Ship trajectory tracking event trigger control method and system with performance game mechanism

Abstract

The embodiment of the invention discloses a ship trajectory tracking event trigger control method and system with a performance game mechanism. The method comprises the following steps: S1, determining a position error and an attitude error corresponding to a ship; s2, designing a first performance index and a first value function; s3, designing a negative gradient estimated value, an optimal virtual control law and an online learning law of the first value function; s4, obtaining a dynamic reference signal and a dynamic control error; s5, defining a second performance index and a corresponding second value function; s6, designing a negative gradient estimated value, an optimal control input and an optimal trigger error corresponding to the second value function; s7, judging whether a triggering rule is met or not; and S8, changing the state of the ship to continuously judge whether the navigation task is completed or not, and if not, re-confirming the current state of the ship and returning to S1. The method solves the problems that in the prior art, it is difficult for an operator to obtain the best triggering effect and control precision, and the robustness of a ship trajectory tracking controller in marine environment interference is poor.

Description

Technical Field

[0001] The present invention relates to the technical field of ship control engineering and ship automated navigation equipment, and in particular to a ship trajectory tracking event triggering control method and system with a performance game mechanism. Background Art

[0002] In the field of ship motion control, the guidance algorithm is one of the important key technologies to solve the ship track keeping control (which can be divided into two categories: indirect track control and direct track control). The existing guidance algorithm is introduced from the field of missile shooting guidance research to the field of ship navigation, which is what we usually call the Line of sight (LOS) algorithm. It solves the problem of using track deviation and heading deviation to calculate the ship's guidance heading in indirect track keeping control, and is also a widely used algorithm in shipborne automatic steering instruments. At the same time, this application will briefly introduce the specific technical features of the LOS algorithm:

[0003] The variable LOS radius and the waypoint switching boundary ring are two important concepts or variables in the LOS algorithm. The purpose of the LOS algorithm is to deduce the heading reference command required to complete the control task in track keeping control, that is, ψ los The existing ship heading keeping controller is used to drive the steering actuator to make the ship heading track the reference command ψ los , reducing track deviation and ultimately enabling the ship to navigate automatically along the planned route. Figure 1 The basic guidance principle diagram of the LOS algorithm is given. In navigation practice, the planned route is usually designed by the pilot by setting waypoints, such as Figure 1 (x i-1 ,y i-1 ),(x i ,y i ) and (x i+1 ,y i+1 ). With the current ship position (x, y) as the center, draw an arc with a LOS radius R. If the LOS radius is selected appropriately, the planned route P can be intersected. i-1 P i At point A B and A F . Starts at the current ship position (x, y) and ends at point A F The line segment is defined as the LOS line, and the corresponding azimuth is ψ los , can be calculated by formula (1) and ψ los ∈<-π,π>. Usually, there are three ways to choose R: 1) Traditional LOS algorithm: R = αL pp , L ppis the length between the two columns of the ship. In this case, α>1 must be required, otherwise the ship will frequently fluctuate around the planned route; 2) Improved LOS algorithm: R=d+L pp , d is the projection distance from the current ship position to the planned course, i.e., the track deviation. This choice can ensure that R>d always holds true and that the LOS line always exists; 3) Exponential convergence LOS algorithm, as shown in formula (2). Where lambertw is the function f(x)=xe x The inverse function of the minimum LOS radius R is selected min =1.7L pp , the coefficient b is the exponential convergence factor, b=0.05.

[0004]

[0005] The LOS guidance algorithm can achieve the navigation guidance of the ship on a straight line and ultimately achieve the track keeping control task. However, when the ship is near a waypoint, that is, Figure 1 Medium i At the position, the waypoint conversion is required. This involves the concept of waypoint switching boundary ring, that is, (x i ,y i ) is centered on δ i A circular area with a radius of . However, when a ship enters this area, the reference route is changed by the route segment {(x i-1 ,y i-1 ), (x i ,y i )}Switch to route segment{(x i ,y i ), (x i+1 ,y i+1 )}, the switching condition is formula (3).

[0006]

[0007] At the same time, in the field of ship motion control, the main design idea of ​​the trajectory tracking control algorithm is to design a virtual control law based on the position attitude error, thereby obtaining the dynamic speed error and further designing the ship controller. In order to solve the problem of ship actuator wear and resist the interference of the marine environment, the existing ship trajectory tracking control technology proposes an event-triggered control algorithm and a disturbance observer.

[0008] Although the event-triggered control algorithm can prevent the controller from generating real-time control commands by reducing the control accuracy and reduce the update frequency of the control input, the adjustment of the event trigger threshold parameters is uncertain and subjective, and it is difficult for the operator to weigh the control accuracy and trigger effect, and there is no standard for judging the control accuracy and trigger effect. In addition, due to the extremely nonlinear and changeable characteristics of the real ocean environment, the disturbance observer is difficult to apply in navigation practice.

[0009] Based on the above analysis, it can be determined that the existing ship trajectory tracking control algorithm has the following two main defects:

[0010] 1) The existing ship trajectory tracking event trigger control algorithm cannot balance the trigger effect and control accuracy. The operator completely relies on trial and error and experience when adjusting the trigger threshold parameters, which easily leads to two extreme situations: good trigger effect and poor control accuracy, or high control accuracy and poor trigger effect. Therefore, the existing ship motion control engineering practice technology makes it difficult for operators to obtain the best trigger effect and control accuracy;

[0011] 2) The existing ship marine environment disturbance observer cannot be directly applied to navigation practice. The main reason is that when facing a complex and changeable marine environment, the disturbance observer often fails to achieve the observation effect, and the ship trajectory tracking controller designed by this method does not have strong robustness, that is, the ship trajectory tracking controller is weak in the robustness of marine environment disturbance. Summary of the invention

[0012] Based on this, in order to solve the shortcomings of the existing technology, a ship trajectory tracking event triggering control method with a performance game mechanism is proposed.

