bow pitch velocity; Y vvrThe hydrodynamic coefficients related to the first power of the ship's sway velocity and the second power of the bow pitch velocity in the sway degree of freedom;

[0038] N v N represents the hydrodynamic coefficient related to the first power of the ship's swaying speed in the pitching degree of freedom; |v|v N represents the hydrodynamic coefficient related to the second power of the ship's swaying speed in the pitching degree of freedom; r The hydrodynamic coefficient related to the first power of the ship's bow speed in the bow pitch degree of freedom; N |r|r The hydrodynamic coefficient related to the second power of the ship's bow speed in the bow pitch degree of freedom; N vvr The hydrodynamic coefficient related to the second power of the ship's sway velocity and the first power of the sway motion velocity on the apparent sway degree of freedom; N vrr It represents the hydrodynamic coefficient related to the first power of the ship's sway velocity and the second power of the sway velocity in the yaw degree of freedom; |·| represents the absolute value; represents the first-order derivative; k represents the number of iterations;

[0039] in,

[0040]

[0041]

[0042] Where: t p represents the propeller wake coefficient; ρ s Indicates the density of seawater; D p Indicates the propeller diameter; K T About J p Nonlinear function of J p represents the propeller thrust coefficient; α H represents the wake coefficient around the rudder; λ R Indicates the aspect ratio of the rudder blade; A R represents the rudder blade area; It represents the wake velocity corresponding to the longitudinal motion; represents the wake velocity corresponding to the sway motion; represents the distance between the bow and stern of the ship; δ represents the rudder angle through the steering gear servo system; x R Indicates the size parameter related to the distance from the ship's center of gravity to the rudder blade; x H It indicates the longitudinal distance from the center of action of the lateral force induced by steering to the center of gravity of the ship;

[0043] Specifically, for the actuator gain T of the ship u (·) and F r (·), there exists T u0 ,F r0 ,T uM ,FrM , satisfying 0 <T u0 ≤T u (·)≤T uM ,0 <F r0 ≤F r (·)≤F rM ; Among them, T u0 Indicates T u (·) corresponds to the minimum value; F r0 Indicates F r (·) corresponds to the minimum value; T uM Indicates T u (·) corresponds to the maximum value; F rM Indicates F r (·) corresponds to the maximum value;

[0044] Specifically, the large-scale ship autonomous navigation in polar ice areas of this embodiment is divided into two parts: path planning and path tracking control. The embodiment will be introduced from these two parts respectively.

[0045] S2: Obtain the minimum cross-sectional area of ​​the threatening ice cone at sea level to obtain the expected heading angle ψ of the ship d ;

[0046] Specifically, according to the characteristics of polar ice floes that are small at the top and large at the bottom, a pyramid with an irregular polygonal bottom is used to approximate the irregular geometric ice floes. The minimum cross-sectional area S of the pyramid threatening the ice floes at sea level is calculated. upM To distinguish threatening ice floes from non-threatening ice floes.

[0047] Preferably, the desired heading angle ψ of the ship is obtained d Here’s how:

[0048] S21: Get the minimum cross-sectional area S of the pyramid threatening floating ice at sea level upM as follows:

[0049]

[0050] Where: S upM represents the minimum cross-sectional area of ​​the pyramid threatening floating ice at sea level; ρ sea represents the polar seawater density; ρ ice represents the density of polar ice floes; α represents the geometric coefficient used to describe the size of ice floes; g p represents the polar gravitational acceleration; B represents the ship width; L represents the ship length; d f Indicates the ship's draft; C indicates the ship's vertical coefficient.

[0051] Specifically, the area of ​​the cross section at sea level is greater than or equal to S upM The floating ice is planned as threat floating ice, and the rest of the floating ice is planned as non-threatening floating ice. Figure 2-Figure 5 ;

[0052] S22: The minimum cross-sectional area S of the pyramid threatening the floating ice at sea level upM , determine whether there is threatening ice in the ship's path planning area:

[0053] When there is a cross-sectional area at sea level greater than or equal to S upM When there is floating ice, there is threatening floating ice in the ship path planning area; execute S23;

[0054] When there is no sea level, the cross-sectional area is greater than or equal to S upM When there is floating ice, there is no threatening floating ice in the ship path planning area; execute S24;

[0055] S23: When there is no threatening floating ice in the ship path planning area, the expected heading angle of the ship is:

[0056]

[0057] Where: d represents the desired heading angle of the ship; x t Indicates the x-axis coordinate of the target point; y t Indicates the y-axis coordinate of the target point; x d Indicates the x-axis coordinate of VS; y d Represents the y-axis coordinate of VS;

[0058] S24: When there is threatening floating ice in the ship path planning area, the expected heading angle of the ship ψ d Get as follows:

[0059] Specifically, in this embodiment, threatening floating ice and non-threatening floating ice can also be divided by the danger coefficient β, and its specific construction form is as shown in (5); wherein the danger coefficient of the ship is used to characterize the ice resistance of the ship and is a design parameter of the ship.

[0060]

[0061] Where: m ice Indicates the mass of floating ice; m vessel represents the mass of the ship; β represents the risk factor of floating ice;

[0062] The above-mentioned risk factor β is then used to calculate the minimum cross-sectional area S of the pyramid at sea level for approaching the threatening ice floe. upM . Establish the geometric coefficient α=h / S upM Used to describe the size of each ice floe, where h represents the vertical distance from the vertex of the pyramid used to approximate the irregular geometry of the ice floe to the sea level.

