Unmanned ship tracking control method and system based on absolute position sensing under speed-measurement-free condition

Abstract

The invention provides a speed-measurement-free unmanned ship tracking control method and system based on absolute position sensing. Aiming at the problem that a following boat cannot acquire speed information due to failure of a pilot boat speed sensor or communication failure, a distributed finite time state observer is designed, and the speed of a pilot boat is estimated in real time by combining an absolute position and positioning information. And designing an auxiliary controller by adopting a backstepping method based on the observation speed, and constructing a stability constraint. And a model prediction control framework based on a distributed Lyapunov function is further proposed, so that the multi-unmanned ship system can realize expected formation trajectory tracking and attitude consistency only depending on self-sensing and positioning information, and meanwhile, the formation stability is ensured. According to the method, under the condition that the speed of the pilot ship cannot be obtained, the speed can still be accurately estimated, and the control strategy can be optimized online, so that the intelligent sensing, positioning precision, control performance and robustness of the unmanned ship formation system are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the technical field of unmanned surface vessel (USV) formation trajectory tracking, specifically, it relates to a USV tracking and control method and system based on absolute position perception without speed measurement. Background Technology

[0002] In recent years, with the rapid development of marine exploration, environmental protection, and national defense, unmanned surface vehicles (USVs), as a type of intelligent marine equipment, have attracted widespread attention due to their advantages such as high flexibility, low cost, and ability to avoid human risks. While the operational capabilities of a single USV are limited, multi-USV swarm collaborative systems can perform more complex tasks through task allocation and cooperation, such as large-area marine mapping, collaborative search and rescue, and three-dimensional surveillance and encirclement, greatly expanding the application prospects of USVs. In swarm control, achieving stable and accurate tracking of USVs based on absolute position perception and positioning information is the foundation and key to completing collaborative operations.

[0003] Currently, significant progress has been made in unmanned surface vessel (USV) swarm tracking and control technology. Typical control methods include behavior-based methods, pilot-follower methods, virtual structure methods, and consistency theory. Among these, the pilot-follower method is widely adopted due to its clear structure and ease of implementation. In this method, the following vessel typically needs to acquire accurate state information (including position, heading, and speed) of itself and the pilot vessel in real time. This complete feedback information is then used to generate control commands through algorithms such as feedback linearization, backstepping, or model predictive control to achieve precise tracking control. However, in actual marine operating environments, the aforementioned control methods, which rely on complete speed information, face severe challenges. The USV's perception and positioning systems (such as GPS, inertial measurement units (IMUs), and Doppler logs (DVLs) may experience functional limitations or decreased measurement accuracy due to adverse sea conditions, signal obstruction, equipment malfunctions, or cost constraints, resulting in the instantaneous or continuous loss of vessel speed information (including linear and angular velocities). For example, inexpensive small unmanned surface vessels may not be equipped with high-precision DVL (Depth-to-Vehicle Positioning), and their speed information can only be estimated through GPS position differential, which has limited accuracy and is susceptible to interference. In complex electromagnetic environments, GPS signals may also be interrupted, further exacerbating the lack of state information.

[0004] Without velocity measurement, many advanced control algorithms (such as feedback linearization) heavily rely on precise dynamic models of the system, making it impossible to accurately construct system state equations using limited sensing and positioning information. This leads to a sharp decline in control performance and even system instability. Furthermore, while some control strategies perform well with complete information, their robustness becomes insufficient when critical state information is missing, making them vulnerable to external disturbances and prone to problems such as increased tracking errors and formation divergence. Therefore, there is an urgent need for an innovative method that can maintain stable tracking control of unmanned surface vessel (USV) formations based on sensing and positioning information even in the absence of velocity measurement. This method should be independent of velocity sensors and possess strong robustness to model uncertainties and external disturbances, thereby improving the reliability and survivability of USV formations in complex real-world environments. Summary of the Invention