[0013] In order to achieve the above object, the technical solution corresponding to the present invention is:

[0014] A ship trajectory tracking event triggering control method with a performance game mechanism, characterized by comprising:

[0015] S1. Based on the three-degree-of-freedom planar motion model of the ship, determine the corresponding position error and attitude error of the ship;

[0016] S2. Based on the position error and the posture error, design a first performance indicator and a corresponding first value function;

[0017] S3, designing a first negative gradient estimate corresponding to the first value function, determining an optimal virtual control law based on the first negative gradient estimate, and designing an online learning law;

[0018] S4. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain a corresponding dynamic reference signal, and then the dynamic control error is obtained;

[0019] S5. Based on the input event trigger mechanism and the dynamic control error, define a second performance indicator and a corresponding second value function, wherein the second performance indicator is used to describe the relationship between the control input and the trigger error differential zero-sum game;

[0020] S6. Design a second negative gradient estimate, an optimal control input, and an optimal trigger error corresponding to the second value function;

[0021] S7, based on the determined design parameters of S6, determine whether the triggering rules are met. If yes, determine the optimal control input as the estimated control input value corresponding to the next triggering moment and execute S8; otherwise, determine the optimal control input as the estimated control input value corresponding to the current triggering moment and execute S8.

[0022] S8, change the ship status to continue to determine whether the navigation mission is completed. If not completed, reconfirm the current state of the ship and return to S1 to re-determine the corresponding position error and attitude error of the ship.

[0023] Optionally, in one embodiment, the formula of the performance indicator and the corresponding value function in S2 is as shown in the following formula (9) (10);

[0024]

[0025] In the formula, J u1 (z e ,α u ) and J r1 (ψ e ,α r ) are the performance indicators of position error and attitude error respectively; and They are J u1 (z e ,α u ) and J r1 (ψ e ,α r )’s value function; α u and α r are the virtual control laws of u and r respectively; and They are respectively able to make J u1 and J r1 When α reaches the minimum value u and α r , z e and ψ e are the ship position error and attitude error respectively.

[0026] Optionally, in one embodiment, the calculation formula of the first negative gradient estimation value corresponding to the first value function in S3 is shown in the following formula (1-3);

[0027]

[0028] In the formula, They are The negative gradient estimate of ku ,k r are the virtual control parameters for forward and yaw degrees of freedom respectively. are the RBF neural network weight critic estimates of the forward and yaw degrees of freedom, S u1 ,S r1 The weights of the radial basis neural network are The activation function of

[0029] The calculation formulas of the optimal virtual control law estimate and the online learning law are shown in the following formulas (1-4) and (1-5);

[0030]

[0031] In the formula, The optimal virtual control law is An estimated value of are the actor estimates of the radial basis neural network for forward and yaw degrees of freedom, respectively; k uc1 ,k rc1 ,k ua1 ,k ra1 for Online learning rate; and are the critic learning law and actor learning law of the radial basis neural network for forward and yaw degrees of freedom, respectively; I is the unit matrix; σ j1 Online Learning Law A constant greater than 0.

[0032] Optionally, in one embodiment, the S4 includes the following steps:

[0033] S41. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain the corresponding dynamic reference signal. The corresponding formula is shown as follows:

[0034]

[0035] In the formula, ∈ u ,∈ r They are The filter time constant; β u ,β r They are The dynamic reference signal obtained after filtering;

[0036] S42. Based on the three-degree-of-freedom planar motion model of the ship and the dynamic reference signal, define the ship dynamic control error. The corresponding formula is shown as follows:

[0037] u e =βu -u,r e =β r -r

[0038] where u e ,r e They are the forward speed error and bow speed error of dynamic control respectively;

[0039] And the derivative form of the aforementioned ship dynamics control error is obtained, and the corresponding formula is shown as follows:

[0040]

[0041] Optionally, in one embodiment, the formula corresponding to the input event trigger mechanism in S5 is as follows:

[0042]

[0043] In the formula, is the control input after triggering, is the trigger error, t represents the continuous time, for the triggering moment;

[0044] The formulas corresponding to the second performance index and the second value function are as follows:

[0045]

[0046] In the formula, J u2 ,J r2 are the performance indicators of the forward speed and bow speed error of dynamic control, J u2 ,J r2 The value function of u ,γ r are the event trigger threshold parameters for propeller and rudder respectively, is the optimal control input.

[0047] Optionally, in one embodiment, the value function in S6 The second negative gradient estimate of is as follows:

[0048]

[0049] In the formula, The value function The negative gradient estimate, k ue ,k re They are τ u ,τ r The speed control parameters are: Estimates of the extreme sea disturbance effects on the ship's forward and yaw freedom; Estimates of the RBF neural network weights for the dynamics of forward and yaw degrees of freedom, is the critic estimate of the radial basis neural network weights for forward and yaw degrees of freedom; b u ,b r They are respectively the negative gradient estimates of the value function A constant greater than 0 in ;

[0050] The formula corresponding to the optimal control input estimate and the optimal trigger error estimate is shown in the following formula (1-11):

[0051]

[0052] In the formula, are the estimates of the optimal control input and the optimal trigger error, respectively, is the actor estimate of the radial basis neural network weights for forward and yaw degrees of freedom;

[0053] Among them, the online learning law of the actor estimation value of the radial basis neural network weights of the forward and forward pitch freedom and the estimation value of the extreme ocean disturbance effect on the forward and forward pitch freedom of the hull is shown in formulas (1-12) and (1-13).

[0054]

[0055] In the formula, The weights of the radial basis neural network are The critic learning law and the actor learning law, is the learning law of the radial basis neural network weights that reorganizes the dynamic nonlinear terms, Learning laws for extreme ocean disturbances. jc2 ,k ja2 ,Γ j ,Γ wj They are The learning rate, σ j2 Online Learning Law A constant greater than 0 in Wj ,σ wj The online learning law A constant greater than 0 in They are The initial value of .

