Preset Precision Trajectory Tracking Control Method for Autonomous Navigation Vessels Based on Enhanced Performance

Abstract

An autonomous navigation ship preset accuracy trajectory tracking control method based on enhanced performance belongs to the technical field of ship trajectory tracking control based on the new generation of information technology, including: S1, power system modeling and data presetting; S2, data acquisition and processing; S3, error conversion; S4, design of an autonomous navigation ship preset accuracy trajectory tracking controller based on enhanced performance; S5, control implementation; S6, establishment of a closed-loop trajectory tracking control system for autonomous navigation ships based on enhanced performance. The present invention can effectively cope with the uncertainties of the environment and model during the operation of autonomous navigation ships on the water surface, constrain the trajectory tracking error of autonomous navigation ships within the preset tracking accuracy range, and can further explicitly enhance the tracking performance by adjusting control parameters, ensuring more accurate tracking of the preset trajectory on the premise of ensuring the safe operation of autonomous navigation ships.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory tracking control of autonomous vessels, and more specifically, to a method for preset precision trajectory tracking control of autonomous vessels based on enhanced performance. Background Art

[0002] Autonomous vessels, relying on their characteristics of small size, high speed, low cost, strong flexibility and high autonomy, are widely used in fields such as surveying and mapping, maritime reconnaissance, rescue and military missions. In recent years, autonomous vessels have received extensive attention and rapid development globally. Trajectory tracking of autonomous vessels is one of the core technologies in the field of ship control, which can ensure that the autonomous vessel operates safely along the preset trajectory and autonomously executes various preset tasks under unmanned and unoperated conditions.

[0003] Traditional trajectory tracking control methods for autonomous vessels mainly include PID control, sliding mode control and model predictive control methods, etc. Although the above control methods can ensure the stability of the autonomous vessel control system, when the autonomous vessel performs tasks on the sea surface, it faces complex and variable environmental conditions, external disturbances and various uncertain factors. Therefore, when dealing with the precise trajectory tracking task in a complex environment, the above traditional control methods have certain limitations. In addition, with the development of modern ship control technology and the new generation of information technology, the requirements for the safety and trajectory tracking performance of autonomous vessels are constantly increasing, making the problem of safe and high-precision trajectory tracking control become complex. To address the above challenges, the preset performance control method emerged. By transforming the tracking error through non-linear mapping, the tracking error is restricted within the pre-set boundaries, so as to ensure that the autonomous vessel can operate safely within the preset tracking error range. In practice, considering factors such as the width of the river channel, obstacles around the operating trajectory and other vessels that threaten the safe operation of the autonomous vessel, the preset performance boundary is usually set as the maximum tolerable error that can ensure the safe operation of the autonomous vessel. However, under the preset performance control method, the trajectory tracking performance depends on the size of the preset performance boundary, and it is impossible to explicitly optimize or adjust it by adjusting the parameters of the controller. Although the tracking accuracy can be improved by designing a smaller preset performance boundary, due to inevitable factors such as the complexity of the operating environment of the autonomous vessel, measurement errors and sensor drift, the tracking error exceeds the preset performance boundary, resulting in singularities or uncontrollable problems in the system, bringing potential safety hazards to the operation of the autonomous vessel.

[0004] In summary, considering the actual requirements of the autonomous navigation ship control system, it is necessary to ensure that the operating trajectory of the autonomous navigation ship always remains within the preset safe range. At the same time, the control system should have the ability to further improve the trajectory tracking accuracy through parameter adjustment to cope with the uncertainties in complex environments and ensure the stability and safety of the autonomous navigation ship under variable conditions. Therefore, researching a trajectory tracking control method for autonomous navigation ships with preset tracking accuracy based on enhanced performance has important practical significance and application value. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a trajectory tracking control method for autonomous navigation ships with preset accuracy based on enhanced performance, including the following steps:

[0006] S1, Power system modeling and data presetting: Select a point on the sea surface where the autonomous navigation ship operates as the origin of the inertial reference system, and establish an Earth-fixed coordinate system; take the center of the autonomous navigation ship as the origin of the body coordinate system and establish a body-fixed coordinate system; considering the uncertain non-linear hydrodynamic damping, unmodeled dynamics and unknown bounded environmental disturbances when the autonomous navigation ship operates on the sea surface, establish a second-order dynamic model of the autonomous navigation ship; preset the desired operating trajectory η r (t) and tracking accuracy β(t);

