A USV Trajectory Tracking Control Method with Obstacle Avoidance Function Introduced

Abstract

The present invention relates to the technical field of unmanned surface vehicle (USV) control, and particularly to a USV trajectory tracking control method with an obstacle avoidance function. The method includes obtaining a nonlinear control system model according to the mathematical model of the USV's surface motion; constructing a prediction model of the nonlinear control system based on the nonlinear control system model; defining obstacle avoidance rules; setting a cost function of the prediction model; and obtaining the predicted state at the next moment by using the prediction model based on the obstacle avoidance rules and minimizing the cost function within the prediction time domain according to the input of the nonlinear control system at the current moment. The present invention conducts research on the trajectory tracking control method of USV based on a model predictive control method and introduces an obstacle avoidance function, that is, when the preset trajectory encounters an obstacle, a corresponding improvement scheme is proposed for this method.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned surface vehicle control, and particularly to a USV trajectory tracking control method with an obstacle avoidance function introduced. Background Art

[0002] As an important part of modern marine technology, unmanned surface vessels (USVs) demonstrate extensive application value. In terms of marine monitoring, unmanned vessels can autonomously perform tasks for a long time and collect marine environmental data in real time, which helps with climate change research and marine ecological protection. In addition, in maritime traffic management, unmanned vessels have efficient cruising capabilities, can effectively monitor ship dynamics, and prevent accidents. In search and rescue operations, surface unmanned vessels, relying on their flexible and rapid response characteristics, can quickly reach the accident site and provide timely assistance. When a USV performs a task, it often needs to move along a preset trajectory or preset trajectory points. The accuracy of trajectory tracking not only improves the operation efficiency of the unmanned vessel but also enhances its reliability in complex tasks, laying a solid foundation for the intelligent and automated development of future marine operations. Therefore, it is very necessary to study the trajectory tracking problem of USVs.

[0003] During the movement of a USV, it may encounter obstacles on the water surface, which can cause very significant losses to the USV. However, in existing research, the trajectory tracking technology of USVs is often studied as an independent problem and rarely combined with the obstacle avoidance problem. Therefore, how to combine USV trajectory tracking and obstacle avoidance problems is particularly important. Summary of the Invention

[0004] To solve the deficiencies in the above background art, the present invention provides a USV trajectory tracking control method with an obstacle avoidance function introduced. This method conducts research on the USV trajectory tracking control method based on the model predictive control method, introduces the obstacle avoidance function, combines USV trajectory tracking and obstacle avoidance, enabling the USV to effectively avoid obstacles during navigation and prevent collisions with obstacles.

[0005] The first object of the present invention is to provide a USV trajectory tracking control method with an obstacle avoidance function introduced, including:

[0006] Construct a mathematical model of the underactuated USV moving on the water surface;

[0007] Obtain a nonlinear control system model according to the mathematical model of the USV moving on the water surface;

[0008] Construct a prediction model of the nonlinear control system according to the nonlinear control system model;

[0009] Set the reference state of the target point, as well as the position and size information of the obstacles;

[0010] Define the obstacle avoidance rules according to the current position of the USV and the position of the obstacles;

[0011] Set the cost function of the prediction model according to the current prediction state of the prediction model and the reference state;

[0012] When minimizing the cost function within the prediction time domain based on the obstacle avoidance rules through the prediction model according to the input of the nonlinear control system model at the current moment, obtain the prediction state at the next moment; wherein, the prediction time domain refers to the time period between the current moment and the next moment;

[0013] Control the USV to navigate according to the prediction state at the next moment.

[0014] Preferably, the obstacle avoidance rules are as follows:

[0015]

[0016] In the formula, ( x ( t ) ，y ( t )) is the position coordinate of the USV in the global coordinate system; ( x ob ( t ), y ob ( t )) is the position coordinate of the geometric center of the obstacle; L A is the total length of the USV; L safe is the safe distance that needs to be maintained between the USV and the obstacle; L ob is the longest distance from the geometric center position of the obstacle to the edge of the obstacle.