[0056] Optionally, in one embodiment, the steps corresponding to S1 include:

[0057] S11. Create a three-degree-of-freedom plane motion model for the ship. The corresponding model formula is:

[0058]

[0059] in

[0060]

[0061] In the above formula, x, y, ψ are the x-axis coordinate, y-axis coordinate and heading angle of the ship in the geodetic coordinate system respectively; v = [u, v, r] T is the ship velocity matrix, where u, v, and r are the ship's forward velocity, drift velocity, and bow pitch velocity, respectively; f u (v),f v (v),f r (v) are the dynamic nonlinear terms of the ship's forward, drift and pitching degrees of freedom; m u ,m v ,m r are the displacement of the ship in forward, drifting and pitching freedom, d wu ,d wv ,d wr are the disturbances of the ocean environment on the forward, sideways and bowing motions of the ship; τ u ,τ r The propulsion force provided by the propeller and the turning torque generated by the rudder blade are used as the control input of the ship motion control system; d u1 ,d u2 ,d u3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship’s forward freedom, d v1 ,d v2 ,d v3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's drifting freedom, d r1 ,d r2 ,d r3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's pitching freedom, respectively;

[0062] S12, based on the three-degree-of-freedom planar motion model of the ship, determining the relationship between the ship coordinates and the virtual ship coordinates, and then obtaining a reference heading angle;

[0063] Assume that the reference trajectory of the ship is generated by the real-time planning of the virtual ship and the relationship between the ship coordinates and the virtual ship coordinates is determined as shown in the following formula (1-16):

[0064]

[0065] In the formula, x r ,y r ,ψ r are the x-axis coordinate, y-axis coordinate and heading angle of the virtual ship in the geodetic coordinate system; ur ,r r are the forward speed and bow pitch speed of the virtual ship respectively;

[0066] According to the relationship between the ship coordinates and the virtual ship coordinates, the reference heading angle is obtained, as shown in the following formula (1-17);

[0067]

[0068] In the formula, x e ,y e is the coordinate error of the ship's x-axis and y-axis, ψ d is the reference heading angle;

[0069] S13. Based on the three-degree-of-freedom planar motion model of the ship and the reference heading angle, the position error and attitude error corresponding to the ship are determined. The corresponding calculation formula is shown in the following formula (1-18);

[0070]

[0071] In the formula, z e and ψ e are the ship position error and attitude error respectively.

[0072] In addition, in order to solve the shortcomings of the traditional technology, a ship trajectory tracking event trigger control system for implementing the above-mentioned design scheme is proposed, which is characterized by including:

[0073] A first data acquisition unit, which is used to determine the position error and attitude error corresponding to the ship based on the three-degree-of-freedom planar motion model of the ship;

[0074] A first data design unit, which is used to design a first performance indicator and a corresponding first value function based on the position error and the posture error;

[0075] A second data design unit, which is used to design a first negative gradient estimation value, an optimal virtual control law, and an online learning law corresponding to the first value function, wherein the optimal virtual control law and the online learning law are used to obtain an index value corresponding to a minimum first performance index;

[0076] A second data acquisition unit, which is used to perform order reduction processing on the derivative of the optimal virtual control law based on a dynamic surface technology to obtain a corresponding dynamic reference signal, and then obtain a dynamic control error;

[0077] A third data design unit, which is used to define a second performance indicator and a corresponding second value function based on an input event triggering mechanism and the obtained dynamic control error;

[0078] a fourth data design unit, which is used to design a second negative gradient estimate, an optimal control input, and an optimal trigger error corresponding to the second value function;

[0079] The first data judgment unit is used to judge whether the trigger rule is met based on the design parameters of the fourth data design unit, and if so, execute S8, otherwise, execute S8,

[0080] The second data judgment unit is used to change the ship state to continue to judge whether the navigation mission is completed. If it is not completed, the current state of the ship is reconfirmed and the corresponding position error and attitude error of the ship are re-determined through the first data acquisition unit.

[0081] In addition, in order to solve the shortcomings of the traditional technology, a computer-readable storage medium is also proposed, including computer instructions. When the computer instructions are executed on a computer, the computer executes the method described.

[0082] Implementing the embodiments of the present invention will have the following beneficial effects:

[0083] 1. The present invention solves the problem that the existing event-triggered control method cannot balance the control accuracy and trigger effect and the trigger threshold parameter adjustment is complicated. That is, the present invention establishes a second performance index describing the relationship between the dynamic control error, the continuous control input and the event trigger error based on the differential game and zero-sum game theory, and obtains the event trigger error that can make the control effect the worst, that is, the optimal trigger error, and the continuous control input that can make the control effect the best, that is, the optimal control input, through the minimum-maximum strategy. This design can minimize the wear of the ship's propeller and rudder while ensuring high control accuracy.

[0084] 2. In view of the problem of marine environment interference on ship motion control, the present invention also provides a parameter estimation method for compensating for marine environment interference, that is, the present invention additionally introduces an extreme marine interference estimate in the value function of the dynamic error and designs a learning law based on the dynamic control error and the extreme marine interference estimate to achieve compensation of the ship for the extreme marine environment. The extreme marine interference estimate introduced by this additional design will directly compensate for the control input. When the ship dynamic control error is large, it can greatly increase the control input, accelerate the response of the ship propeller and rudder to the external environment, and achieve rapid stabilization of the control error. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0086] in:

[0087] Figure 1 It is the basic guidance principle diagram of the -LOS algorithm mentioned in the prior art;

[0088] Figure 2 This is a flow chart of the execution of a ship trajectory tracking control algorithm corresponding to an embodiment of the control method of the present invention;

[0089] Figure 3 A wind farm plane view and a wave three-dimensional view under level 3 sea conditions corresponding to an embodiment of the control method of the present invention;

[0090] Figure 4 This is a trajectory tracking result diagram of a ship in level 3 sea conditions corresponding to an embodiment of the control method of the present invention;

[0091] Figure 5 A curve diagram of ship trajectory tracking error corresponding to an embodiment of the control method of the present invention;

[0092] Figure 6 The control input curve diagram of a ship corresponding to the embodiment of the control method of the present invention;

[0093] Figure 7 The event triggering interval diagram corresponding to the embodiment of the control method of the present invention;

[0094] Figure 8 This is a curve diagram of interference estimation corresponding to an extreme marine environment according to an embodiment of the control method of the present invention;

[0095] Fig. 9 The trajectory diagram of the unmanned boat INAC-5 corresponding to the embodiment of the control method of the present invention;

[0096] Fig.10 A state diagram of the unmanned boat passing through each waypoint under the perspective of a fixed aircraft position in an embodiment of the control method of the present invention;

[0097] Fig.11 The trajectory tracking error diagram of the unmanned boat INAC-5 corresponding to the embodiment of the control method of the present invention;

[0098] Fig.12 The control command diagram of the unmanned boat INAC-5 corresponding to the embodiment of the control method of the present invention;

[0099] Fig.13 It is a basic flow chart corresponding to the control method of the present invention. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0101] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the technical field of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. It is understood that the terms "first", "second", etc. used in the present invention can be used to describe various elements in this article, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first element can be referred to as the second element, and similarly, the second element can be the first element. Both the first element and the second element are elements, but they are not the same element.