[0005] Objective: To overcome the shortcomings of existing unmanned surface vessel (USV) swarm control technologies that overly rely on the speed information of the lead vessel, this invention provides a USV tracking control method and system based on absolute position perception without speed measurement. By designing a distributed finite-time state observer, the speed state of the lead vessel is estimated using perception and positioning information. Combined with a backstepping method and a model predictive control algorithm based on distributed Lyapunov functions, this invention enables a multi-USV system to quickly and stably form and maintain the desired swarm formation, completing the cooperative trajectory tracking task, even without directly acquiring the lead vessel's speed signal. This invention achieves high-precision state estimation and optimized control in complex environments with limited information, relying on limited perception and positioning information, significantly improving the robustness, reliability, and cooperative control performance of the USV swarm system.

[0006] Technical Solution: To achieve the above objectives, the first aspect of this invention provides a tracking and control method for unmanned surface vessels based on absolute position awareness without velocity measurement, comprising the following steps:

[0007] S1. Establish an unmanned surface vessel (USV) model in a multi-USV formation system. Based on the reference trajectory, obtain the absolute position perception and positioning trajectory information of the lead USV and the expected trajectory of the follower.

[0008] S11, construct the motion model and three-degree-of-freedom dynamics model of the unmanned surface vessel;

[0009] Ignoring the ship's roll, heave, and bow roll, the kinematic model of the unmanned surface vessel is as follows:

[0010]

[0011] In the formula, η = [x, y, ψ] T ∈R 3This represents the position vector of the ship in the northeast-northeast coordinate system, where x is the eastward displacement, y is the northward displacement, and ψ is the heading angle (R). 3 (representing a 3-dimensional vector space), υ=[u,v,r] T ∈R 3 Let represent the velocity vector of the unmanned surface vessel (USV), where u is the sway velocity, v is the yaw velocity, and r is the turning rate; R(ψ) is the rotation matrix; further, the three-degree-of-freedom nonlinear dynamic model of the USV is as follows:

[0012]

[0013] In the formula, τ=[τ u ,τ v ,τ r ] T ∈R 3 It is the control input vector, τ u ,τ v ,τ r Let M, C(υ), and D(υ) represent the longitudinal control moment, lateral control moment, and turning control moment of the unmanned surface vessel, respectively; M, C(υ), and D(υ) represent the rigid body inertia matrix, Coriolis-centripetal matrix, and damping matrix, respectively, and their specific expressions are as follows:

[0014]

[0015] In the formula, d 11 ,d 22 ,d 33 and m 11 ,m 22 ,m 33 d represents the inherent parameters of the unmanned surface vessel. 23 ≠d 32 ,m 23 ≠m 32 .

[0016] S12, Construct a model for a tracking system for unmanned surface vessels (USVs);

[0017] The kinematics and dynamics models of the unmanned surface vessel (USV) are used to establish a system model for the tracking problem of USV i, as detailed below:

[0018]

[0019] In the formula, X i =col(η) i ,υ i )∈R 6 To track the state vector of the system, col(η) i ,υ i ) represents η i and υ i The 6-dimensional vector formed, R 6Represents a 6-dimensional vector space. Let M be the inertia matrix. i The inverse matrix;

[0020] S13, Constructing the reference trajectory for the lead unmanned surface vessel (USV) in a leader-follower formation, including:

[0021] The reference trajectory of the pilot unmanned surface vessel as a function of time is defined as η0(t)=[x0(t),y0(t),ψ0(t)] T The reference trajectory is extended as follows:

[0022]

[0023]

[0024] Where atan2 is the arctangent function in the fourth quadrant; u0 is the sway velocity of the pilot UAV, v0 is the yaw velocity of the pilot UAV, and r0 is the turning rate of the pilot UAV. The desired trajectory of the i-th following UAV is defined as η. i.r (t)=[x i,r (t),y i,r (t),ψ i,r (t)] T η i.r The relative deviation between (t) and the desired trajectory of the pilot unmanned surface vessel is λ. i,r (t)=R(ψ i )d i,r , where d i,r Let η represent the expected relative position of follower i with respect to the lead unmanned surface vessel. The specific calculation method for the expected trajectory of follower i is η. i.r (t)=η0-λ i,r .

