Autonomous sailing ship preset precision trajectory tracking control method based on strengthening performance

Abstract

An autonomous navigation ship preset precision trajectory tracking control method based on enhanced performance belongs to the technical field of ship trajectory tracking control based on a new generation of information technology, and comprises the following steps: S1, power system modeling and data presetting; s2, data acquisition and processing; s3, error conversion; s4, designing a preset precision trajectory tracking controller of the autonomous navigation ship based on enhanced performance; s5, implementing control; s6, establishing an autonomous navigation ship closed-loop trajectory tracking control system based on enhanced performance; according to the method, the uncertainty of the environment and the model in the water surface operation process of the autonomous navigation ship can be effectively dealt with, so that the trajectory tracking error of the autonomous navigation ship is restrained in a preset tracking precision range, and the tracking performance can be further explicitly enhanced by adjusting the control parameters; and the preset track can be tracked more accurately on the premise of ensuring the safe operation of the autonomous navigation ship.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory tracking control of autonomous vessels, and more specifically, to a preset precision trajectory tracking control method for 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 changeable 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 continuously increasing, making the problem of safe and high-precision trajectory tracking control become complex. To address the above challenges, the preset performance control method has emerged. By transforming the tracking error through non-linear mapping, the tracking error is restricted within the pre-set boundaries, thereby ensuring that the autonomous vessel can operate safely within the preset tracking error range. In practice, considering factors such as river width, 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 autonomous vessel operating environment, measurement errors, and sensor drift, the tracking error exceeds the preset performance boundary, leading to singularities or uncontrollable problems in the system, posing 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 running 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 changing 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 frame, 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 nonlinear 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 running trajectory η r (t) and the 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 running trajectory η r (t) preset 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);

[0008] S3, Error conversion: According to the preset desired running trajectory η r (t) and the tracking accuracy β(t) of the autonomous navigation ship preset in step 1, perform a nonlinear 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 velocity v(t) state of the autonomous navigation ship collected at time t in step S2, calculate the virtual velocity tracking error z v (t);

[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 non - linear dynamics in the autonomous navigation ship system are estimated using an RBF neural network; according to the virtual speed tracking error z of the autonomous navigation ship obtained in step S3 v (t) and the RBF neural network, an interference observer is designed to estimate the unknown interference suffered by the autonomous navigation ship control system; based on the trajectory tracking error e in step S2 η (t) and the speed tracking error e v (t), as well as the preset performance trajectory tracking error z in step S3 η (t) and the virtual speed tracking error z v (t), the trajectory enhanced performance feedback variable ξ η (t) and the speed enhanced performance feedback variable ξ v (t) are designed, and the enhanced performance virtual control law α(t) and the preset precision trajectory tracking controller τ(t) of the autonomous navigation ship based on enhanced performance are designed;

[0010] S5, control implementation: According to the second - order dynamic model of the autonomous navigation ship constructed in step 1, 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 is applied to the actuator of the autonomous navigation ship;

[0011] S6, establishment of the closed - loop trajectory tracking control system of the autonomous navigation ship based on enhanced performance: Update the autonomous navigation ship system, and obtain the state information of the autonomous navigation ship at the next moment, establish a closed - loop trajectory tracking control system of 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).

[0012] Furthermore, in step S1, the expression of the second - order dynamic model of the autonomous navigation 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 navigation 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 navigation ship at time t, and u(t), v(t) and r(t) respectively represent the forward speed, lateral speed and yaw angle of the autonomous navigation ship at time t; represents the transformation matrix for converting the body - fixed coordinate system of the autonomous navigation ship to the earth - fixed coordinate system at time t;

[0015]

[0016] and respectively 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, 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.