[0102] In view of the shortcomings of the prior art, in this embodiment, a performance game control method based on the idea of ​​differential game is proposed to realize the ship trajectory tracking task. That is, this method takes into account the two problems in the practice of ship motion control engineering: "it is difficult for operators to obtain the best trigger effect and control accuracy" and "the ship trajectory tracking controller is less robust in the marine environment interference", and proposes a ship trajectory tracking event trigger control method based on differential game to solve the ship trajectory tracking control problem.

[0103] Specifically, Fig.13 As shown, the control method is characterized by comprising the following steps:

[0104] S1. Based on the three-degree-of-freedom planar motion model of the ship, determine the corresponding position error and attitude error of the ship;

[0105] S2. Based on the position error and the attitude error, design a first performance indicator and a corresponding first value function, wherein the first performance indicator is used to describe the performance indicator corresponding to the position error and the attitude error;

[0106] S3, designing a first negative gradient estimate corresponding to the first value function, determining an optimal virtual control law based on the first negative gradient estimate, and designing an online learning law;

[0107] S4. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain a corresponding dynamic reference signal, and then the dynamic control error is obtained;

[0108] S5. Based on the input event trigger mechanism and the dynamic control error, define a second performance indicator and a corresponding second value function, wherein the second performance indicator is used to describe the relationship between the control input and the trigger error differential zero-sum game;

[0109] S6. Design a second negative gradient estimate, an optimal control input, and an optimal trigger error corresponding to the second value function;

[0110] S7: Based on the determined design parameters of S6, determine whether the triggering rules are met. If yes, determine the optimal control input as the next triggering moment. The corresponding control input estimate And execute S8, otherwise, determine the optimal control input as the current triggering moment The corresponding control input estimate and execute S8;

[0111] S8, change the ship status to continue to determine whether the navigation mission is completed. If not completed, reconfirm the current state of the ship and return to S1 to re-determine the corresponding position error and attitude error of the ship.

[0112] In some specific embodiments, the steps corresponding to S1 include:

[0113] S11. Create a three-degree-of-freedom plane motion model for the ship. The corresponding model formula is:

[0114]

[0115] in

[0116]

[0117] Where x, y, ψ are the x-axis coordinate, y-axis coordinate and heading angle of the ship in the geodetic coordinate system respectively; v = [u, v, r] T is the ship velocity matrix, where u, v, and r are the ship's forward velocity, drift velocity, and bow pitch velocity, respectively; f u (v),f v (v),f r (v) are the dynamic nonlinear terms of the ship's forward, drift and pitching degrees of freedom, which can be approximated online using a radial basis function neural network. For a detailed introduction to the principle, please refer to references [1][2]; m u ,m v ,m r are the displacement of the ship in forward, drifting and pitching freedom, d wu,d wv ,d wr are the disturbances of the ocean environment on the forward, sideways and bowing motions of the ship; τ u ,τ r The propulsion force provided by the propeller and the turning torque generated by the rudder blade are used as the control input of the ship motion control system; d u1 ,d u2 ,d u3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship’s forward freedom, d v1 ,d v2 ,d v3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's drifting freedom, d r1 ,d r2 ,d r3 They are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's pitching freedom.

[0118] References mentioned in the previous paragraph: [1] Guoxing Wen, Shuzhi Sam Ge, CLPhilipChen, Fangwen Tu and Shengnan Wang. Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique. IEEE Transactions on Cybernetics, 2019, 49(9): 3420-3431.

[0119] [2]Jiqiang Li, Guoqing Zhang, Cheng Liu and Weidong Zhang. COLREGs-Constrained Adaptive Fuzzy Event-Triggered Control for Underactuated SurfaceVessels With the Actuator Failures. IEEE Transactions on Fuzzy System. 2021, 29(12): 3822-3832.

[0120] S12, based on the three-degree-of-freedom planar motion model of the ship, determining the relationship between the ship coordinates and the virtual ship coordinates, and then obtaining a reference heading angle;

[0121] Assume that the reference trajectory of the ship is generated by the real-time planning of the virtual ship and the relationship between the ship coordinates and the virtual ship coordinates is determined as shown in the following formula (6):

[0122]

[0123] In the formula, x r ,y r ,ψ r are the x-axis coordinate, y-axis coordinate and heading angle of the virtual ship in the geodetic coordinate system; u r ,r r are the forward speed and bow pitch speed of the virtual ship respectively;

[0124] According to the relationship between the ship coordinates and the virtual ship coordinates, the reference heading angle is obtained, as shown in the following formula (7);

[0125]

[0126] In the formula, x e ,y e is the coordinate error of the ship's x-axis and y-axis, ψ d is the reference heading angle;

[0127] S13, based on the ship's three-degree-of-freedom planar motion model (4) and the reference heading angle (7), determining the corresponding position error and attitude error of the ship, the corresponding calculation formula is shown in the following formula (8);

[0128]

[0129] In the formula, z e and ψ e are the ship position error and attitude error respectively.

[0130] In some specific embodiments, the steps corresponding to S2 include:

[0131] S2. Based on the position error and attitude error (see formula (8)), design a first performance indicator and a corresponding first value function, wherein the first performance indicator is used to describe the performance indicator corresponding to the ship position error and attitude error that need to be considered;

[0132] The formula of the first performance indicator and the corresponding first value function is shown in the following formula (9) (10);

[0133]

[0134] In the formula, J u1 (z e ,α u ) and J r1 (ψ e ,α r ) are the performance indicators of position error and attitude error respectively; and They are Ju1 (z e ,α u ) and J r1 (ψ e ,α r )’s value function; α u and α r are the virtual control laws of u and r respectively; and They are respectively able to make J u1 and J r1 When α reaches the minimum value u and α r That is, the optimal virtual control law, t is time, and s is a certain moment.