[0025] S2 uses graph theory to describe the communication topology between unmanned surface vessels (USVs). Based on the USV model and the absolute position perception and positioning information of the pilot USV, a distributed finite-time state observer is designed to estimate the speed information of the pilot USV.

[0026] S21, Establishing an unmanned surface vessel (USV) formation communication topology network based on graph theory;

[0027] A directed graph G = {V, E, A} is used to describe the communication topology of n unmanned surface vessels; where V = {n i {i = 1, 2, ..., n} represents a finite non-empty set of n unmanned surface vessels; E ∈ (n i ×n j A represents the set of edges; A = [a ij ]∈R n×n Let R represent the weighted adjacency matrix of graph G, and let R represent the set of all real numbers. If a ijIf a > 0, it means there is an edge between unmanned surface vessel i and unmanned surface vessel j; otherwise, a ij =0 means there is no connection between the two boats, therefore, the unmanned boat node n i The set of neighboring nodes is represented as N i ={j:(n j ,n i Based on this, the degree of the i-th unmanned surface vessel node is defined as )∈E}; Then the degree matrix and Laplacian matrix of graph G are respectively D=diag{d1,d2,…,d n} and L = DA. If there is information exchange between the following UAV and the lead UAV, then the connectivity between the lead UAV and the follower is represented as B = [b1, b2, ..., b n ] T ∈R n When the unmanned surface vessel (USV) can obtain information about the navigator USV, b i >0, otherwise, b i =0.

[0028] A directed graph G is said to be strongly connected if any two nodes in G can reach each other. If a directed graph G is strongly connected, then there exists a column vector W = [w1, w2, ..., w...]. n ] T ∈R n This makes W T L(A) = 0 T Furthermore, for a non-negative vector B∈R, all its elements are positive. n If b exists i If the value is greater than 0, then the matrix S = (diag(W)L(A) + L(A)) T diag(W))+diag(W)diag(B) / 2 is positive definite.

[0029] S22, Design of a distributed finite-time state observer based on graph theory;

[0030] Design a distributed finite-time observer for unmanned surface vessel (USV) swarms to estimate the speed information of the lead USV. The specific expression of the observer is as follows:

[0031]

[0032] In the formula, ζ i,0 =R(ψ) i )υ i , It is for ζ i,0 The estimate, which means the velocity estimate of unmanned surface vessel i for the lead unmanned surface vessel in the world coordinate system, It is for η i,0The estimate represents the position estimate of unmanned surface vessel i for the lead unmanned surface vessel. [c i ] 0 =sgn(c i ), sgn(·) is the sign function; k1, k2 are the observer coefficients, which need to satisfy the following conditions:

[0033]

[0034] In the formula, ρ is an arbitrary constant between 0 and 1, g is a positive constant, and γ0 and γ1 are hyperparameters that need to satisfy the condition γ0=(1+3γ1) / (1-ρ)>1.

[0035] The controller is designed using the observed speed values ​​instead of the actual speed values ​​to ensure that multiple unmanned surface vessels can still eventually form the desired formation even if the speed information of the lead unmanned surface vessel is lost.

[0036] S3. Based on the estimated system state information of the navigating unmanned surface vessel, the auxiliary controller is designed using the backstepping method.

[0037] S31, reconstruct the kinematic and dynamic system model of the position error of the following unmanned surface vessel i;

[0038] Based on the system state information of the lead unmanned surface vessel (USV), a following error system for the USV is constructed. The trajectory of the following USV i is defined as follows: Meanwhile, the desired path of unmanned surface vessel i is defined as follows: The virtual reference trajectory of unattended vessel i is then:

[0039]

[0040] In the formula, Λ represents the trajectory tracking error of the unmanned surface vessel i. i The diagonal parameter matrix to be designed is used to define a new state variable s. i You can get

[0041] The position error dynamics can then be expressed as:

[0042]

[0043] In the formula, υ i,0 This represents the expected speed of the unmanned surface vessel i. It is a function used for system stability.