[0017] Furthermore, in step S2, the data acquisition and processing are specifically as follows:

[0018] 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:

[0019] e η (t) = η(t) - η r (t)

[0020] In the formula, η r (t) = [η r,1 (t), η r,2 (t), η r,3 (t)] T ;

[0021] 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:

[0022]

[0023] Furthermore, in step S3, the error conversion is specifically as follows:

[0024] Define the preset performance trajectory tracking error \(z\) of the autonomous navigation ship at time \(t\). η (t)=[z η,1 (t),z η,2 (t),z η,3 (t)] T , where

[0025]

[0026] In the formula, \(i = 1, 2, 3\), and the tracking accuracy of the preset autonomous navigation ship at time \(t\) is selected as:

[0027]

[0028] where \(\beta\) i 0 , \(\beta\) i ∞ and \(\beta\) i γ are all positive constants, \(i = 1, 2, 3\);

[0029] 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 navigation ship at time \(t\), and its expression is:

[0030] z v (t)=v(t)-\(\alpha\)(t)

[0031] In the formula, \(\alpha\)(t) represents the virtual control law of the autonomous navigation ship to be designed.

[0032] 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 nonlinear dynamics in the autonomous navigation ship system by using the RBF neural network, specifically:

[0033] Define \(\theta\)(t)(R(t)) = M -1 (D(v(t))v(t)+\(\omega\)(t))=[\theta 1 (t),\(\theta\) 2 (t),\(\theta\) 3 (t)] T represent the unknown nonlinear dynamics in the system, where \(R(t)=[\eta(t) T ,v(t) T T , and the RBF neural network is applied to approximate the unknown function \(\theta\) i (R(t)).​i (t) is re-expressed by using the parameters in the RBF neural network:

[0034]

[0035] 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.

[0036] Furthermore, in step S4, the disturbance observer is as follows:

[0037]

[0038] In the formula, is a positive constant, W(R(t)) = [W 1 (R(t)), W 2 (R(t)), W 3 (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

[0039]

[0040] In the formula, σ i and Γ i are positive constants to be designed, i = 1, 2, 3.

[0041] 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:

[0042] ξ η (t) = z η (t) + e η (t)

[0043] ξ v (t) = z v (t) + e v (t)

[0044] The designed virtual control law α(t) for enhanced performance and the preset precision trajectory tracking controller τ(t) for an autonomous navigation ship based on enhanced performance are as follows:

[0045]

[0046] In the formula, Wherein, Wherein, 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.

[0047] Furthermore, in step S6, the closed-loop trajectory tracking control system for the autonomous navigation ship based on enhanced performance includes a software component module, a storage, and a processor;

[0048] 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 precision 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;

[0049] The storage 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 realized.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention can effectively cope with the uncertainties of the environment and the 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 preset tracking accuracy range, and can further explicitly enhance the tracking performance by adjusting the control parameters, ensuring that the autonomous navigation ship more precisely tracks the preset trajectory on the premise of safe operation. Description of the Drawings

[0052] Figure 1 is the flowchart of the preset precision trajectory tracking control method for the autonomous navigation ship based on enhanced performance of the present invention;

[0053] Figure 2 is the schematic diagram of the software component module of the closed-loop trajectory tracking control system for the autonomous navigation ship based on enhanced performance of the present invention;

[0054] Figure 3 Schematic diagram of the preset tracking trajectory and the actual operating trajectory of the autonomous navigation ship in the embodiment of the present invention;

[0055] Figures 4 - 6 Schematic diagram of the trajectory tracking error of the autonomous navigation ship controlled by the preset tracking accuracy trajectory tracking control method and the preset performance control method based on enhanced performance in the embodiment of the present invention. Detailed implementation manners

[0056] 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 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.

[0057] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may 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.

[0058] As Figure 1 shown, the preset accuracy trajectory tracking control method for an autonomous navigation ship based on enhanced performance includes:

[0059] In step S1, a point is selected on the sea surface where the autonomous navigation ship operates as the origin of the inertial reference system. The direction pointing due north from the origin is taken as the positive direction of the longitudinal axis of the coordinate system, and the due east direction is taken as the positive direction of the transverse axis of the coordinate system to establish an earth-fixed coordinate system;

[0060] Taking the center of the autonomous navigation ship as the origin of the body-fixed coordinate system, the starboard side of the autonomous navigation ship as the positive direction of the transverse axis of the coordinate system, and the bow of the autonomous navigation ship as the positive direction of the longitudinal axis of the coordinate system to establish a body-fixed coordinate system;