[0135] In some specific embodiments, the step corresponding to S3 includes: designing a first negative gradient estimate corresponding to the first value function, determining an optimal virtual control law and an online learning law based on the first negative gradient estimate; obtaining a minimum index value of a first performance index through the optimal virtual control law and the online learning law; wherein,

[0136] Consider the value function The first negative gradient estimate is:

[0137]

[0138] In the formula, They are The negative gradient estimate, k u ,k r are the virtual control parameters for forward and yaw degrees of freedom, are the RBF neural network weight critic estimates of the forward and yaw degrees of freedom, S u1 ,S r1 The weights of the radial basis neural network are The activation function of .

[0139] In order to obtain the minimum value of the performance index, the optimal virtual control law estimate shown in formula (12) and the online learning law shown in formula (13) are designed.

[0140]

[0141] In the formula, The optimal virtual control law is The estimated value of are the actor estimates of the radial basis neural network for forward and yaw degrees of freedom, k uc1 ,k rc1 ,k ua1 ,k ra1 for The online learning rate, and are the critic learning law and actor learning law of the radial basis neural network for forward and yaw degrees of freedom, respectively, I is the unit matrix, σ j1 Online Learning Law A constant greater than 0.

[0142] In some specific embodiments, due to the optimal virtual control law The subsequent derivation will cause a large computational load problem, so it is necessary to introduce dynamic surface technology to reduce the order of the derivative of the optimal virtual control law. The specific steps corresponding to S4 include:

[0143] S41. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain the corresponding dynamic reference signal. The corresponding formula is shown as follows:

[0144]

[0145] In the formula, ∈ u ,∈ r They are The filter time constant; β u ,β r They are The dynamic reference signal obtained after filtering;

[0146] S42. Based on the three-degree-of-freedom planar motion model of the ship, equation (4), and the dynamic reference signal, equation (14), define the ship dynamic control error. The corresponding formula is shown in the following equation:

[0147] u e =β u -u,r e =β r -r

[0148] where u e ,r e They are the forward speed error and bow speed error of dynamic control respectively.

[0149] According to formula (4), the derivative form of the dynamic control error is:

[0150]

[0151] In some specific embodiments, in order to reduce actuator wear and avoid unnecessary actuator braking, the following input event trigger mechanism is introduced, and the corresponding formula is shown as follows:

[0152]

[0153] In the formula, is the control input after triggering, is the trigger error, t represents the continuous time, is the triggering moment; by adopting the event triggering mechanism (16), it can be ensured that the control input is within the triggering time period remains unchanged; for the event trigger mechanism, a larger trigger error means a longer trigger period and worse control accuracy, so it is hoped to obtain the optimal trigger error that can balance the control accuracy and trigger period, that is,

[0154] Considering the event trigger mechanism (16) and radial basis neural network, equation (15) can be expressed as:

[0155]

[0156] Where W j ,S j (v) are the weights and activation functions of the radial basis neural network for reorganizing the nonlinear terms of ship dynamics, ε j is the neural network approximation error.

[0157] Considering the zero-sum game relationship between the control input and the trigger error, the second performance index and the second value function of the zero-sum game of the differential of the control input and the trigger error are defined as follows:

[0158]

[0159] In the formula, J u2 ,J r2 are the performance indicators of the forward speed and bow speed error of dynamic control, J u2 ,J r2 The value function of u ,γ r are the event trigger threshold parameters for propeller and rudder respectively, is the optimal control input.

[0160] In some specific embodiments, the steps corresponding to S6 include:

[0161] Consider the value function The second negative gradient estimate is:

[0162]

[0163] In the formula, The value function The negative gradient estimate, k ue ,k re They are τu ,τ r The speed control parameters are: is the estimated value of the extreme sea disturbance effect on the hull forward and bow pitch freedom, Estimates of the RBF neural network weights for the dynamics of forward and yaw degrees of freedom, is the critic estimate of the radial basis neural network weights for forward and yaw degrees of freedom, b u ,b r They are respectively the negative gradient estimates of the value function A constant greater than 0 in .

[0164] The optimal control input estimate and the optimal trigger error estimate are shown in formula (21).

[0165]

[0166] In the formula, are the estimates of the optimal control input and the optimal trigger error, respectively, Actor estimates of the RBF neural network weights for the forward and yaw degrees of freedom.

[0167] The online learning laws of the radial basis neural network weight estimates and extreme ocean disturbance estimates involved are shown in equations (22)(23).

[0168]

[0169] In the formula, The weights of the radial basis neural network are The critic learning law and the actor learning law, is the learning law of the radial basis neural network weights that reorganizes the dynamic nonlinear terms, is the learning law for extreme ocean disturbances, k jc2 ,k ja2 ,Γ j ,Γ wj They are The learning rate, σ j2 Online Learning Law A constant greater than 0 in Wj ,σ wj The online learning law A constant greater than 0 in . , They are The initial value of .

[0170] Based on the same inventive concept, the present invention also proposes a ship trajectory tracking event triggering control system with a performance game mechanism:

[0171] A first data acquisition unit, which is used to determine the position error and attitude error corresponding to the ship based on the three-degree-of-freedom planar motion model of the ship;

[0172] A first data design unit, which is used to design a first performance indicator and a corresponding first value function based on the position error and the posture error;

[0173] A second data design unit, which is used to design a first negative gradient estimation value, an optimal virtual control law and an online learning law corresponding to the first value function;

[0174] A second data acquisition unit, which is used to perform order reduction processing on the derivative of the optimal virtual control law based on a dynamic surface technology to obtain a corresponding dynamic reference signal, and then obtain a dynamic control error;

[0175] A third data design unit, which is used to define a second performance indicator and a corresponding second value function based on an input event triggering mechanism and the obtained dynamic control error;

[0176] a fourth data design unit, which is used to design a second negative gradient estimate, an optimal control input, and an optimal trigger error corresponding to the second value function;

[0177] A first data judgment unit, which is used to judge whether the trigger rule is satisfied based on the design parameters of the fourth data design unit. If yes, the optimal control input is determined to be the estimated control input value corresponding to the next trigger moment, and the second data judgment unit continues to judge whether the navigation task is completed; otherwise, the optimal control input is determined to be the estimated control input value corresponding to the current trigger moment, and the second data judgment unit continues to judge whether the navigation task is completed;

[0178] The second data judgment unit is used to change the ship state to continue to judge whether the navigation mission is completed. If it is not completed, the current state of the ship is reconfirmed and the corresponding position error and attitude error of the ship are re-determined through the first data acquisition unit.