[0063] S241: Constructing the boundary of the barrier area threatening floating ice:

[0064]

[0065] R up =max{y1,y2,...,y j}+d ob

[0066] R down =min{y1,y2,...,y j}+d ob

[0067] R right =max{x1,x2,...,x j}+d ob

[0068] R left =min{x1,x2,...,x j}+d ob (7)

[0069] Where: d ob represents the distance between the rectangular boundary of the pyramid tangent to the sea level and the boundary of the ice obstacle area; B represents the ship width; d f Indicates the ship's draft height; x v represents the x-axis coordinate of the pyramid vertex of the floating ice in three-dimensional space; y v The y-axis coordinate of the pyramid vertex of the floating ice in three-dimensional space; v represents the z-axis coordinate of the pyramid vertex of the floating ice in three-dimensional space; c Indicates the hull and d M The x-axis coordinate of the intersection point of the corresponding edge; y c Indicates the hull and d M The y-axis coordinate of the intersection point of the corresponding edge; d M The maximum value of the distance between the projection point of the pyramid vertex of the floating ice to the sea level and the vertex of the irregular polygonal section of the pyramid at the sea level; R up The first boundary ordinate of the obstacle area threatening floating ice, R down The second boundary ordinate of the obstacle area threatening floating ice, R right The third boundary abscissa of the obstacle area threatening floating ice, R left The fourth boundary abscissa representing the obstacle area threatening floating ice; x j represents the x-axis coordinate of the jth vertex of the irregular polygonal section of the pyramid at sea level; j Represents the y-axis coordinate of the jth vertex of the irregular polygonal section of the pyramid at sea level; j represents the total number of vertices of the irregular polygonal section of the pyramid at sea level.

[0070] Specifically, taking the sea level as the xy-0 plane of the coordinate system, the coordinates of the apex of the ice floe pyramid in three-dimensional space are defined as (x v ,y v ,z v ), the coordinates of the projection point of the pyramid vertex at sea level are (x v ,y v ,0), the distance from the projection point of the iceberg cone vertex to each vertex of the irregular polygonal section of the cone at sea level is d1,d2,...,d j , where j represents the number of vertices of the irregular polygonal facet, d M =max{d1,d2,...,d j} represents the maximum value. Define the hull and d M The corresponding edge intersection coordinates are (x c ,y c ,z c ). In the geodetic coordinate system, a rectangle is used to approximate the irregular polygonal section of the pyramid at sea level. The distance d from the boundary of the rectangle to the boundary of each threatening floating ice obstacle area is ob The construction form is shown in (6), and then d ob The boundary of the obstacle area threatening the floating ice is calculated, and its construction form is shown in (7). j ,y j ) represents the coordinates of each vertex of the irregular polygon of the pyramid at sea level;

[0071] Specifically, based on the classification standard of this embodiment, floating ice is classified, and floating ice that will have an irreversible impact on the hull at the time of collision is planned as threatening floating ice, which requires obstacle avoidance measures, while other small-sized floating ice does not need to be avoided. Specifically, a path planning scheme is designed based on the obstacle area boundary of the threatening floating ice, and the target point coordinates (x t ,y t ). When a ship passes through an ice area in the geodetic coordinate system, if there is no floating ice or only non-threatening floating ice in the bow direction of the current virtual ship VS, the expected heading angle is constructed as shown in (8), and there is no need to avoid obstacles at this time.

[0072]

[0073] S242: Get the coordinates of the intersection of the current heading angle of VS and the obstacle area:

[0074] Specifically, there are four situations when the VS encounters an obstacle area during the process of crossing the ice area, namely, the VS approaches the target point from the southwest to the northeast, the VS approaches the target point from the northwest to the southeast, the VS approaches the target point from the northeast to the southwest, and the VS approaches the target point from the southeast to the northwest. Define the abscissa of the intersection of the current heading angle of the VS and the straight line where the first and second boundaries of the obstacle area are located as x reach and the ordinate of the intersection of the current heading angle of the VS and the straight line where the third and fourth boundaries of the obstacle area are located as y reach . Then x reach and y reach are constructed as shown in (9).

[0075]

[0076] In the formula: x reach is the abscissa of the intersection of the current heading angle of the VS and the straight line where the first and second boundaries of the obstacle area are located; y reach is the ordinate of the intersection of the current heading angle of the VS and the straight line where the third and fourth boundaries of the obstacle area are located; x d represents the x-axis coordinate of the VS; y d represents the y-axis coordinate of the VS; ψ d represents the desired heading angle of the ship;

[0077] Specifically, define the distance from the VS to the first boundary and the second boundary of the obstacle area as dist x , and the distance from the VS to the third boundary and the fourth boundary of the obstacle area as dist y . To achieve threat iceberg avoidance, set a threat distance threshold d.

[0078] To make the form of the algorithm more concise, the above four situations are simplified to two situations where the VS approaches the target point from west to east and from east to west in the geodetic coordinate system. Define the distance from the VS to the northwest corner of the obstacle area as dist1, the distance from the VS to the southeast corner of the obstacle area as dist2, the distance from the VS to the southwest corner of the obstacle area as dist3, and the distance from the VS to the northeast corner of the obstacle area as dist4.

[0079] S243: When the VS approaches the target point from west to east, the desired heading angle of the ship is obtained as follows:

[0080] First, consider the situation where the VS approaches the target point from west to east. When R left < x reach < R right and dist x < d or R down < y reach < R up and dist yWhen <d, the construction form of the desired heading angle of the ship is shown in (12). Among them, when considering the virtual ship approaching the target point from west to east, regardless of whether the virtual ship approaches the target point from north to south or from south to north, the desired heading angle can be obtained through Equation (10).