[0044] Furthermore, the dynamics of following the unmanned surface vessel i can be obtained:

[0045]

[0046] In the formula, also,

[0047]

[0048] S32, based on the formation tracking error system model, designs a multi-unmanned surface vessel auxiliary controller based on Lyapunov functions;

[0049] The ideal auxiliary controller is designed as follows:

[0050]

[0051] In the formula, K p,i =diag{k p,i1 ,k p,i2 ,k p,i3} of k p,i1 ,k p,i2 ,k p,i3 It is a positive parameter; Chinese K d,i1 ,k d,i2 ,k d,i3 It is a positive number. For ideal control input.

[0052] S4. Construct the evaluation function and constraints, and calculate the optimal control input for following the unmanned surface vessel based on the distributed model predictive control algorithm to achieve tracking control of the following vessel;

[0053]

[0054] In the formula, S(δ) represents a family of piecewise constant functions with a sampling period of δ, and s∈[t k ,t k+T ]and It is an unmanned surface vessel (USV) in t k Control input at any given time and optimal control input; It is the control input calculated by the auxiliary controller; V i It is a Lyapunov function. Indicates control input as The stability of the unmanned surface vessel system i is expressed by the following formula:

[0055]

[0056] also, X represents the optimal solution to the above optimization problem; i (t k ) represent the actual state of unmanned surface vessel i in the system model of the tracking problem, and respectively. Optimal state. Equation ① represents the system model constraints of unmanned surface vessel i, used for predicting the state of unmanned surface vessel i. Equation ② represents t.k The initial state condition constraints at time i are given by equation ③, which represents the control input constraints of the unmanned surface vessel i, and equation ④, which represents the stability constraints based on the control input calculated by the auxiliary controller and the relevant Lyapunov function.

[0057] The second aspect of the present invention provides an unmanned surface vessel tracking and control system based on absolute position perception without velocity measurement, comprising a model building module, a distributed finite-time observer module, an auxiliary control module, and a distributed model predictive control module;

[0058] Model building module: Obtain absolute position perception and positioning information of the following and leading unmanned surface vessels (USVs), establish the motion equations for formation tracking, and build a three-degree-of-freedom dynamic model of the following USVs based on these equations;

[0059] Distributed finite-time observer module: Utilizing the inter-vessel communication topology and the position perception and positioning information of the pilot unmanned surface vessel, a distributed finite-time velocity observer is constructed, and the velocity state of the pilot unmanned surface vessel is estimated in real time under the condition of no direct velocity measurement;

[0060] Auxiliary controller module: Based on the estimation results of the observer, the ideal auxiliary control law for following the unmanned surface vessel is designed using the backstepping method;

[0061] Distributed Model Predictive Control Module: Based on a distributed Lyapunov function predictive control model, and combined with evaluation functions and constraints, the predictive control input is optimized online to obtain the optimal control input that satisfies stability constraints.

[0062] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0063] 1. By designing a distributed finite-time state observer, the speed information of the lead unmanned surface vessel can be estimated quickly and accurately by relying solely on the inter-vessel communication network when the speed information of the lead vessel is completely missing. This overcomes the dependence of traditional formation control methods on speed information and significantly enhances the applicability and reliability of the system.

[0064] 2. Ensuring system stability and finite-time convergence: The backstepping method design is combined with a distributed model predictive control framework. The backstepping auxiliary controller provides good initial stability and reference for the system, while the Lyapunov constraints strictly guarantee the stability of the entire predictive control closed-loop system, ensuring that the tracking error converges asymptotically and improving the control accuracy. Attached Figure Description

[0065] Figure 1 This is a flowchart of the unmanned surface vessel tracking and control algorithm based on absolute position perception without velocity measurement, according to the present invention.