[0179] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium, comprising computer instructions, and when the computer instructions are executed on a computer, the computer executes the method described.

[0180] In order to verify that Figure 2 To verify the effectiveness of the ship trajectory tracking control algorithm proposed by the present invention, the applicant used matlab to conduct computer simulation experiments and compared it with the existing conventional algorithms. At the same time, in order to further verify that the ship trajectory tracking control algorithm proposed by the present invention has the value of algorithm engineering development, the applicant conducted a real ship verification experiment at the Lingshui Wharf of Dalian Maritime University.

[0181] The relevant experimental design process and verification process are as follows:

[0182] Experiment 1:

[0183] Condition setting:

[0184] Set the initial state of the virtual ship to [x r (0),y r (0),ψ r (0),u r (0),r r (0)] = [0m, 0m, 45deg, 3m / s, 0deg / s], the initial state of the ship [x(0), y(0), ψ(0), u(0), v(0), r(0)] = [-20m, 0m, 0deg, 0m / s, 0m / s, 0deg / s], the forward speed and bow speed of the virtual ship are designed and expressed by formula (23), and the corresponding formula is as follows

[0185]

[0186] Experimental purpose: The ship trajectory tracking control algorithm proposed in this invention is used to track the ship position and attitude on a virtual ship, and is compared with the existing control algorithm in the aforementioned document [1] to verify the superiority of the ship trajectory tracking control algorithm proposed in this invention.

[0187] The verification results are as follows:

[0188] Figure 3-Figure 8 They are the simulation results of ship trajectory tracking under level 3 sea conditions simulated on the matlab simulation platform; Figure 3 It is a simulation environment used on the matlab simulation platform, namely the wind field plane view and wave three-dimensional view under level 3 sea conditions. Figure 3 (a) is a plan view of the wind field at a wind direction of 150°, and (b) is a three-dimensional view of wind-generated waves. Figure 4 As for the ship trajectory tracking results, both the ship trajectory tracking control algorithm proposed in the present invention and the algorithm in the literature [1] can achieve ship trajectory tracking, but the ship using the ship trajectory tracking control algorithm proposed in the present invention is closer to the virtual ship and has better trajectory tracking performance; Figure 5 The position error and attitude error diagrams of ship trajectory tracking, where (a) is the position error diagram and (b) is the attitude error diagram. It can be seen that the control error of the ship trajectory tracking control algorithm proposed in the present invention is smaller than that in the literature [1], especially the attitude error is much smaller than that of the algorithm proposed in the literature [1]. Figure 6The graphs of the control inputs provided by the two control algorithms for the ship are as follows: (a) propeller thrust diagram, (b) rudder ship torque diagram; the control input provided by the control algorithm in the literature [1] is a time-varying continuous input, which will continuously send control commands to the propeller and rudder to cause unnecessary wear, while the control input of the control algorithm proposed in the present invention is an intermittent trigger input, which only sends new control commands to the propeller and rudder at the trigger time point, greatly reducing equipment wear; the number of triggers and the trigger interval between two adjacent trigger time points are shown in Figure 2. Figure 7 As shown ( Figure 7 (a) Propeller input trigger effect. (b) Rudder input trigger effect). The propeller control input was triggered 203 times and the rudder control input was triggered 1037 times. For the algorithm in reference [1], the rudder and propeller control inputs were triggered 15,000 times each. Figure 8 Estimation results of the proposed ship trajectory tracking control algorithm for extreme ocean disturbances (a) Estimation of ocean disturbances for forward degrees of freedom Estimated value of ocean disturbance for bow pitch freedom Curve), it can be seen that compared with the forward motion direction of the ship, the ship needs to make more compensation for nonlinearity and interference in the bow motion direction.

[0189] Experiment 2:

[0190] Key points of experimental design:

[0191] In this experiment, the unmanned boat "INAC-5" is used as the experimental object, and the DVS guidance algorithm is used to generate a virtual ship to guide the unmanned boat "INAC-5". The main parameters of the unmanned boat "INAC-5" are shown in Table 1. At the same time, the relevant content of the experimental object (for details, see the reference [3] Guoqing Zhang, Shang Liu, Jiangshuai Huang and Weidong Zhang. Dynamic event-triggered path-following control of underactuated surface vehicle with the experiment verification. IEEE Transactions on Vehicular Technology, 2022, 71 (10): 10415–10425.); the use of the DVS guidance algorithm to generate a virtual ship to guide the unmanned boat "INAC-5" related technology, see the reference [4] Guoqing Zhang and Xianku Zhang. A novel DVS guidance principle and robust adaptive path-following control for underactuated ships using low frequency gain-learning. ISA Transactions, 2015, 56: 75-86.

[0192]

[0193] Experimental parameter selection:

[0194] The following five waypoints are selected in the experiment: 1 (38.8650188°N, 121.5333179°E), W 2 (38.8648882°N, 121.5336210°E), W 3 (38.86551169°N, 121.5341547°E), W 4 (38.8656234°N, 121.5338168°E), W 5(38.8650188°N, 121.5333179°E). The starting position of the unmanned boat is (38.8650175°N, 121.5333118°E); at the same time, in this experiment, the forward speed of the virtual boat is 2m / s. In order to generate control commands for the unmanned boat "INAC-5", the control algorithm proposed in the present invention is embedded into the main processing unit of the personal computer through visual basic 2015, and the personal computer is connected to the shipboard wireless LAN to realize the transmission of control commands to the unmanned boat "INAC-5". In addition, in order to realize the event trigger mechanism, the personal computer sends the same control command within the trigger interval time period, and the shipboard industrial control box and the steering gear server realize the execution of the control command by the propeller and the steering gear. In order to record the experiment, at waypoint W 1 A fixed camera nearby recorded the status of the unmanned boat "INAC-5" as it passed each waypoint.