[0081]

[0082] In the formula: dist1 represents the distance from the VS to the northwest corner of the obstacle area; dist2 represents the distance from the VS to the southeast corner of the obstacle area; dist3 represents the distance from the VS to the southwest corner of the obstacle area; dist4 represents the distance from the VS to the northeast corner of the obstacle area;

[0083] When the VS approaches the target point from east to west, the desired heading angle of the ship is obtained as follows:

[0084] Specifically, considering the situation where the VS approaches the target point from east to west, when R left <x reach <R right and dist x <d or R down <y reach <R up and dist y <d, the construction form of the desired heading angle of the ship is shown in (11). Among them, when considering the virtual ship approaching the target point from east to west, regardless of whether the virtual ship approaches the target point from north to south or from south to north, the desired heading angle can be obtained through (11).

[0085]

[0086] S3: Establish a logical virtual ship, obtain the coordinates of the logical virtual ship, so as to obtain the guidance law signal ψ according to the desired heading angle of the ship r,k ;

[0087] Use the virtual ship (Virtual ship, VS) to connect the path planning and path tracking control parts, that is, first use the path planning technology to generate a reference path, then use the VS to generate a reference signal, and finally use the path tracking control technology to realize ice area navigation. The construction form of the VS is shown in (12).

[0088] Preferably, the method for obtaining the guidance law signal is as follows:

[0089] S31: Obtain the coordinates of the logical virtual ship as follows:

[0090]

[0091] In the formula: x d represents the x-axis coordinate of the VS; y dIndicates the y-axis coordinate of VS; u d represents the expected speed of VS; ψ d represents the desired heading angle of VS.

[0092] S32: Obtain guidance law signal ψ according to the coordinates of the logical virtual ship r,k as follows:

[0093] Specifically, the desired heading angle ψ of VS is obtained using the above path planning algorithm: d , combined with the set virtual ship speed u d Get the coordinates of VS (x d ,y d ), and then obtain the guidance law signal ψ based on the coordinates of VS and RS r,k as follows:

[0094] Specifically, according to the relationship between the current position of the real ship (RS) and the reference position, the guidance law signal ψ is obtained. r,k , and its construction form is shown in (13).

[0095]

[0096] x e,k =x d -x k

[0097] y e,k =y d -y k

[0098] Where: r,k represents the guidance law signal; x e,k represents the lateral position tracking error; y e,k represents the longitudinal position tracking error;

[0099] S4: According to the coordinates of the logical virtual ship and the guidance law signal ψ r,k , construct the ship's position tracking error and heading angle tracking error to obtain the virtual control law α corresponding to the position tracking error u,k The virtual control law corresponding to the heading tracking error α r,k ; Then, based on the dynamic surface technology, the first-order filter β corresponding to the position tracking error is obtained u,k The first-order filter β corresponding to the heading tracking error r,k ,

[0100] Preferably, obtain the first-order filter β u,k and β r,k The method used is as follows:

[0101] Specifically, in order to ensure that the real ship RS trajectory can converge to the reference path with high precision, an adaptive iterative learning control algorithm is introduced. Based on the Backstepping method, the iterative learning control system is designed in two steps. The first step is to design a virtual control law, and the second step is to establish the control law and the intermediate control law based on the adaptive iterative learning control technology, as follows:

[0102] S41: According to the coordinates of the logical virtual ship and the guidance law signal ψ r,k , construct the ship's position tracking error z e,k and heading angle tracking error ψ e,k , as shown in (14):

[0103]

[0104] Where: x e,k represents the lateral position tracking error; y e,k represents the longitudinal position tracking error; z e,k is the ship's position tracking error; ψ e,k is the ship’s heading angle tracking error;

[0105] S42: Based on the position tracking error z of the ship e,k and heading angle tracking error ψ e,k , obtain the virtual control law α corresponding to the position tracking error u,k The virtual control law corresponding to the heading tracking error α r,k ;

[0106] Specifically, in order to stabilize the ship's position tracking error z e,k and heading angle tracking error ψ e,k , the virtual control law is designed based on the Backsteeping method (15):

[0107]

[0108] Where: α u,k represents the virtual control law corresponding to the position tracking error; α r,k represents the virtual control law corresponding to the heading tracking error; represents the positive design parameter of the virtual control law corresponding to the position tracking error; δ xy is a constant set according to control requirements to ensure that RS always follows VS; x d Indicates the x-axis coordinate of VS; y d represents the y-axis coordinate of VS; ψ r,k represents the guidance law signal; represents the first-order derivative; represents the positive design parameter of the virtual control law corresponding to the heading tracking error;

[0109] S43: According to the virtual control law α corresponding to the position tracking error u,k The virtual control law corresponding to the heading tracking error α r,k , obtain the first-order filter β corresponding to the position tracking error based on dynamic surface control technology u,k The first-order filter β corresponding to the heading tracking error r,k ; The formula used is as follows:

[0110] Specifically, in order to avoid the virtual control law α u,k and α r,k Differentiation leads to the so-called "computational explosion" problem [6] , introduces the dynamic surface control (DSC) technology, using the first-order filter β u,k and β r,k Instead of the virtual control law, its specific construction form is as shown in (16).