[0066] Figure 2 A schematic diagram illustrating how an unmanned surface vessel follows a desired trajectory;

[0067] Figure 3 This is a communication topology diagram of the three unmanned surface vessels in this example;

[0068] Figure 4 This is a schematic diagram illustrating the unmanned surface vessel (USV) formation trajectory tracking in this example.

[0069] Figure 5 The pose and velocity state estimation error curves for the three unmanned surface vessels in this example;

[0070] Figure 6 This is a schematic diagram illustrating the following errors of the three unmanned surface vessels relative to the desired trajectory in this example;

[0071] Figure 7 The control torque curves for the three unmanned surface vessels in this example are shown. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] refer to Figure 1 The present invention provides a tracking and control method for unmanned surface vessels based on absolute position perception without velocity measurement, comprising:

[0074] S1. Establish an unmanned surface vessel (USV) model in a multi-USV formation system, and obtain the trajectory of the lead USV and the expected trajectory of the follower based on the reference trajectory.

[0075] S11, construct the motion model and three-degree-of-freedom dynamics model of the unmanned surface vessel;

[0076] S12, Construct the unmanned surface vessel i-tracking system model;

[0077] S13, Constructing the reference trajectory of the lead unmanned surface vessel (USV) in a leader-follower formation structure.

[0078] S2 uses graph theory to describe the communication topology between unmanned surface vessels (USVs). Based on the USV model and the pose state of the lead USV, a distributed finite-time state observer is designed to estimate the speed information of the lead USV.

[0079] S21, Establishing an unmanned surface vessel (USV) formation communication topology network based on graph theory;

[0080] S22, Design of a distributed finite-time state observer based on graph theory;

[0081] S3. Based on the estimated system state information of the navigating unmanned surface vessel, the auxiliary controller is designed using the backstepping method.

[0082] S31, reconstruct the kinematic and dynamic system model of the position error of the following unmanned surface vessel i;

[0083] S32, based on the formation tracking error system model, designs a multi-unmanned surface vessel auxiliary controller based on Lyapunov functions;

[0084] S4. Construct the evaluation function and constraints, and calculate the optimal control input for following the unmanned surface vessel based on the distributed model predictive control algorithm to achieve tracking control of the following vessel;

[0085] To enable those skilled in the art to understand the technical solutions provided by the embodiments of this disclosure in more detail, the following description, in conjunction with the accompanying drawings, provides detailed examples:

[0086] In this embodiment, MATLAB 2024b is used as the simulation software to simulate a formation of three follower unmanned surface vessels (USVs) and one lead USV in a northeast-northeast coordinate system, performing formation trajectory tracking control. The lead USV operates independently along a reference trajectory, providing motion reference for the formation and remaining unaffected by the other follower USVs. The three follower USVs maintain their relative positions and follow the lead USV's movement through a formation control strategy, specifically as follows... Figure 2 As shown.

[0087] Its inertia matrix and damping matrix are as follows:

[0088]

[0089] The communication topology diagram of the three follower and lead unmanned surface vessels is as follows: Figure 3 As shown, the corresponding communication Laplace matrix is:

[0090]

[0091] The sampling period of the unmanned surface vessel (USV) model predictive controller is 0.1s, the prediction time domain is 0.6s, and the initial states of the three USVs are [0,2,(π / 2),0.4,0,0]. T ,[-0.5,-0.5,(3π / 4),0.4,0,0] T and [-0.1, 0.1, -(π / 2), 0.4, 0, 0] T The control input constraint is 500 N (N·m), the desired formation of the formation is an equilateral triangle, and the reference trajectory of the lead unmanned surface vessel is:

[0092] x0 = 0.5t

[0093] y0 = sin(0.5t + π / 2)

[0094] Figure 4 A schematic diagram of unmanned surface vessel (USV) formation trajectory tracking is provided. The diagram shows three USVs using the method of this invention to track the reference trajectory of the lead USV, forming a desired equilateral triangle formation. The dashed line represents the reference trajectory of the lead USV, the solid black line represents the trajectory of follower USV 1, the dotted line below represents the trajectory of follower USV 2, and the dashed line above represents the trajectory of follower USV 3. Figure 4 It can be seen that the unmanned surface vessel (USV) formation tracking and control method and system proposed in this invention, without speed measurement, can ensure that the USV formation can track the desired trajectory and maintain the desired formation.