[0061] Under the body-fixed coordinate system, a second-order dynamic model of the autonomous navigation ship is established. Based on the transformation matrix the operation of the autonomous navigation ship under the body-fixed coordinate system is converted into an earth-fixed coordinate system. The expression of the second-order dynamic model of the autonomous navigation ship is:

[0062]

[0063] 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 navigation ship 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 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;

[0064]

[0065] and respectively represent the inertia matrix of the autonomous ship, the Coriolis / centripetal matrix of the autonomous ship at time t and the uncertain non-linear hydrodynamic damping matrix, 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 non-linear 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, i.e., the enhanced performance trajectory tracking controller; ω(t) represents the unknown bounded environmental disturbance acting on the autonomous ship at time t;

[0066] Preset the desired operating trajectory η r (t) = [η r,1 (t), η r,2 (t), η r,3 (t)] T of the autonomous ship at time t, so that the autonomous ship can run 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 trajectory tracking accuracy β(t) of the autonomous ship, 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.

[0067] 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 denotes the trajectory tracking error of the autonomous ship at time t, and its expression is:

[0068] e η (t) = η(t) - η r (t)

[0069] For the trajectory tracking error e η (t), take the derivative and let denote the speed tracking error of the autonomous ship at time t, and its expression is:

[0070]

[0071] In step S3, according to the preset desired operating trajectory η r (t) of the autonomous ship and the tracking accuracy β(t) in step S1, perform a non - linear transformation on the calculated trajectory tracking error e η (t), and define the preset performance trajectory tracking error z η (t) = [z η,1 (t), z η,2 (t), z η,3 (t)] T , which is:

[0072]

[0073] In the formula, i = 1, 2, 3, and the preset tracking accuracy of the autonomous ship at time t is selected as:

[0074]

[0075] where β i 0 , β i ∞ and β i γ are all positive constants, and i = 1, 2, 3.

[0076] According to the real - time speed v(t) state of the autonomous ship collected in step S2 at time t, define the speed tracking error z v (t) = [z v,1 (t), z v,2 (t), z v,3 (t)] T :

[0077] z v (t) = v(t) - α(t)

[0078] where, α(t) represents the virtual control law of the autonomous navigation ship to be designed.

[0079] In step S4, according to the trajectory tracking error e η (t) of the autonomous navigation ship obtained in step S2 and the preset performance trajectory tracking error z η (t) obtained in step S3, define θ(t)(R(t)) = M -1 (D(v(t))v(t) + ω(t)) = [θ 1 (t), θ 2 (t), θ 3 (t)] T which represents the unknown non - linear dynamics in the system, where, R(t) = [η(t) T , v(t) T T , and apply the RBF neural network to approximate the unknown function θ i (R(t)). The value of θ i (t) is re - expressed by the parameters in the RBF neural network:

[0080]

[0081] 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;

[0082] To estimate the unknown disturbance suffered by the autonomous navigation ship control system, according to the speed 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:

[0083]

[0084] where, is a positive constant, W(R(t)) = [W 1 R(t)), W 2 R(t)), W 3 (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 is as follows:

[0085]

[0086] In the formula, σ i and Γ i are positive constants to be designed, where i = 1, 2, 3;

[0087] In order to ensure that the trajectory tracking error of the autonomous ship can not only be constrained within the preset tracking accuracy range, but also be further improved by adjusting the control parameters, based on the trajectory tracking error e η (t) and the speed tracking error e v (t) in step S2, as well as the preset performance trajectory tracking error z η (t) and the virtual speed tracking error z v (t) in step S3, design the trajectory enhanced performance feedback variable ξ η (t) and the speed enhanced performance feedback variable ξ v (t) as follows:

[0088] ξ η (t) = z η (t) + e η (t)

[0089] ξ v (t) = z v (t) + e v (t)

[0090] Design the enhanced performance virtual control law α(t) and the preset accuracy trajectory tracking controller τ(t) for the autonomous ship based on enhanced performance as follows:

[0091]

[0092] 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.