[0111] β u,k (0) = α u,k (0)

[0112] β r,k (0) = α r,k (0) (16)

[0113] Where: β u,k represents the first-order filter corresponding to the position tracking error; β r,k represents the first-order filter corresponding to the heading tracking error; ∈ u and ∈ r All represent time constants, s represents the Laplace operator; β u,k (0) represents the initial value of the first-order filter corresponding to the position tracking error; α u,k (0) represents the initial value of the virtual control law corresponding to the position tracking error; β r,k (0) represents the initial value of the first-order filter corresponding to the heading tracking error; α r,k (0) represents the initial value of the virtual control law corresponding to the heading tracking error;

[0114] S5: A first-order filter β corresponding to the position tracking error based on dynamic surface control technology u,k The first-order filter β corresponding to the heading tracking error r,k , construct the surge acceleration kinematic error u e,k and the kinematic error of the yaw angular velocity r e,k ;

[0115] Preferably, the kinematic error is constructed as follows:

[0116] Specifically, based on the first-order filter β u,k and β r,k Construct the kinematic error and differentiate it to obtain equation (17).

[0117] u e,k =β u,k -u k

[0118] r e,k =β r,k -r k

[0119]

[0120] Where: u e,k represents the kinematic error of surge acceleration; r e,k represents the kinematic error of the yaw angular velocity; u k Indicates the forward speed of the ship; v k represents the lateral speed of the ship; r k Indicates the ship's pitch angular velocity; m u It represents the hydrodynamic additional mass of the ship in the forward degree of freedom; m v represents the hydrodynamic additional mass of the ship in the drifting degree of freedom; f u (v) represents the nonlinear term of the ship's forward freedom; f r (v) represents the nonlinear term of the ship's pitching degree of freedom; T u (·) represents the actuator gain corresponding to the host speed; F r (·) represents the actuator gain corresponding to the rudder angle; δ represents the rudder angle through the servo system; n represents the main engine speed through the servo system; |n| represents the absolute value of n; d wu (v) represents the disturbance force and torque of the external environment on the forward freedom of the ship; d wr (ν) represents the disturbance force and moment of the external environment acting on the ship's pitching freedom;

[0121] Specifically, from equation (17), it is found that there is an uncertain nonlinear term f in the differential of the kinematic error: u (v) and f r (ν), and the unknown actuator gain T u (·) and F r (·). To address these two problems, the radial basis function (RBF) neural damping technology is first introduced to deal with uncertain nonlinear terms. The construction form of this technology is shown in formula (18):

[0122] f u (v) = S(v)Au ν+ε u (v) = S(v)A u β v -S(v)A u v e +ε u (v)

[0123] f r (v) = S(v)A r v+ε r (v) = S(v)A r β v -S(v)A r v e +ε r (v) (18)

[0124] β v =[β u,k ,v k ,β r,k ] T ,v e =[u e,k ,0,r e,k ] T

[0125] Where: S(v) represents the Gaussian function; A u and A r The average table v = [u k ,v k ,r k ] T Indicates the motion acceleration of the ship in the three degrees of freedom direction; ε u (v) and ε u (v) represents the approximation error; β v represents the matrix of the first-order filter in the three degrees of freedom of the ship; v e The matrix representing the kinematic acceleration error in the three degrees of freedom of the ship; T represents transpose;

[0126] S6: Based on the surge acceleration kinematic error u e,k and the kinematic error of the yaw angular velocity r e,k , obtain the estimated value of the adaptive parameter corresponding to the kinematic error of the longitudinal acceleration, the estimated value of the adaptive parameter corresponding to the kinematic error of the bow acceleration, and the estimated value of the control law corresponding to the kinematic error of the longitudinal acceleration Estimated value of the control law corresponding to the kinematic error of the bow acceleration Intermediate control law corresponding to the kinematic error of surge acceleration Control law corresponding to the kinematic error of yaw angular velocity To obtain the actual control input of the ship and complete the intelligent path planning and control of the ship for ice navigation missions.

[0127] Preferably, the actual control input of the ship is obtained as follows:

[0128] Specifically, in order to deal with the unknown actuator gain problem, an adaptive technique is introduced and the adaptive parameter λ is defined as u =1 / T u (·),λ r =1 / F r (·), and the adaptive parameter λ u and λ r Estimated value of and satisfy in and Represents the estimation error.

[0129] In order to stabilize the kinematic errors, the actual control input (19), control law (20), adaptive law (21), control law estimation term (22) and intermediate control law (23) are designed based on the above results.

[0130]

[0131] Where: N = |n|n represents the actual control input; δ represents the rudder angle through the steering gear servo system; are all estimates of adaptive parameters; α N,k represents the control law corresponding to the kinematic error of the surge acceleration; Represents α N,k Estimation of represents the intermediate control law corresponding to the kinematic error of the surge acceleration; α δ,k represents the control law corresponding to the kinematic error of the yaw angular velocity; Represents α δ,k Estimation of represents the intermediate control law corresponding to the kinematic error of the yaw angular velocity;

[0132]

[0133]

[0134] Where: represents the estimated value of the adaptive parameter of the last iteration corresponding to the surge acceleration kinematic error; represents the estimated value of the control law for the last iteration corresponding to the surge acceleration kinematic error; θ u represents the design parameters of the adaptive law corresponding to the kinematic error of the surge acceleration; represents the control law design parameters corresponding to the surge acceleration kinematic error; It represents the estimated value of the adaptive parameter of the last iteration corresponding to the kinematic error of the yaw acceleration; represents the estimated value of the control law of the last iteration corresponding to the kinematic error of the bow acceleration; θ r represents the design parameters of the adaptive law corresponding to the kinematic error of the bow pitch acceleration; represents the control law design parameters corresponding to the kinematic error of the bow pitch acceleration; represents the design parameters used to stabilize the kinematic errors of surge acceleration; represents the design parameter used to stabilize the kinematic error of bow pitch acceleration; k nu k represents the design parameter corresponding to the nonlinear compensation term in the intermediate control law corresponding to the surge acceleration kinematic error; nr represents the design parameter corresponding to the nonlinear compensation term in the intermediate control law corresponding to the kinematic error of the bow acceleration; Φ u (·) and Φ r (·) all represent neural network damping terms;