[0195] Purpose of the experiment: Through this experiment, we hope that the unmanned boat can autonomously navigate along a closed route consisting of 5 waypoints and pass through the 5 waypoints in sequence.

[0196] The verification results are as follows:

[0197] Figure 9-12 is the experimental result of this experiment; Fig. 9 The trajectory results of the unmanned boat "INAC-5" sailing along the route show that the unmanned boat "INAC-5" can autonomously complete the trajectory tracking tasks of the straight and turning paths. The unmanned boat "INAC-5" is at the waypoint W 1 -W 5 The steering state is Fig.10 As shown. Fig.11 It can be seen from the figure (a) position error, (b) attitude error) that the unmanned boat "INAC-5" can stabilize the position error below 3m and the attitude error below ±20° when adopting the control algorithm proposed in the present invention. Fig.12 (In the figure, (a) car clock command, (b) steering command) shows the control commands generated by the control algorithm proposed in the present invention. In this experiment, 53 control commands were generated for the propeller and 587 control commands were generated for the steering gear.

[0198] In summary, combined with the above verification experiments and compared with the existing technology, the implementation of the embodiments of the present invention will have the following beneficial effects:

[0199] (1) The ship trajectory tracking control algorithm / method proposed in the present invention solves the defect that "the event-triggered trajectory tracking control algorithm is difficult to obtain the best triggering effect and control accuracy", realizes the performance game and trade-off between control accuracy and triggering effect, can optimize the ship's automatic driving performance, and has the characteristics of high control accuracy and greenness;

[0200] (2) The ship trajectory tracking control algorithm / method proposed in the present invention involves a parameter estimation and compensation method for marine environmental disturbances, so that the present invention can consider the relationship between marine environmental disturbances and dynamic control errors, and has the ability to resist extreme marine environmental disturbances. The ship path tracking controller designed in this way has strong robustness, which is of great significance to ship engineering with high navigation accuracy requirements.

[0201] (1) At present, the research on event triggering and game control theory is becoming more and more prosperous, but its application in ship control engineering is rare. The ship trajectory tracking control algorithm / method proposed in this invention has been verified by actual ship experiments, which provides the possibility for the application of the results of this invention in the practice of ship control engineering.

[0202] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A ship trajectory tracking event triggering control method with a performance game mechanism, characterized in that: include: S1. Based on the three-degree-of-freedom planar motion model of the ship, determine the corresponding position error and attitude error of the ship; S2. Based on the position error and the attitude error, design a first performance indicator and a corresponding first value function, wherein the first performance indicator is used to describe the performance indicator corresponding to the position error and the attitude error; S3, designing a first negative gradient estimate corresponding to the first value function, determining an optimal virtual control law based on the first negative gradient estimate, and designing an online learning law; S4. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain a corresponding dynamic reference signal, and then the dynamic control error is obtained; S5. Based on the input event trigger mechanism and the dynamic control error, define a second performance indicator and a corresponding second value function, wherein the second performance indicator is used to describe the relationship between the control input and the trigger error differential zero-sum game; S6. Design a second negative gradient estimate corresponding to the second value function, and determine an optimal control input and an optimal trigger error based on the second negative gradient estimate; S7, based on the determined design parameters of S6, determine whether the triggering rules are met. If yes, determine the optimal control input as the estimated control input value corresponding to the next triggering moment and execute S8; otherwise, determine the optimal control input as the estimated control input value corresponding to the current triggering moment and execute S8. S8, change the ship status to continue to determine whether the navigation mission is completed. If not completed, reconfirm the current state of the ship and return to S1 to re-determine the corresponding position error and attitude error of the ship.

2. A ship trajectory tracking event triggering control method with a performance game mechanism according to claim 1, characterized in that: The formula of the first performance indicator and the corresponding first value function in S2 is shown in the following formula (9) (10); In the formula, J u1 (z e ,α u ) and J r1 (ψ e ,α r ) are the performance indicators of position error and attitude error respectively; and They are J u1 (z e ,α u ) and J r1 (ψ e ,α r )’s value function; α u and α r are the virtual control laws of u and r respectively; and They are respectively able to make J u1 and J r1 When α reaches the minimum value u and α r , z e and ψ e are the ship position error and attitude error respectively, t is the time, and s is a certain moment.

3. A ship trajectory tracking event triggering control method with performance game mechanism according to claim 1, characterized in that: The calculation formula of the first negative gradient estimation value corresponding to the first value function in S3 is shown in the following formula (1-3); In the formula, They are The negative gradient estimate, k u ,k r are the virtual control parameters for forward and yaw degrees of freedom, are the RBF neural network weight critic estimates of the forward and yaw degrees of freedom, S u1 ,S r1 The weights of the radial basis neural network are The activation function of The calculation formulas of the optimal virtual control law estimate and the online learning law are shown in the following formulas (1-4) and (1-5); In the formula, The optimal virtual control law is An estimated value of are the actor estimates of the radial basis neural network for forward and yaw degrees of freedom, respectively; k uc1 ,k rc1 ,k ua1 ,k ra1 for Online learning rate; and are the critic learning law and actor learning law of the radial basis neural network for forward and yaw degrees of freedom, respectively; I is the unit matrix; σ j1 For online learning A constant greater than 0.

4. A ship trajectory tracking event triggering control method with performance game mechanism according to claim 1, characterized in that: S4 includes the following steps S41. Based on the dynamic surface technology, the derivative of the optimal virtual control law is reduced to obtain the corresponding dynamic reference signal. The corresponding formula is shown as follows: In the formula, ∈ u ,∈ r They are The filter time constant; β u ,β r They are The dynamic reference signal obtained after filtering; S42. Based on the three-degree-of-freedom planar motion model of the ship and the dynamic reference signal, define the ship dynamic control error. The corresponding formula is shown as follows: you e =b u -u,r e =b r -r where u e ,r e They are the forward speed error and bow speed error of dynamic control respectively; And the derivative form of the aforementioned ship dynamics control error is obtained, and the corresponding formula is shown as follows:

5. The ship trajectory tracking event triggering control method with performance game mechanism according to claim 1 is characterized in that: The formula corresponding to the input event trigger mechanism in S5 is as follows: In the formula, is the control input after triggering, is the trigger error, t represents the continuous time, for the triggering moment; The formulas corresponding to the second performance index and the second value function are as follows: In the formula, J u2 ,J r2 are the performance indicators of the forward speed and bow speed error of dynamic control, J u2 ,J r2 The value function of u ,γ r are the event trigger threshold parameters for propeller and rudder respectively, is the optimal control input.