[0007] S2, Data acquisition and processing: Collect the real-time position η(t) and velocity v(t) state information of the autonomous navigation ship at time t; compare the real-time position η(t) collected by the autonomous navigation ship with the preset operating trajectory η r (t) in step S1 to calculate the trajectory tracking error e η (t); take the derivative of the trajectory tracking error e η (t) to calculate the velocity tracking error e v (t);

[0008] S3, Error conversion: According to the preset desired operating trajectory η r (t) and tracking accuracy β(t) of the autonomous navigation ship in step 1, perform a non-linear transformation on the calculated trajectory tracking error e η (t) to obtain the preset performance trajectory tracking error z η (t); calculate the virtual velocity tracking error z v (t) of the autonomous navigation ship according to the real-time velocity v(t) state of the autonomous navigation ship collected in step S2;

[0009] S4, Design of a trajectory tracking controller for autonomous navigation ships with preset accuracy based on enhanced performance: According to the trajectory tracking error e η (t) obtained in step S2 and the preset performance trajectory tracking error z η(t), the unknown nonlinear dynamics in the autonomous ship system are estimated using an RBF neural network; based on the virtual velocity tracking error z of the autonomous ship obtained in step S3 v (t) and the RBF neural network, a disturbance observer is designed to estimate the unknown disturbances suffered by the autonomous ship control system; based on the trajectory tracking error e in step S2 η (t) and the velocity tracking error e v (t), as well as the preset performance trajectory tracking error z in step S3 η (t) and the virtual velocity tracking error z v (t), the trajectory enhanced performance feedback variable ξ η (t) and the velocity enhanced performance feedback variable ξ v (t), the enhanced performance virtual control law α(t) and the preset accuracy trajectory tracking controller τ(t) of the autonomous ship based on enhanced performance are designed;

[0010] S5, Control implementation: According to the second-order dynamics model of the autonomous ship constructed in step 1, the preset accuracy trajectory tracking controller τ(t) of the autonomous ship based on enhanced performance calculated at the current moment in step S4 is applied to the actuator of the autonomous ship;

[0011] S6, Establishment of the closed-loop trajectory tracking control system of the autonomous ship based on enhanced performance: Update the autonomous ship system, and obtain the state information of the autonomous ship at the next moment, establish the closed-loop trajectory tracking control system of the autonomous ship based on enhanced performance, so that the actual running trajectory η(t) of the autonomous ship approaches the preset desired running trajectory η r (t).

[0012] Furthermore, in step S1, the expression of the second-order dynamics model of the autonomous ship is:

[0013]

[0014] In the formula, t represents the current moment; and v(t) = [u(t), v(t), r(t)] T respectively represent the real-time position and velocity vector of the autonomous ship in the earth-fixed coordinate system at time t, where x(t), y(t) and respectively represent the forward displacement, lateral drift displacement and heading angle of the autonomous ship at time t, and u(t), v(t) and r(t) respectively represent the forward velocity, lateral velocity and yaw angle of the autonomous ship at time t; represents the transformation matrix for converting the body-fixed coordinate system of the autonomous ship to the earth-fixed coordinate system at time t;

[0015] and represent the inertia matrix of the autonomous ship, the Coriolis / centripetal matrix of the autonomous ship at time t, and the uncertain nonlinear hydrodynamic damping matrix, respectively, where m 11 , m 22 , m 23 , m 32 and m 33 are positive constants, d 11 (v(t)), d 22 (v(t)), d 23 (v(t)), d 32 (v(t)) and d 33 (v(t)) are nonlinear functions of v(t); Φ(v(t)) represents the unmodeled dynamics in the autonomous ship system at time t; τ(t) represents the preset precision trajectory tracking controller of the autonomous ship based on enhanced performance at time t, i.e., the enhanced performance trajectory tracking controller; ω(t) represents the unknown bounded environmental disturbance function acting on the autonomous ship at time t.