[0135] in,

[0136]

[0137] Where: represents the neural network design term corresponding to the surge acceleration kinematic error; S(v) represents the Gaussian function; represents the neural network design term corresponding to the kinematic error of the bow roll acceleration; ||·|| represents the norm; ζ u (v) represents the design compensation term corresponding to the surge acceleration kinematic error designed for stability analysis; ζ r (v) represents the design compensation term corresponding to the yaw acceleration kinematic error designed for stability analysis;

[0138] Among them

[0139] Specifically, in order to make the obstacle function corresponding to the designed control system bounded and the tracking error converge consistently, these design parameters are all positive design parameters. In order to verify the effectiveness and superiority of the large ship polar autonomous navigation strategy of this embodiment, three groups of numerical experiments were set up. Among them, the large ship in this embodiment is a large ship in a broad sense, specifically a ship of ice class PC3 to PC5, and PC refers to the ice zone reinforcement level of the ship. Simulation case A shows the path planning effect under different floating ice densities in the same sea area. Simulation case B is a ship adaptive iterative learning path tracking control experiment. Simulation experiment C compares the algorithm of the present invention with the prior art.

[0140] Simulation Case A:

[0141] 150, 300, 450, 600 pieces of ice of different sizes are randomly generated in a 3000m×3000m sea area, based on a 175m container ship [8] For path planning, assume that the risk factor is 1 / 4 and the geometry factor is 1 / 300. Set the target point (x i ,y i )=(3000m,3000m) and threat distance threshold d=300m, then the path planning effects under four ice floe densities are as follows: Figure 6 As shown, it can be seen that with the increase of floating ice density in the same sea area, the complexity of the reference path also increases, but a suitable path can be planned to enable the ship to leave the ice area safely and quickly.

[0142] Simulation Case B:

[0143] The path planning result in simulation case A is used as the reference path for path tracking control experiments to verify the adaptability of the proposed adaptive iterative learning control algorithm to the high control accuracy requirements of polar autonomous navigation. Figure 7 The path tracking effect under the proposed control algorithm is demonstrated. When the number of iterations k is set to 9, it can be seen that the RS trajectory of the actual ship can converge to the reference path and avoid the threatening ice floes. Figure 8 The tracking error curve under the proposed adaptive iterative learning control is shown, and it can be seen that the position tracking error z e It has an obvious convergence trend, and the heading tracking error oscillates in the neighborhood of 0 during the simulation time. At the same time, with the increase of the number of iterations, the tracking error has an obvious decreasing trend. Fig. 9 The actual control input under the proposed adaptive iterative learning control algorithm is demonstrated. It can be seen that under the action of the steering gear servo system, the main engine speed and rudder angle are limited within a certain range. Fig.10 The gain adaptation curve under adaptive iterative learning control is shown. It can be seen that the adaptive estimate value changes with the change of the unknown actuator gain, and has an obvious downward trend with the increase of the number of iterations.

[0144] Simulation case C: In order to further demonstrate the effectiveness and superiority of the proposed algorithm, the number of iterations k is set to 3, and the proposed algorithm is compared with the robust adaptive control algorithm of the prior art.

[0145] Fig.11 The comparison of RS trajectories under the action of two control algorithms is shown. In the enlarged figure, it can be found that under the action of the proposed algorithm, the RS trajectory converges to the reference path with higher accuracy. Fig.12The tracking error curves under the two control algorithms are shown. The tracking error is an important indicator that can measure the control accuracy of the control system. Through observation, it is found that the tracking error of the algorithm in this embodiment has a smaller jitter amplitude and a higher convergence rate. Fig.13 The actual control input curves under the action of the two control algorithms are shown. It can be seen that the control input oscillation frequency and oscillation amplitude of the algorithm proposed in this embodiment are both small, and it has obvious advantages in energy saving.

[0146] Combining the prior art and numerical experiments, the invention of intelligent ship path planning and control for ice navigation missions in this embodiment has the following obvious advantages:

[0147] In the path planning module, a method for planning obstacle areas suitable for polar ice floes is proposed. The obstacle areas are planned through geometric relationships to achieve obstacle avoidance of the underwater part of the ice floes. In addition, a path planning scheme is proposed based on the above obstacle area planning method. The scheme of this embodiment is simple and efficient, and can ensure that large ships can safely and quickly leave the ice area. In the path tracking control module, in order to meet the high control accuracy requirements of ice floe obstacle avoidance, an adaptive iterative learning control algorithm is proposed. With the increase of the number of iterations, the tracking error gradually decreases. At the same time, the control system has a certain adaptability to the unknown actuator gain. In addition, through comparative experiments, it is found that this embodiment has obvious advantages in energy saving.

[0148] The present embodiment has the following beneficial effects:

[0149] (1) A threat ice classification method is proposed, which divides ice floes of different sizes into threat ice floes and non-threat ice floes through physical and geometric relationships. At the same time, the underwater part of the ice floes is considered for obstacle avoidance, and the obstacle area of ​​the ice floes is planned through geometric relationships to facilitate subsequent path planning.