[0095] Figure 5 The velocity state estimation error curves for three follower unmanned surface vessels are presented. Figure 5 It can be seen that all three followers were able to estimate the speed of the lead unmanned surface vessel within a limited time.

[0096] Figure 6 A schematic diagram illustrating the following errors of three unmanned surface vessels relative to their desired trajectories is presented. From... Figure 6 It can be seen that all three followers can track the lead unmanned surface vessel and achieve the desired formation.

[0097] Figure 7 Three control torque response curves for following unmanned surface vessels are presented. Figure 7 It can be seen that the control torque of the three following unmanned surface vessels can achieve stable control of the three following unmanned surface vessels and satisfy the control input constraints.

[0098] The simulation results clearly demonstrate that the unmanned surface vessel (USV) tracking control method and system based on absolute position awareness without velocity measurement proposed in this invention can quickly estimate the velocity information of the lead USV. Furthermore, the system control input consistently ensures system stability during the control process.

[0099] In summary, the present invention provides an unmanned surface vessel (USV) tracking and control method based on absolute position perception without velocity measurement. The state observer has a fast convergence speed and strong control robustness, enabling USV formation trajectory tracking and control without velocity measurement information.

Claims

1. A tracking and control method for unmanned surface vessels based on absolute position awareness without velocity measurement, characterized in that, include: S1. Establish an unmanned surface vessel (USV) model in a multi-USV formation system. Based on the reference trajectory, obtain the absolute position perception and positioning trajectory information of the lead USV and the expected trajectory of the follower. S2 uses graph theory to describe the communication topology between unmanned surface vessels (USVs). Based on the USV model and the absolute position perception and positioning information of the pilot USV, a distributed finite-time state observer is designed to estimate the speed information of the pilot USV. S3. Based on the estimated system state information of the navigating unmanned surface vessel, the backstepping method is used to design the auxiliary controller; S4. Construct the evaluation function and constraints, and calculate the optimal control input for following the unmanned surface vessel based on the distributed model predictive control algorithm to achieve tracking control of the following vessel.