6. The ship trajectory tracking event triggering control method with performance game mechanism according to claim 1 is characterized in that: The second value function in S6 The second negative gradient estimate of is as follows: In the formula, The value function The negative gradient estimate, k ue ,k re They are τ u ,τ r The speed control parameters, Estimates of the extreme sea disturbance effects on the ship's forward and yaw freedom; Estimates of the RBF neural network weights for the dynamics of forward and yaw degrees of freedom, is the critic estimate of the radial basis neural network weights for forward and yaw degrees of freedom; b u ,b r They are respectively the negative gradient estimates of the value function A constant greater than 0 in ; The formula corresponding to the optimal control input estimate and the optimal trigger error estimate is shown in the following formula (1-11): In the formula, are the estimates of the optimal control input and the optimal trigger error, respectively, are the actor estimates of the RBF neural network weights for the forward and yaw degrees of freedom, Among them, the online learning law of the actor estimation value of the radial basis neural network weights of the forward and forward pitch freedom and the estimation value of the extreme ocean disturbance effect on the forward and forward pitch freedom of the hull is shown in formulas (1-12) and (1-13). In the formula, The weights of the radial basis neural network are The critic learning law and the actor learning law, is the learning law of the radial basis neural network weights that reorganizes the dynamic nonlinear terms, is the learning law for extreme ocean disturbances, k jc2 ,k ja2 ,Γ j ,Γ wj They are The learning rate, σ j2 For online learning A constant greater than 0 in Wj ,σ wj The online learning law A constant greater than 0 in They are The initial value of .

7. A ship trajectory tracking event triggering control method with performance game mechanism according to claim 1, characterized in that: The steps corresponding to S1 include: S11. Create a three-degree-of-freedom plane motion model for the ship. The corresponding model formula is: in In the above formula, x, y, ψ are the x-axis coordinate, y-axis coordinate and heading angle of the ship in the geodetic coordinate system respectively; v = [u, v, r] T is the ship velocity matrix, where u, v, and r are the ship's forward velocity, drift velocity, and bow pitch velocity, respectively; f u (v),f v (v),f r (v) are the dynamic nonlinear terms of the ship's forward, drift and pitching degrees of freedom; m u ,m v ,m r are the displacement of the ship in forward, drifting and pitching freedom, d wu ,d wv ,d wr are the disturbances of the ocean environment on the forward, sideways and bowing motions of the ship; τ u ,τ r The propulsion force provided by the propeller and the turning torque generated by the rudder blade are used as the control input of the ship motion control system; d u1 ,d u2 ,d u3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship’s forward freedom, d v1 ,d v2 ,d v3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's drifting freedom, d r1 ,d r2 ,d r3 are the 1st, 2nd and 3rd order hydrodynamic derivatives of the ship's pitching freedom, respectively; S12, based on the three-degree-of-freedom planar motion model of the ship, determining the relationship between the ship coordinates and the virtual ship coordinates, and then obtaining a reference heading angle; Assume that the reference trajectory of the ship is generated by the real-time planning of the virtual ship and the relationship between the ship coordinates and the virtual ship coordinates is determined as shown in the following formula (1-16): In the formula, x r ,y r ,ψ r are the x-axis coordinate, y-axis coordinate and heading angle of the virtual ship in the geodetic coordinate system; u r ,r r are the forward speed and bow pitch speed of the virtual ship respectively; According to the relationship between the ship coordinates and the virtual ship coordinates, the reference heading angle is obtained, as shown in the following formula (1-17); In the formula, x e ,y e is the coordinate error of the ship's x-axis and y-axis, ψ d is the reference heading angle; S13. Based on the three-degree-of-freedom planar motion model of the ship and the reference heading angle, the position error and attitude error corresponding to the ship are determined. The corresponding calculation formula is shown in the following formula (1-18); In the formula, z e and ψ e are the ship position error and attitude error respectively.

8. A control system for executing the ship trajectory tracking event triggering control method according to any one of claims 1 to 7, characterized in that include: A first data acquisition unit, which is used to determine the position error and attitude error corresponding to the ship based on the three-degree-of-freedom planar motion model of the ship; A first data design unit, which is used to design a first performance indicator and a corresponding first value function based on the position error and the posture error; A second data design unit, which is used to design a first negative gradient estimation value, an optimal virtual control law, and an online learning law corresponding to the first value function, wherein the optimal virtual control law and the online learning law are used to obtain an index value corresponding to a minimum first performance index; A second data acquisition unit, which is used to perform order reduction processing on the derivative of the optimal virtual control law based on a dynamic surface technology to obtain a corresponding dynamic reference signal, and then obtain a dynamic control error; A third data design unit, which is used to define a second performance indicator and a corresponding second value function based on an input event triggering mechanism and the obtained dynamic control error; a fourth data design unit, which is used to design a second negative gradient estimate, an optimal control input, and an optimal trigger error corresponding to the second value function; A first data judgment unit, which is used to judge whether the trigger rule is satisfied based on the design parameters of the fourth data design unit. If yes, the optimal control input is determined to be the estimated control input value corresponding to the next trigger moment, and the second data judgment unit continues to judge whether the navigation task is completed; otherwise, the optimal control input is determined to be the estimated control input value corresponding to the current trigger moment, and the second data judgment unit continues to judge whether the navigation task is completed; The second data judgment unit is used to change the ship state to continue to judge whether the navigation mission is completed. If it is not completed, reconfirm the current state of the ship and re-determine the corresponding position error and attitude error of the ship through the first data acquisition unit.

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