[0016] Furthermore, in step S2, the data acquisition and processing are specifically as follows:

[0017] Let e η (t) = [e η,1 (t), e η,2 (t), e η,3 (t)] T represent the trajectory tracking error of the autonomous ship at time t, and its expression is:

[0018] e η (t) = η(t) - η r (t)

[0019] where η r (t) = [η r,1 (t), η r,2 (t), η r,3 (t)] T ;

[0020] Differentiate the trajectory tracking error e η (t), and let represent the speed tracking error of the autonomous ship at time t, and its expression is:

[0021]

[0022] Furthermore, in step S3, the error conversion is specifically as follows:

[0023] Define the preset performance trajectory tracking error z η (t) = [zη,1 (t), z η,2 (t), z η,3 (t)] T , where

[0024]

[0025] In the formula, i = 1, 2, 3, and the tracking accuracy of the pre-set autonomous vessel at time t is selected as:

[0026]

[0027] where β i 0 , β i ∞ and β i γ are all positive constants, i = 1, 2, 3;

[0028] Let z v (t) = [z v,1 (t), z v,2 (t), z v,3 (t)] T denote the speed tracking error of the autonomous vessel at time t, and its expression is:

[0029] z v (t) = v(t) - α(t)

[0030] In the formula, α(t) represents the virtual control law of the autonomous vessel to be designed.

[0031] Furthermore, in step S4, the trajectory tracking error e η (t) and the preset performance trajectory tracking error z η (t) obtained according to step 3 are used to estimate the unknown non-linear dynamics in the autonomous vessel system, specifically:

[0032] Define θ(t)(R(t)) = M -1 (D(v(t))v(t) + ω(t)) = [θ1(t), θ2(t), θ3(t)] T represent the unknown non-linear dynamics in the system, where R(t) = [η(t) T , v(t) T T , apply the RBF neural network to approximate the unknown function θ i (R(t)), and the value of θ i (t) is re-expressed using the parameters in the RBF neural network:

[0033] ​

[0034] Among them, represents the unknown optimal weight component of the RBF neural network, i = 1, 2, 3; W i (R(t)) represents the Gaussian basis function component of the RBF neural network; δ i (t) represents the optimal estimation error component of the RBF neural network, i = 1, 2, 3.

[0035] Furthermore, in step S4, the disturbance observer is as follows:

[0036]

[0037] In the formula, is a positive constant, W(R(t)) = [W1(R(t)), W2(R(t)), W3(R(t))] T ; represents the estimation of the function ; represents the state variable of the disturbance observer; represents the estimated value of the unknown optimal weight of the RBF neural network, The update law of

[0038]

[0039] In the formula, σ i and Γ i are positive constants to be designed, i = 1, 2, 3.

[0040] Furthermore, in step S4, the designed trajectory reinforcement performance feedback variable ξ η (t) and the speed reinforcement performance feedback variable ξ v (t) are as follows:

[0041] ξ η (t) = z η (t) + e η (t)

[0042] ξ v (t) = z v (t) + e v (t)

[0043] The designed reinforcement performance virtual control law α(t) and the preset precision trajectory tracking controller τ(t) of the autonomous navigation ship based on reinforcement performance are as follows:

[0044]

[0045] In the formula, Among them, Among them, and represent the control gains to be designed. Among them, K 1,1 , K 1,2 , K 1,3 , K 2,1 , K 2,2 , K 2,3 are positive constants.

[0046] Furthermore, in step S6, the closed-loop trajectory tracking control system of the autonomous navigation ship based on enhanced performance includes a software component module, a memory, and a processor;

[0047] The software component module includes: a power system modeling and data preset module applied to step S1, a data acquisition and processing module applied to step S2, an error conversion module applied to step S3, a preset accuracy trajectory tracking controller design module for the autonomous navigation ship based on enhanced performance applied to step S4, and a control implementation module applied to step S5;

[0048] The memory includes the instruction codes and data of the software component module. When the instruction codes and data of the software component module are executed by the processor, steps S1 - S5 are implemented.

[0049] The beneficial effects of the present invention are as follows:

[0050] The present invention can effectively cope with the uncertainties of the environment and model during the operation of the autonomous navigation ship on the water surface, constrain the trajectory tracking error of the autonomous navigation ship within the pre-set tracking accuracy range, and can further explicitly enhance the tracking performance by adjusting the control parameters, ensuring more accurate tracking of the pre-set trajectory on the premise of ensuring the safe operation of the autonomous navigation ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of the preset accuracy trajectory tracking control method for the autonomous navigation ship based on enhanced performance of the present invention;

[0052] Figure 2 is a schematic diagram of the software component module of the closed-loop trajectory tracking control system of the autonomous navigation ship based on enhanced performance of the present invention;

[0053] Figure 3 is a schematic diagram of the preset tracking trajectory and the actual operation trajectory of the autonomous navigation ship in the embodiment of the present invention;