[0017] Preferably, when obtaining the prediction state at the next moment, it includes:

[0018] The input of the nonlinear control system at the current moment includes the current state of the system and the control sequence obtained according to the current state within the prediction time domain; wherein, the control sequence refers to the control vector of the USV at each time point in the prediction time domain;

[0019] Obtain the prediction state corresponding to each control vector according to the current state of the system and each control vector in the control sequence through the prediction model;

[0020] Based on the obstacle avoidance rules, minimize the cost function according to the deviation between the current predicted state and the reference state, and obtain the control vector corresponding to the minimization;

[0021] Obtain the predicted state at the next moment through the prediction model according to the current state of the system and the control vector corresponding to the minimization.

[0022] Preferably, the cost function is as follows:

[0023]

[0024] In the formula, , τ represents the differential symbol with respect to the time variable; Δ T represents the prediction horizon; represents the system input at time τ; represents the current predicted state of the prediction model and the reference state the deviation between; Q and R respectively represent positive definite symmetric weight diagonal matrices.

[0025] Preferably, construct a mathematical model for the underactuated USV surface motion, including the kinematic model of the USV on the horizontal plane and the dynamic model on the horizontal plane.

[0026] Preferably, the non-linear control system model is as follows:

[0027]

[0028] In the formula, t 0 is the initial time of the system; δ ( t ) is the system input; λ ( t ) is the system state; is the first derivative of the system state; f () is the non-linear system function model.

[0029] Preferably, the prediction model of the non-linear control system is as follows:

[0030]

[0031] In the formula, λ ( k ) is the system state; δ ( k ) is the system input; F () is the non-linear function in the predicted system output at the k + i th moment; i represents 1, 2, ……,n ; represents the predicted state output by the prediction model.

[0032] Preferably, after obtaining the predicted state of the next moment, it further includes:

[0033] Obtain the prediction error by detecting the current output of the USV horizontal motion model system and the predicted state of the next moment; correct the predicted state of the system according to the prediction error;

[0034] Control the USV to navigate according to the corrected predicted state.

[0035] The present invention has at least the following beneficial effects:

[0036] The present invention provides a USV trajectory tracking control method introducing an obstacle avoidance function. This method first constructs the horizontal motion model of the underactuated USV; secondly, designs the control method of the underactuated USV based on this model, and on this basis, introduces the obstacle avoidance function, combines the USV trajectory tracking with obstacle avoidance, so that the USV can effectively avoid obstacles during navigation and avoid collisions with obstacles. The content of the present invention is simulated in the scenario where an obstacle is encountered on the preset trajectory, and the effectiveness of the method of the present invention is verified.

[0037] The USV trajectory tracking model prediction method designed by the present invention does not need to construct an accurate non-linear model, effectively solves the problem of difficult derivative calculation in the process of solving the optimization problem, and only needs the past input-output information and the current system state information to predict the output information of the next moment according to the input information of the next moment.

[0038] By introducing the obstacle avoidance mechanism, the present invention effectively combines the USV trajectory tracking with obstacle avoidance, which better meets the needs of actual engineering. The method of the present invention is combined with the actual kinematics and dynamics models of the USV, which is more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the flow chart of the technical solution of the invention.

[0040] Figure 2 is the schematic diagram of USV motion obstacle avoidance.

[0041] Figure 3 is the simulation result diagram of the USV running trajectory.

[0042] Figure 4 is the diagram of the actual speed and angular velocity change of the USV.

[0043] Figure 5 is the diagram of the actual control input change of the USV. DETAILED DESCRIPTION OF THE INVENTION

[0044] To illustrate the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following detailed description will be given in conjunction with embodiments.