2. The unmanned surface vessel tracking and control method based on absolute position awareness without velocity measurement as described in claim 1, characterized in that, Step S1 includes: S11, construct the motion model and three-degree-of-freedom dynamics model of the unmanned surface vessel; Ignoring the ship's roll, heave, and bow roll, the kinematic model of the unmanned surface vessel is as follows: In the formula, η = [x, y, ψ] T ∈R 3 This represents the absolute position vector of the ship in the northeast-northeast coordinate system, where x is the eastward displacement, y is the northward displacement, ψ is the heading angle, and R... 3 Let υ represent a 3-dimensional real vector space, where υ = [u, v, r]. T ∈R 3 Let represent the velocity vector of the unmanned surface vessel, where u is the sway velocity, v is the roll velocity, r is the turning rate; R(ψ) is the rotation matrix. The three-degree-of-freedom nonlinear dynamic model of the unmanned surface vessel is as follows: In the formula, τ=[τ u ,τ v ,τ r ] T ∈R 3 It is the control input vector, τ u ,τ v ,τ r Let M, C(υ), and D(υ) represent the longitudinal control moment, lateral control moment, and turning control moment of the unmanned surface vessel, respectively; M, C(υ), and D(υ) represent the inertia matrix, Coriolis-centripetal matrix, and damping matrix, respectively, and their specific expressions are as follows: In the formula, d 11 ,d 22 ,d 33 and m 11 ,m 22 ,m 33 d represents the inherent parameters of the unmanned surface vessel. 23 ≠d 32 ,m 23 ≠m 32 ; S12, Construct a model for a tracking system for unmanned surface vessels (USVs); The kinematics and dynamics models of the unmanned surface vessel (USV) are used to establish a system model for the tracking problem of USV i, as detailed below: In the formula, X i =col(η) i ,υ i )∈R 6 X i To track the state vector of the system, col(η) i ,υ i ) represents η i and υ i The 6-dimensional vector formed, R 6 Represents a 6-dimensional vector space. Let M be the inertia matrix. i The inverse matrix; S13, Constructing the reference trajectory for the lead unmanned surface vessel (USV) in a leader-follower formation, including: The reference trajectory of the pilot unmanned surface vessel as a function of time is defined as η0(t)=[x0(t),y0(t),ψ0(t)] T The reference trajectory is extended as follows: v0＝0 Where atan2 is the arctangent function in the fourth quadrant; u0 is the sway velocity of the pilot UAV, v0 is the yaw velocity of the pilot UAV, r0 is the turning rate of the pilot UAV, and the expected trajectory of the i-th following UAV is defined as η. i.r (t)=[x i,r (t),y i,r (t),ψ i,r (t)] T η i.r The relative deviation between (t) and the desired trajectory of the pilot unmanned surface vessel is λ. i,r (t)=R(ψ i )d i,r , where d i,r Let η represent the expected relative position of follower i with respect to the lead unmanned surface vessel. The specific calculation method for the expected trajectory of follower i is η. i.r (t)=η0-λ i,r .

3. The unmanned surface vessel tracking and control method based on absolute position awareness without velocity measurement according to claim 2, characterized in that, Step S2 includes: S21, Establishing an unmanned surface vessel (USV) formation communication topology network based on graph theory; A directed graph G = {V, E, A} is used to describe the communication topology of n unmanned surface vessels; where V = {n i {i = 1, 2, ..., n} represents a finite non-empty set of n unmanned surface vessels; E ∈ (n i ×n j A represents the set of edges; A = [a ij ]∈R n×n Let a be the weighted adjacency matrix of graph G, and R be the set of all real numbers; if a ij If a > 0, it means there is an edge between unmanned surface vessel i and unmanned surface vessel j; otherwise, a ij =0 means there is no connection between the two boats; therefore, the unmanned boat node n i The set of neighboring nodes is represented as N i ={j:(n j ,n i Based on this, the degree of the i-th unmanned surface vessel node is defined as )∈E}; Then the degree matrix and Laplacian matrix of graph G are respectively D=diag{d1,d2,…,d n } and L = DA; if there is information exchange between the follower UV and the lead UV, then the connectivity between the lead UV and the follower is represented as B = [b1, b2, ..., b n ] T ∈R n When the unmanned surface vessel (USV) can obtain information about the navigator USV, b i >0, otherwise, b i =0; A directed graph G is said to be strongly connected if any two nodes in G can reach each other; if a directed graph G is strongly connected, then there exists a column vector W = [w1, w2, ..., w...]. n ] T ∈R n This makes W T L(A) = 0 T And all elements w of the vector i All are positive numbers; furthermore, for a non-negative vector B∈R n If b exists i If the value is greater than 0, then the matrix S = (diag(W)L(A) + L(A)) T diag(W))+diag(W)diag(B) / 2 is positive definite; S22, Design of a distributed finite-time state observer based on graph theory; Design a distributed finite-time observer for unmanned surface vessel (USV) swarms to estimate the speed information of the lead USV. The specific expression of the observer is as follows: In the formula, ζ i,0 =R(ψ) i )υ i , It is for ζ i,0 The estimate, which means the velocity estimate of unmanned surface vessel i for the lead unmanned surface vessel in the world coordinate system, It is for η i,0 The estimate represents the position estimate of unmanned surface vessel i for the lead unmanned surface vessel. [c i ] 0 =sgn(c i ), sgn(·) is the sign function; k1, k2 are the observer coefficients, which need to satisfy the following conditions: In the formula, ρ is an arbitrary constant between 0 and 1, g is a positive constant; γ0 and γ1 are both hyperparameters, which need to satisfy the condition γ0=(1+3γ1) / (1-ρ)>1; The controller is designed using the observed speed values ​​instead of the actual speed values ​​to ensure that multiple unmanned surface vessels can still eventually form the desired formation even if the speed information of the lead unmanned surface vessel is lost.