[0054] Figures 4 - 6This is a schematic diagram of the trajectory tracking error of an autonomous sailing vessel controlled by the preset tracking accuracy trajectory tracking control method and the preset performance control method based on enhanced performance in the embodiments of the present invention. Detailed implementation manners

[0055] In order to be able to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0056] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0057] As Figure 1 shown, the preset accuracy trajectory tracking control method for an autonomous sailing vessel based on enhanced performance includes:

[0058] In step S1, a point on the sea surface where the autonomous sailing vessel operates is selected as the origin of the inertial reference system. The direction pointing due north from the origin is used as the positive direction of the longitudinal axis of the coordinate system, and the due east direction is used as the positive direction of the transverse axis of the coordinate system to establish a earth-fixed coordinate system;

[0059] Taking the center of the autonomous sailing vessel as the origin of the body-fixed coordinate system, the starboard side of the autonomous sailing vessel as the positive direction of the transverse axis of the coordinate system, and the bow of the autonomous sailing vessel as the positive direction of the longitudinal axis of the coordinate system to establish a body-fixed coordinate system;

[0060] In the body-fixed coordinate system, a second-order dynamic model of the autonomous sailing vessel is established. Based on the transformation matrix the operation of the autonomous sailing vessel in the body-fixed coordinate system is transformed into the earth-fixed coordinate system. The expression of the second-order dynamic model of the autonomous sailing vessel is:

[0061]

[0062] In the formula, t represents the current moment; and v(t) = [u(t), v(t), r(t)] T respectively represent the real-time position and velocity vector of the autonomous sailing vessel in the earth-fixed coordinate system at time t. Among them, x(t), y(t) and respectively represent the forward displacement, lateral drift displacement and heading angle of the autonomous sailing vessel at time t, and u(t), v(t) and r(t) respectively represent the forward velocity, lateral velocity and yaw angle of the autonomous sailing vessel at time t; represents the transformation matrix for transforming the body-fixed coordinate system of the autonomous sailing vessel to the earth-fixed coordinate system at time t;

[0063] and represent the inertia matrix of the autonomous ship, the Coriolis / centripetal matrix of the autonomous ship at time t, and the uncertain nonlinear hydrodynamic damping matrix respectively, where m 11 , m 22 , m 23 , m 32 and m 33 are positive constants, d 11 (v(t)), d 22 (v(t)), d 23 (v(t)), d 32 (v(t)) and d 33 (v(t)) are nonlinear functions of v(t); Φ(v(t)) represents the unmodeled dynamics in the autonomous ship system at time t; τ(t) represents the control input torque of the autonomous ship at time t, that is, the enhanced performance trajectory tracking controller; ω(t) represents the unknown bounded environmental disturbance acting on the autonomous ship at time t;

[0064] Preset the desired operating trajectory η r (t) = [η r,1 (t), η r,2 (t), η r,3 (t)] T , so that the autonomous ship can operate along the desired trajectory η r (t) through the designed autonomous ship preset precision trajectory tracking controller τ(t) based on enhanced performance; preset the autonomous ship trajectory tracking accuracy β(t), so that the tracking error of the autonomous ship is limited within the preset trajectory tracking accuracy to ensure the safe operation of the autonomous ship.

[0065] In step S2, collect the real-time position η(t) and velocity v(t) state information of the autonomous ship at time t through sensors and other devices; compare the real-time operating trajectory η(t) collected by the autonomous ship with the preset operating trajectory η r (t) in step 1, and let e η (t) = [e η,1 (t), e η,2 (t), e η,3 (t)] T represent the trajectory tracking error of the autonomous ship at time t, and its expression is:

[0066] e η (t) = η(t) - η r (t)

[0067] For the trajectory tracking error eη (t) is differentiated, and let denote the speed tracking error of the autonomous navigation vessel at time t, and its expression is:

[0068]

[0069] In step S3, according to the preset desired running trajectory η r (t) of the autonomous navigation vessel and the tracking accuracy β(t), the calculated trajectory tracking error e η (t) is nonlinearly transformed, and the preset performance trajectory tracking error z η (t) of the autonomous navigation vessel at time t is defined as: η,1 (t), z η,2 (t), z η,3 (t) T , which is:

[0070]

[0071] In the formula, i = 1, 2, 3, and the tracking accuracy of the autonomous navigation vessel at time t set in advance is selected as:

[0072]

[0073] where β i 0 , β i ∞ and β i γ are all positive constants, and i = 1, 2, 3.