[0150] (2) Based on the proposed floating ice obstacle area planning scheme, a local ship path planning scheme with an automatic turning mechanism is proposed. The principle of this scheme is simple and can plan a suitable reference path to avoid threatening floating ice, allowing large ships to leave the ice area quickly and safely.

[0151] (3) Considering the high control accuracy requirement for path tracking in the obstacle avoidance mission of large ships in ice areas, an iterative learning control algorithm for autonomous navigation of large ships in ice areas is proposed. The control accuracy improves with the increase of iteration times. By designing the gain adaptive law, the control system's adaptability to unknown actuator gains is improved.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent ship path planning and control for ice navigation missions, characterized in that: The steps include: S1: Establish a three-degree-of-freedom nonlinear mathematical model of the ship; S2: Determine the threatening floating ice in the ship path planning area according to the minimum cross-sectional area of ​​the threatening floating ice pyramid at sea level to obtain the expected heading angle of the ship; S3: establishing a logical virtual ship to obtain the coordinates of the logical virtual ship according to the expected heading angle of the ship to obtain a guidance law signal; S4: According to the three-degree-of-freedom nonlinear mathematical model of the ship and the coordinate and guidance law signals of the logical virtual ship, the position tracking error and the heading angle tracking error of the ship are constructed to obtain the virtual control law corresponding to the position tracking error and the virtual control law corresponding to the heading angle tracking error; then based on the dynamic surface control technology, the first-order filter corresponding to the position tracking error and the first-order filter corresponding to the heading angle tracking error are obtained; S5: constructing the swell acceleration kinematic error and the yaw angular velocity kinematic error according to the first-order filter corresponding to the position tracking error and the first-order filter corresponding to the heading angle tracking error based on the dynamic surface control technology; S6: According to the kinematic error of the longitudinal surge acceleration and the kinematic error of the bow angular velocity, obtain the estimated value of the adaptive parameter corresponding to the kinematic error of the longitudinal surge acceleration, the estimated value of the adaptive parameter corresponding to the kinematic error of the bow angular velocity, the estimated value of the control law corresponding to the kinematic error of the longitudinal surge acceleration, the estimated value of the control law corresponding to the kinematic error of the bow angular velocity, the intermediate control law corresponding to the kinematic error of the longitudinal surge acceleration, and the control law corresponding to the kinematic error of the bow angular velocity, so as to obtain the actual control input of the ship and complete the intelligent path planning and control of the ship for ice navigation missions.

2. The method for intelligent ship path planning and control for ice navigation missions according to claim 1 is characterized in that: The method to obtain the expected heading angle of the ship is as follows: S21: Get the minimum cross-sectional area S of the pyramid threatening floating ice at sea level upM as follows: Where: S upM represents the minimum cross-sectional area of ​​the pyramid threatening floating ice at sea level; ρ sea represents the polar seawater density; ρ ice represents the density of polar ice floes; α represents the geometric coefficient used to describe the size of ice floes; g p represents the polar gravitational acceleration; B represents the ship width; L represents the ship length; d f Indicates the draft height of the ship; C indicates the ship vertical coefficient; β indicates the danger factor of floating ice; S22: The minimum cross-sectional area S of the pyramid threatening the floating ice at sea level upM , determine whether there is threatening ice in the ship's path planning area: When there is a cross-sectional area at sea level greater than or equal to S upM When there is floating ice, there is threatening floating ice in the ship path planning area; execute S23; When there is no sea level, the cross-sectional area is greater than or equal to S upM When there is floating ice, there is no threatening floating ice in the ship path planning area; execute S24; S23: When there is no threatening floating ice in the ship path planning area, the expected heading angle of the ship is: Where: d represents the desired heading angle of the ship; x t Indicates the x-axis coordinate of the target point; y t Indicates the y-axis coordinate of the target point; x d Represents the x-axis coordinate of the logical virtual ship VS; y d Represents the y-axis coordinate of the logical virtual ship VS; S24: When there is threatening floating ice in the ship path planning area, the expected heading angle of the ship ψ d Get as follows: S241: Constructing the boundary of the barrier area threatening floating ice: R up =max{y1,y2,...,y j }+d ob R down =min{y1,y2,...,y j }+d ob R right =max{x1,x2,...,x j }+d ob R left =min{x1,x2,...,x j }+d ob Where: d ob The distance between the rectangular boundary of the pyramid tangent to the sea level and the boundary of the ice obstacle area; x v represents the x-axis coordinate of the vertex of the ice floe pyramid in three-dimensional space; v The y-axis coordinate of the pyramid vertex of the floating ice in three-dimensional space; v represents the z-axis coordinate of the pyramid vertex of the floating ice in three-dimensional space; c Indicates the hull and d M The x-axis coordinate of the intersection point of the corresponding edge; y c Indicates the hull and d M The y-axis coordinate of the corresponding edge intersection point; d M The maximum value of the distance between the projection point of the pyramid vertex of the floating ice to the sea level and the vertex of the irregular polygonal section of the pyramid at the sea level; R up The first boundary ordinate of the obstacle area threatening floating ice, R down The second boundary ordinate of the obstacle area threatening floating ice, R right The third boundary abscissa of the obstacle area threatening floating ice, R left The fourth boundary abscissa representing the obstacle area threatening floating ice; x j represents the x-axis coordinate of the jth vertex of the irregular polygonal section of the pyramid at sea level; y j represents the y-axis coordinate of the jth vertex of the irregular polygonal section of the pyramid at sea level; j represents the total number of vertices of the irregular polygonal section of the pyramid at sea level; S242: Obtain the coordinates of the intersection of the current heading angle of the logical virtual ship and the obstacle area: Where: x reach y is the horizontal coordinate of the intersection of the current heading angle of the logical virtual ship VS and the straight line where the first and second boundaries of the obstacle area are located; reach It is the ordinate of the intersection of the current heading angle of the logical virtual ship VS and the straight line where the third and fourth boundaries of the obstacle area are located; S243: When the logical virtual ship VS approaches the target point from west to east, the expected heading angle of the ship is obtained as follows: Where: dist1 represents the distance from the logical virtual ship VS to the northwest corner of the obstacle area; dist2 represents the distance from the logical virtual ship VS to the southeast corner of the obstacle area; dist3 represents the distance from the logical virtual ship VS to the southwest corner of the obstacle area; dist4 represents the distance from the logical virtual ship VS to the northeast corner of the obstacle area; When the logical virtual ship VS approaches the target point from east to west, the expected heading angle of the ship is obtained as follows:

3. The method for intelligent ship path planning and control for ice navigation missions according to claim 1 is characterized in that: The three-degree-of-freedom nonlinear mathematical model of the ship is established as follows: Where: x k Indicates the horizontal coordinate of the ship's position; y k Indicates the vertical coordinate of the ship's position; u k Indicates the forward speed of the ship; v k represents the lateral speed of the ship; ψ k Indicates the heading angle of the ship; r k Indicates the ship's pitch angular velocity; m u It represents the hydrodynamic additional mass of the ship in the forward degree of freedom; m v Indicates the hydrodynamic additional mass of the ship in the drifting degree of freedom; m r represents the hydrodynamic additional mass of the ship in the pitching degree of freedom; f u (v) represents the nonlinear term of the ship's forward freedom; f v (v) Nonlinear term of the ship in the drift degree of freedom; f r (v) represents the nonlinear term of the ship's pitching degree of freedom; n represents the main engine speed through the steering gear servo system; δ represents the rudder angle through the steering gear servo system; T u (·) represents the actuator gain corresponding to the host speed, F r (·) represents the actuator gain corresponding to the rudder angle; d wu (v) is the disturbance force and torque of the external environment acting on the forward freedom of the ship; wv (v) is the disturbance force and moment of the external environment acting on the ship's drift freedom; d wr (v) is the disturbance force and moment of the external environment acting on the ship's pitching freedom; v = [u k ,v k ,r k ] T Indicates the speed of the ship in the three degrees of freedom; X |u|u The hydrodynamic coefficient related to the longitudinal motion of the ship in the longitudinal freedom degree; X vr represents the hydrodynamic coefficient related to the ship's sway and pitch motion in the longitudinal freedom degree; X vv represents the hydrodynamic coefficient related to the ship's swaying motion in the longitudinal freedom degree; X rr Y represents the hydrodynamic coefficient related to the ship's bow pitch motion in the longitudinal freedom degree; v Y represents the hydrodynamic coefficient related to the first power of the ship's swaying speed in the swaying degree of freedom; |v|v Y represents the hydrodynamic coefficient related to the second power of the ship's swaying speed in the swaying degree of freedom; r Y represents the hydrodynamic coefficient related to the first power of the ship's bow speed in the swaying degree of freedom; |r|r Y represents the hydrodynamic coefficient related to the second power of the ship's bow speed in the swaying degree of freedom; vvr The hydrodynamic coefficient of the sway degree of freedom is related to the second power of the ship's sway velocity and the first power of the bow pitch velocity; Y vrr The hydrodynamic coefficient related to the first power of the ship's sway velocity and the second power of the bow pitch velocity in the sway degree of freedom; N v N represents the hydrodynamic coefficient related to the first power of the ship's swaying speed in the pitching degree of freedom; |v|v N represents the hydrodynamic coefficient related to the second power of the ship's swaying speed in the pitching degree of freedom; r The hydrodynamic coefficient related to the first power of the ship's bow speed in the bow pitch degree of freedom; N |r|r The hydrodynamic coefficient related to the second power of the ship's bow speed in the bow pitch degree of freedom; N vvr The hydrodynamic coefficient related to the second power of the ship's sway velocity and the first power of the sway motion velocity on the apparent swaying degree of freedom; N vrr It represents the hydrodynamic coefficient related to the first power of the ship's sway velocity and the second power of the sway velocity in the yaw degree of freedom; |·| represents the absolute value; represents the first-order derivative; k represents the number of iterations; in, Where: t p represents the propeller wake coefficient; ρ s Indicates the density of seawater; D p Indicates the propeller diameter; K T About J p Nonlinear function of J p represents the propeller thrust coefficient; α H represents the wake coefficient around the rudder; λ R Indicates the aspect ratio of the rudder blade; A R represents the rudder blade area; It represents the wake velocity corresponding to the longitudinal motion; represents the wake velocity corresponding to the sway motion; Indicates the distance between the bow and stern vertical lines of the ship; x R Indicates the size parameter related to the distance from the ship's center of gravity to the rudder blade; x H It represents the longitudinal distance from the center of action of the lateral force induced by steering to the center of gravity of the ship.