[0093] In step S5, according to the second-order dynamic model of the autonomous ship established in step S1, the preset precision 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; in step S6, the autonomous ship system is updated, and the state information of the autonomous ship at the next moment is obtained, and a closed-loop trajectory tracking control system of the autonomous ship based on enhanced performance is established, so that the actual operation trajectory η(t) of the autonomous ship approaches the preset desired operation trajectory η r (t), and steps 2-5 are repeated.

[0094] Among them, 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;

[0095] As Figure 2 shown, the software component module includes: the power system modeling and data virtual control law applied to step S1 and the controller design are as follows:

[0096]

[0097] Verified through simulation experiments, Figure 3 the preset tracking trajectory and the actual operation trajectory of the autonomous ship are given. It can be seen that the preset precision trajectory tracking control method of 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 of the autonomous ship based on enhanced performance and the preset performance control method are as Figures 4 - 6 shown; it can be seen from Figures 4 - 6 that 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 of the autonomous ship based on enhanced performance has a faster convergence speed and smaller trajectory tracking errors.

[0098] In summary, the embodiment of the present application can realize that the tracking accuracy of the autonomous ship can be preset, and can further improve the tracking accuracy of the autonomous ship.

[0099] 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 within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A preset precision trajectory tracking control method for an autonomous ship based on enhanced performance, characterized in that: The following steps are involved: S1, dynamic system modeling and data preset: select a point on the sea surface where the autonomous ship is running as the origin of the inertial reference system to establish the earth-fixed coordinate system; take the center of the autonomous ship as the origin of the hull coordinate system to establish the body-following coordinate system; consider the uncertain nonlinear hydrodynamic damping, unmodeled dynamics and unknown bounded environmental disturbances of the autonomous ship when running on the sea surface, and establish the second-order dynamic model of the autonomous ship; preset the expected trajectory η of the autonomous ship r (t) and tracking accuracy β(t); S2, data collection and processing: collect the real-time position η(t) and speed v(t) status information of the autonomous ship at time t; compare the real-time position η(t) collected by the autonomous ship with the running track η preset in step S1 r (t) and calculate the trajectory tracking error e η (t); trajectory tracking error e η (t) is derived to calculate the velocity tracking error e v (t); S3, error conversion: according to the expected autonomous ship running trajectory η preset in step 1 r (t) and tracking accuracy β(t), the calculated trajectory tracking error e η (t) performs nonlinear transformation to obtain the preset performance trajectory tracking error z η (t); Calculate the virtual speed tracking error z of the autonomous ship according to the real-time speed v(t) state of the autonomous ship at time t collected in step S2 v (t); S4, design of preset precision trajectory tracking controller for autonomous sailing ship based on enhanced performance: According to the trajectory tracking error e obtained in step S2 η (t) and the preset performance trajectory tracking error z obtained in step S3 η (t), using RBF neural network to estimate unknown nonlinear dynamics in autonomous ship system; According to the virtual speed tracking error z of the autonomous ship obtained in step S3 v (t) and RBF neural network, design a disturbance observer to estimate the unknown disturbance to the control system of the autonomous ship; based on the trajectory tracking error e in step S2 η (t) and velocity tracking error e v (t), and the preset performance trajectory tracking error z in step S3 η (t) and virtual velocity tracking error z v (t), the design trajectory enhances the performance feedback variable ξ η (t) and the speed enhancement 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 ship based on the enhanced performance; S5, control implementation: according to the second-order dynamic model of the autonomous ship constructed in step 1, the preset precision 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; S6, establishment of closed-loop trajectory tracking control system for autonomous ship based on enhanced performance: update the autonomous ship system, obtain the state information of the autonomous ship at the next moment, establish a closed-loop trajectory tracking control system for autonomous ship based on enhanced performance, so that the actual running trajectory η(t) of the autonomous ship approaches the expected running trajectory η preset in step 1 r (t).