[0074] According to the real-time speed v(t) state of the autonomous navigation vessel at time t collected in step S2, the speed tracking error z v (t) of the autonomous navigation vessel at time t is defined as: v,1 (t), z v,2 (t), z v,3 (t) T :

[0075] z v (t) = v(t) - α(t)

[0076] In the formula, α(t) represents the virtual control law of the autonomous navigation vessel to be designed.

[0077] In step S4, according to the trajectory tracking error e η (t) of the autonomous navigation vessel obtained in step S2 and the preset performance trajectory tracking error z η (t) obtained in step S3, θ(t)(R(t)) = M -1(D(v(t))v(t) + ω(t)) = [θ1(t), θ2(t), θ3(t)] T represents the unknown non - linear dynamics in the system, where R(t) = [η(t) T , v(t) T T , apply the RBF neural network to approximate the unknown function θ i (R(t)), and the value of θ i (t) is re - expressed using the parameters in the RBF neural network:

[0078]

[0079] where, represents the unknown optimal weight component of the RBF neural network, i = 1, 2, 3; W i (R(t)) represents the Gaussian basis function component of the RBF neural network; δ i (t) represents the optimal estimation error component of the RBF neural network, i = 1, 2, 3;

[0080] To estimate the unknown disturbance suffered by the autonomous navigation ship control system, based on the velocity tracking error z v (t) of the autonomous navigation ship obtained in step S3 and the RBF neural network, design the disturbance observer as follows:

[0081]

[0082] In the formula, is a positive constant, W(R(t)) = [W1R(t)), W2R(t)), W3(R(t))] T ; represents the estimation of the function ; represents the state variable of the disturbance observer; represents the estimated value of the unknown optimal weight of the RBF neural network, the update law of

[0083]

[0084] In the formula, σ i and Γ i are positive constants to be designed, i = 1, 2, 3;

[0085] In order to make the trajectory tracking error of the autonomous navigation ship not only be constrained within the pre - set tracking accuracy range, but also be further improved by adjusting the control parameters, based on the trajectory tracking error e η (t) and the velocity tracking error e​v (t), and the preset performance trajectory tracking error z η (t) and the virtual velocity tracking error z v (t), design the trajectory enhanced performance feedback variable ξ η (t) and the velocity enhanced performance feedback variable ξ v (t) are as follows:

[0086] ξ η (t) = z η (t) + e η (t)

[0087] ξ v (t) = z v (t) + e v (t)

[0088] Design the enhanced performance virtual control law α(t) and the preset precision trajectory tracking controller τ(t) for the autonomous navigation ship based on enhanced performance as follows:

[0089]

[0090] In the formula, where, where, and represent the control gains to be designed, where, K 1,1 , K 1,2 , K 1,3 , K 2,1 , K 2,2 , K 2,3 are positive constants.

[0091] In step S5, according to the second-order dynamic model of the autonomous navigation ship established in step S1, apply the preset precision trajectory tracking controller τ(t) of the autonomous navigation ship based on enhanced performance calculated at the current moment in step S4 to the actuator of the autonomous navigation ship; in step S6, update the autonomous navigation ship system, and obtain the state information of the autonomous navigation ship at the next moment, and establish a closed-loop trajectory tracking control system for the autonomous navigation ship based on enhanced performance, so that the actual running trajectory η(t) of the autonomous navigation ship approaches the preset desired running trajectory η r (t), and repeat steps 2 - 5.

[0092] Among them, the closed-loop trajectory tracking control system for the autonomous navigation ship based on enhanced performance includes a software component module, a memory, and a processor;

[0093] Such as Figure 2As shown, the software component modules include: The power system modeling, data virtual control law, and controller applied to step S1 are designed as follows:

[0094]

[0095] Verified through simulation experiments, Figure 3 The preset tracking trajectory and the actual running trajectory of the autonomous ship are given. It can be seen that the preset precision trajectory tracking control method for the autonomous ship based on enhanced performance has a better tracking effect. The trajectory tracking errors of the autonomous ship controlled by the preset precision trajectory tracking control method for the autonomous ship based on enhanced performance and the preset performance control method are as Figures 4 - 6 shown; as can be seen from Figures 4 - 6 it, in the simulation, the trajectory tracking errors of the autonomous ship can be limited within the preset range by both control methods. However, the preset precision trajectory tracking control method for the autonomous ship based on enhanced performance has a faster convergence speed and smaller trajectory tracking errors.