4. The method for intelligent ship path planning and control for ice navigation missions according to claim 3 is characterized in that: The method for obtaining the guidance law signal is as follows: S31: Obtain the coordinates of the logical virtual ship as follows: Where: x d Represents the x-axis coordinate of the logical virtual ship VS; y d Indicates the y-axis coordinate of the logical virtual ship VS; u d represents the expected speed of the logical virtual ship VS; ψ d Indicates the expected heading angle of the logical virtual ship VS; S32: According to the coordinates of the logical virtual ship, the guidance law signal is obtained as follows: x e,k =x d -x k and e,k =and d -and k Where: r,k represents the guidance law signal; x e,k represents the lateral position tracking error; y e,k Represents the longitudinal position tracking error.

5. The method for intelligent ship path planning and control for ice navigation missions according to claim 4 is characterized in that: The method used to obtain the first-order filter corresponding to the position tracking error and the first-order filter corresponding to the heading angle tracking error is as follows: S41: According to the coordinates of the logical virtual ship and the guidance law signal, the ship's position tracking error and heading angle tracking error are constructed. Where: x e,k represents the lateral position tracking error; y e,k represents the longitudinal position tracking error; z e,k is the ship's position tracking error; ψ e,k is the ship’s heading angle tracking error; S42: acquiring a virtual control law corresponding to the position tracking error and a virtual control law corresponding to the heading angle tracking error according to the position tracking error and the heading angle tracking error of the ship; Where: α u,k represents the virtual control law corresponding to the position tracking error; α r,k represents the virtual control law corresponding to the heading angle tracking error; represents the positive design parameter of the virtual control law corresponding to the position tracking error; δ xy is a constant set according to control requirements; x d Represents the x-axis coordinate of the logical virtual ship VS; y d Represents the y-axis coordinate of the logical virtual ship VS; represents the positive design parameter of the virtual control law corresponding to the heading angle tracking error; S43: According to the virtual control law corresponding to the position tracking error and the virtual control law corresponding to the heading angle tracking error, a first-order filter corresponding to the position tracking error and a first-order filter corresponding to the heading angle tracking error based on the dynamic surface control technology are obtained; the formula used is as follows: Where: β u,k represents the first-order filter corresponding to the position tracking error; β r,k represents the first-order filter corresponding to the heading angle tracking error; ∈ u and ∈ r All represent time constants, and s represents the Lagrangian operator; β u,k (0) represents the initial value of the first-order filter corresponding to the position tracking error; α u,k (0) represents the initial value of the virtual control law corresponding to the position tracking error; β r,k (0) represents the initial value of the first-order filter corresponding to the heading angle tracking error; α r,k (0) represents the initial value of the virtual control law corresponding to the heading angle tracking error.

6. The method for intelligent ship path planning and control for ice navigation missions according to claim 5 is characterized in that: The kinematic error of surge acceleration and yaw angular velocity is constructed as follows: in e,k =β u,k -in k r e,k ＝β r,k -r k Where: u e,k represents the kinematic error of surge acceleration; r e,k represents the kinematic error of the yaw angular velocity; in, f u (e)=S(v)A u n+e u (n)=S(n)A u b ν -S(n)A u n e +e u (n) f r (v)=S(v)A r v+e r (v)=S(v)A r b v -S(v)A r n e +e r (n) Where: S(v) represents the Gaussian function; ε u (ν) and ε u (v) represents the approximation error; β v represents the matrix of the first-order filter in the three degrees of freedom of the ship; v e The matrix representing the kinematic acceleration errors in the three degrees of freedom of the ship; T represents the transpose.

7. The method for intelligent ship path planning and control for ice navigation missions according to claim 6 is characterized in that: The formula used to obtain the actual control input of the ship is as follows: Where: N represents the actual control input; δ represents the rudder angle through the steering gear servo system; are all estimates of adaptive parameters; α N,k represents the control law corresponding to the kinematic error of the surge acceleration; Represents α N,k Estimated items; represents the intermediate control law corresponding to the kinematic error of the surge acceleration; α δ,k represents the control law corresponding to the kinematic error of the yaw angular velocity; Represents α δ,k Estimation of represents the intermediate control law corresponding to the kinematic error of the yaw angular velocity; Where: represents the estimated value of the adaptive parameter of the last iteration corresponding to the surge acceleration kinematic error; represents the estimated value of the control law for the last iteration corresponding to the surge acceleration kinematic error; θ u represents the design parameters of the adaptive law corresponding to the kinematic error of the surge acceleration; represents the control law design parameters corresponding to the surge acceleration kinematic error; The estimated value of the adaptive parameter of the last iteration corresponding to the yaw angular velocity kinematic error; represents the estimated value of the control law of the last iteration corresponding to the kinematic error of the yaw angular velocity; θ r represents the design parameters of the adaptive law corresponding to the kinematic error of the yaw angular velocity; represents the control law design parameters corresponding to the yaw angular velocity kinematic error; represents the design parameters used to stabilize the kinematic errors of surge acceleration; represents the design parameter used to stabilize the kinematic error of the bow angular velocity; k nu k represents the design parameter corresponding to the nonlinear compensation term in the intermediate control law corresponding to the surge acceleration kinematic error; nr represents the design parameter corresponding to the nonlinear compensation term in the intermediate control law corresponding to the kinematic error of the yaw angular velocity; Φ u (·) and Φ r (·) all represent neural network damping terms; in, Where: The neural network design term corresponding to the kinematic error of the surge acceleration is represented; represents the neural network design term corresponding to the kinematic error of the yaw angular velocity; ||·|| represents the norm; ζ u (v) represents the design compensation term corresponding to the surge acceleration kinematic error designed for stability analysis; ζ r (ν) represents the design compensation term corresponding to the yaw rate kinematic error designed for stability analysis; Among them

Images