2. The preset precision trajectory tracking control method for an autonomous ship based on enhanced performance according to claim 1 is characterized in that: In step S1, the second-order dynamic model expression of the autonomous sailing ship is: In the formula, t represents the current moment; and v(t)=u(t),υ(t),r(t)] T They represent the real-time position and velocity vector of the autonomous ship in the earth-fixed coordinate system at time t, respectively, where x(t), y(t) and They represent the forward displacement, lateral drift displacement and heading angle of the autonomous ship at time t, respectively; u(t), v(t) and r(t) represent the forward speed, lateral speed and heading angle of the autonomous ship at time t, respectively; represents the transformation matrix that transforms the autonomous ship's body coordinate system to the earth's fixed coordinate system at time t; and denote 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. 11 ,m 22 ,m 23 ,m 32 and m 33 is a positive constant, d 11 (v(t)),d 22 (v(t)),d 23 (v(t)),d 32 (v(t)) and d 33 (v(t)) is a nonlinear function 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 of the autonomous ship at time t.

3. The preset precision trajectory tracking control method for an autonomous ship based on enhanced performance according to claim 2 is characterized in that: In step S2, the data collection and processing are specifically as follows: Let e η (t) = [e η,1 (t),e η,2 (t),e η,3 (t)] T It represents the trajectory tracking error of the autonomous 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 ; The trajectory tracking error e η (t) is derived, and It represents 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 ship based on enhanced performance according to claim 3 is characterized in that: In step S3, the error conversion is specifically: Define the preset performance trajectory tracking error z of the autonomous ship at time t η (t) = [z η,1 (t),z η,2 (t),z η,3 (t)] T ,in, In the formula, i = 1, 2, 3, and the preset tracking accuracy of the autonomous ship at time t is selected as: Among them, β i 0 ,β i ∞ and β i γ All are positive numbers, i = 1, 2, 3; Let z v (t) = [z v,1 (t),z v,2 (t),z v,3 (t)] T It represents 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 ship based on enhanced performance according to claim 4 is characterized in that: In step S4, the trajectory tracking error e obtained in step 3 is η (t) and the preset performance trajectory tracking error z η (t), the unknown nonlinear dynamics in the autonomous ship system are estimated using the RBF neural network, 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 RBF neural network to the unknown function θ i (R(t)) is approximated, θ i The value of (t) is re-expressed using the parameters in the RBF neural network: in, 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.

6. The preset precision trajectory tracking control method for an autonomous ship based on enhanced performance according to claim 5, characterized in that: In step S4, the interference observer is as follows: In the formula, is a positive constant, W(R(t))=[W1(R(t)),W2(R(t)),W3(R(t))] T ; Represents a function estimates; represents the state variable of the disturbance observer; Express estimates of the unknown optimal weights of the neural network, The update law is: In the formula, σ i and Γ i is the normal number to be designed, i=1,2,3.

7. The preset precision trajectory tracking control method for an autonomous ship based on enhanced performance according to claim 6, characterized in that: In step S4, the design trajectory strengthens the performance feedback variable ξ η (t) and the speed enhancement performance feedback variable ξ v (t) are as follows: ξ η (t)=z η (t)+e η (t) ξ v (t)=z v (t)+e v (t) The designed enhanced performance virtual control law α(t) and the preset precision trajectory tracking controller τ(t) of the autonomous ship based on enhanced performance are as follows: In the formula, in, in, and represents the control gain to be designed, where K 1,1 ,K 1,2 ,K 1,3 ,K 2,1 ,K 2,2 ,K 2,3 Is a normal number.

8. The preset precision trajectory tracking control method for an autonomous ship based on enhanced performance according to any one of claims 1 to 7, characterized in that: In step S6, the closed-loop trajectory tracking control system for an autonomous ship based on enhanced performance includes a software component module, a storage and a processor; The software component modules include: 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 precision trajectory tracking controller design module for autonomous sailing ships based on enhanced performance applied to step S4, and a control implementation module applied to step S5; The storage includes instruction codes and data of the software component modules. When the instruction codes and data of the software component modules are executed by the processor, steps S1-S5 are implemented.

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