4. The unmanned surface vessel tracking and control method based on absolute position awareness without velocity measurement according to claim 3, characterized in that, Step S3 includes: S31, reconstruct the kinematic and dynamic system model of the position error of the following unmanned surface vessel i; Based on the system state information of the lead unmanned surface vessel (USV), a USV following error system is constructed; the trajectory of the following USV i is defined as follows: Meanwhile, the desired path of unmanned surface vessel i is defined as follows: The virtual reference trajectory of unattended vessel i is then: In the formula, Λ represents the trajectory tracking error of the unmanned surface vessel i. i The diagonal parameter matrix to be designed is used to define a new state variable s. i ,get The position error dynamics are then expressed as: In the formula, It is a function used for system stability, υ i,0 This represents the expected speed at which the unmanned surface vessel i is being followed; The dynamics of following the unmanned surface vessel i are obtained: In the formula, also, S32, based on the formation tracking error system model, designs a multi-unmanned surface vessel auxiliary controller based on Lyapunov functions; The ideal auxiliary controller is designed as follows: in, k p,i1 ,k p,i2 ,k p,i3 It is a positive parameter; Chinese K d,i1 ,k d,i2 ,k d,i3 It is a positive number. For ideal control input.

5. The unmanned surface vessel tracking and control method based on absolute position awareness without velocity measurement according to claim 4, characterized in that, Step S4 includes: The optimization problem is as follows: In the formula, S(δ) represents a family of piecewise constant functions with a sampling period of δ, and s∈[t k ,t k+T ]and It is an unmanned surface vessel (USV) in t k Control input at any given time and optimal control input; It is the control input calculated by the auxiliary controller; V i It is a Lyapunov function. Indicates control input as The stability of the unmanned surface vessel system i is expressed by the following formula: also, X represents the optimal solution to the above optimization problem; i (t k ) represent the actual state of unmanned surface vessel i and , respectively. Optimal state; Equation ① represents the system model constraints of unmanned surface vessel i, used for predicting the state of unmanned surface vessel i; Equation ② represents t k The initial state condition constraints at time i are given by equation ③, which represents the control input constraints of the unmanned surface vessel i, and equation ④, which represents the stability constraints based on the control input calculated by the auxiliary controller and the relevant Lyapunov function.

6. A tracking and control system for unmanned surface vessels based on absolute position perception without speed measurement, characterized in that, The method for tracking and controlling an unmanned surface vessel based on absolute position awareness without velocity measurement, as described in any one of claims 1-5, includes a model building module, a distributed finite-time observer module, an auxiliary control module, and a distributed model predictive control module. Model building module: Obtain absolute position perception and positioning information of the following and leading unmanned surface vessels (USVs), establish the motion equations for formation tracking, and build a three-degree-of-freedom dynamic model of the following USVs based on these equations; Distributed finite-time observer module: Utilizing the inter-vessel communication topology and the position perception and positioning information of the pilot unmanned surface vessel, a distributed finite-time velocity observer is constructed, and the velocity state of the pilot unmanned surface vessel is estimated in real time under the condition of no direct velocity measurement; Auxiliary controller module: Based on the estimation results of the observer, the ideal auxiliary control law for following the unmanned surface vessel is designed using the backstepping method; Distributed Model Predictive Control Module: Based on a distributed Lyapunov function predictive control model, and combined with evaluation functions and constraints, the predictive control input is optimized online to obtain the optimal control input that satisfies stability constraints.

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