[0096] In summary, the embodiments of the present application can achieve that the tracking accuracy of the autonomous ship can be preset, and can further improve the tracking accuracy of the autonomous ship.

[0097] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An autonomous navigation ship preset precision trajectory tracking control method based on enhanced performance, characterized in that, It includes the following steps: S1, Power System Modeling and Data Presetting: Select a point on the sea surface where the autonomous ship sails as the origin of the inertial reference system, and establish an Earth-fixed coordinate system; take the center of the autonomous ship as the origin of the hull coordinate system and establish a body-fixed coordinate system; consider the uncertain non-linear hydrodynamic damping, unmodeled dynamics and unknown bounded environmental disturbances when the autonomous ship sails on the sea surface, and establish a second-order dynamic model of the autonomous ship; preset the desired running trajectory η r (t) and tracking accuracy β(t); S2, Data acquisition and processing: Collect the real-time position η(t) and velocity v(t) state information of the autonomous sailing vessel at time t; Compare the real-time position η(t) collected by the autonomous sailing vessel with the preset running trajectory η r (t) in step S1, and calculate the trajectory tracking error e η (t); Differentiate the trajectory tracking error e η (t) to calculate the velocity tracking error e v (t); S3, Error conversion: According to the preset desired trajectory η r (t) of the autonomous vessel and the tracking accuracy β(t) in step 1, perform a non-linear transformation on the calculated trajectory tracking error e η (t) to obtain the preset performance trajectory tracking error z η (t); According to the real-time speed v(t) state of the autonomous vessel collected in step S2 at time t, calculate the virtual speed tracking error z v (t); S4. Design of a Preset Precision Trajectory Tracking Controller for an Autonomous Sailing Vessel Based on Enhanced Performance: Based on the trajectory tracking error e η (t) obtained in step S2 and the preset performance trajectory tracking error z η (t) obtained in step S3, use an RBF neural network to estimate the unknown nonlinear dynamics in the autonomous sailing vessel system; The virtual speed tracking error z of the autonomous vessel obtained according to step S3 v (t) and the RBF neural network are used to design a disturbance observer to estimate the unknown disturbance suffered by the control system of the autonomous vessel; Based on the trajectory tracking error e η (t) and the speed tracking error e v (t), as well as the preset performance trajectory tracking error z η (t) and the virtual speed tracking error z v (t), design the trajectory enhanced performance feedback variable ξ η (t) and the speed enhanced performance feedback variable ξ v (t), design the enhanced performance virtual control law α(t) and the preset accuracy trajectory tracking controller τ(t) of the autonomous vessel based on enhanced performance; S5, Control implementation: According to the second-order dynamic model of the autonomous ship constructed in step 1, apply the preset accuracy trajectory tracking controller τ(t) of the autonomous ship based on enhanced performance calculated at the current moment in step S4 to the actuator of the autonomous ship. S6. Establishment of a closed-loop trajectory tracking control system for an autonomous sailing vessel based on enhanced performance: Update the autonomous sailing vessel system and obtain the state information of the autonomous sailing vessel at the next moment. Establish a closed-loop trajectory tracking control system for the autonomous sailing vessel based on enhanced performance, so that the actual operating trajectory η(t) of the autonomous sailing vessel approaches the expected operating trajectory η r (t) preset in step 1.

2. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 1, characterized in that In step S1, the expression of the second-order dynamic model of the autonomous ship is: where \(t\) represents the current moment; and \(v(t)=[u(t),\upsilon(t),r(t)]\) T respectively represent the real-time position and velocity vector of the autonomous ship in the Earth-fixed coordinate system at time \(t\), where \(x(t), y(t)\) and respectively represent the forward displacement, cross-track displacement and heading angle of the autonomous ship at time \(t\), and \(u(t), v(t)\) and \(r(t)\) respectively represent the forward velocity, lateral velocity and yaw rate of the autonomous ship at time \(t\); represents the transformation matrix for transforming the body-fixed coordinate system of the autonomous ship to the Earth-fixed coordinate system at time \(t\); and represent the inertia matrix of the autonomous ship, the Coriolis / centripetal matrix of the autonomous ship at time t, and the uncertain nonlinear hydrodynamic damping matrix, respectively, where m 11 , m 22 , m 23 , m 32 and m 33 are positive constants, d 11 (v(t)), d 22 (v(t)), d 23 (v(t)), d 32 (v(t)) and d 33 (v(t)) are nonlinear functions of v(t); Φ(v(t)) represents the unmodeled dynamics in the autonomous ship system at time t; τ(t) represents the preset precision trajectory tracking controller of the autonomous ship based on enhanced performance at time t; ω(t) represents the unknown bounded environmental disturbance function acting on the autonomous ship at time t.

3. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 2, wherein In step S2, the data acquisition and processing are specifically as follows: Let e η (t) = [e η,1 (t), e η,2 (t), e η,3 (t)] T denote the trajectory tracking error of the autonomous navigation ship at time t, and its expression is: e η (t) = η(t) - η r (t) where η r (t) = [η r,1 (t), η r,2 (t), η r,3 (t)] T ; Derive the trajectory tracking error e η (t), and let represent the speed tracking error of the autonomous ship at time t, and its expression is:

4. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 3, characterized in that, In step S3, the error conversion is specifically as follows: Define the preset performance trajectory tracking error z of the autonomous vessel at time t η (t)=[z η,1 (t),z η,2 (t),z η,3 (t)] T , where, where i = 1, 2, 3, and the tracking accuracy of the preset autonomous ship at time t is selected as: where β i 0 , β i ∞ and β i γ are all positive constants, and i = 1, 2, 3; Let \(z\) v (t)=[z v,1 (t), z v,2 (t), z v,3 (t)] T represent the speed tracking error of the autonomous ship at time \(t\), and its expression is: z v (t) = v(t) - α(t) where α(t) represents the virtual control law of the autonomous ship to be designed.

5. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 4, wherein In step S4, the trajectory tracking error e η (t) obtained according to step 3 and the preset performance trajectory tracking error z η (t) are used to estimate the unknown nonlinear dynamics in the autonomous navigation ship system, specifically: Define θ(t)(R(t)) = M -1 (D(v(t))v(t) + ω(t)) = [θ1(t), θ2(t), θ3(t)] T represents the unknown nonlinear dynamics in the system, where R(t) = [η(t) T , v(t) T T , apply the RBF neural network to approximate the unknown function θ i (R(t)), and the value of θ i (t) is re-expressed using the parameters in the RBF neural network:​ Among them, represents the unknown optimal weight component of the RBF neural network, where i = 1, 2, 3; W i (R(t)) represents the Gaussian basis function component of the RBF neural network; δ i (t) represents the optimal estimation error component of the RBF neural network, where i = 1, 2, 3.

6. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 5, characterized in that, In step S4, the disturbance observer is as follows: wherein is a positive constant, and W(R(t)) = [W1(R(t)), W2(R(t)), W3(R(t))] T ; denotes the estimation of the function ; denotes the state variable of the disturbance observer; denotes the estimation value of the unknown optimal weight of the neural network, and the update law of is as follows:​ where σ i and Γ i are positive constants to be designed, and i = 1, 2, 3.

7. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to claim 6, wherein In step S4, the design trajectory enhanced performance feedback variable ξ η (t) and the velocity enhanced performance feedback variable ξ v (t) are as follows: ξ η z(t) = η + e(t) η (t) ξ v z(t) = v z(t) + e v (t) The design of the virtual control law α(t) with enhanced performance and the preset accuracy trajectory tracking controller τ(t) of the autonomous ship based on enhanced performance are as follows: In the formula, wherein, wherein, and represent the control gains to be designed, wherein, K 1,1 , K 1,2 , K 1,3 , K 2,1 , K 2,2 , K 2,3 are positive constants.

8. The preset precision trajectory tracking control method for an autonomous navigation ship based on enhanced performance according to any one of claims 1-7, characterized in that, In step S6, the closed-loop trajectory tracking control system of the autonomous ship based on enhanced performance includes a software component module, a memory, and a processor. The software component module includes: a power system modeling and data preset module applied to step S1, a data acquisition and processing module applied to step S2, an error conversion module applied to step S3, a preset accuracy trajectory tracking controller design module of the autonomous ship based on enhanced performance applied to step S4, and a control implementation module applied to step S5. The memory includes the instruction codes and data of the software component module. When the instruction codes and data of the software component module are executed by the processor, steps S1 - S5 are implemented.

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