[0045] The purpose of the present invention is to make up for the deficiencies of the existing technology and introduce an obstacle avoidance function into the trajectory tracking control method of an unmanned surface vehicle (USV). When the USV is performing tasks, it usually moves along a preset trajectory. Therefore, the research on the trajectory tracking of a single USV is crucial. As Figure 1 shown in the flowchart of, the present invention conducts research on the trajectory tracking control method of USV based on a model predictive control method and introduces an obstacle avoidance function, that is, when the preset trajectory encounters an obstacle, a corresponding improvement scheme is proposed for this method. First, construct the horizontal plane motion model of the underactuated USV; secondly, design the trajectory tracking method of the underactuated USV based on the model predictive method, and introduce the obstacle avoidance function on this basis; finally, simulate the content of the present invention in the scenario where the preset trajectory encounters an obstacle, and verify the effectiveness of the method of the present invention.

[0046] To achieve the above purpose, the present invention provides a USV trajectory tracking control method introducing an obstacle avoidance function, including:

[0047] Construct a mathematical model of the underactuated USV moving on the water surface;

[0048] Among them, constructing the mathematical model of the underactuated USV moving on the water surface includes the kinematic model of the USV on the horizontal plane and the dynamic model on the horizontal plane.

[0049] Obtain a nonlinear control system model according to the mathematical model of the USV moving on the water surface;

[0050] Among them, the nonlinear control system model is as follows:

[0051]

[0052] In the formula, t 0 is the initial moment of the system; δ ( t ) is the system input; λ ( t ) is the system state; is the first derivative of the system state; f () is the nonlinear system function model.

[0053] Construct a prediction model of the nonlinear control system according to the nonlinear control system model;

[0054] Among them, the prediction model of the nonlinear control system is as follows:

[0055]

[0056] In the formula, λ ( k ) is the system state; δ ( k ) is the system input; F () is the non - linear function in the predicted system output at the k + i th moment; i denotes 1, 2, ……, n ; denotes the predicted state output by the prediction model.

[0057] Set the reference state of the target point, as well as the position and size information of the obstacle;

[0058] Define the obstacle avoidance rules according to the current position of the USV and the position of the obstacle;

[0059] Among them, the obstacle avoidance rules are as follows:

[0060]

[0061] In the formula, ( x ( t )) is the position coordinate of the USV in the global coordinate system; ( ，y ( t )) is the geometric center position coordinate of the obstacle; x ob ( t ), y ob ( t )) is the geometric center position coordinate of the obstacle; L A is the total length of the USV; L safe is the safe distance that needs to be maintained between the USV and the obstacle; L ob is the longest distance from the geometric center position of the obstacle to the edge of the obstacle.

[0062] Set the cost function of the prediction model according to the current predicted state and the reference state of the prediction model;

[0063] Among them, the cost function is as follows:

[0064]

[0065] In the formula, , τ denotes the differential symbol with respect to the time variable; Δ T denotes the prediction time domain; denotes the system input at the τth moment; denotes the current predicted state of the prediction model and the reference state The deviation between; Q and R respectively represent positive definite symmetric weight diagonal matrices.

[0066] Based on the input of the nonlinear control system model at the current moment through the prediction model, based on the obstacle avoidance rule, and minimizing the cost function within the prediction time domain, the predicted state at the next moment is obtained; wherein, the prediction time domain refers to the time period between the current moment and the next moment;

[0067] Control the USV to navigate according to the predicted state at the next moment.

[0068] When obtaining the predicted state at the next moment, it includes:

[0069] The input of the nonlinear control system at the current moment includes the current state of the system and the control sequence within the prediction time domain obtained according to the current state; wherein, the control sequence refers to the control vector of the USV at each time point within the prediction time domain;

[0070] Obtain the predicted state corresponding to each control vector through the prediction model according to the current state of the system and each control vector in the control sequence;

[0071] Based on the obstacle avoidance rule, minimize the cost function according to the deviation between the current predicted state and the reference state, and obtain the control vector corresponding to the minimization;

[0072] Obtain the predicted state at the next moment through the prediction model according to the current state of the system and the control vector corresponding to the minimization.

[0073] After obtaining the predicted state at the next moment, it further includes:

[0074] Obtain the prediction error by detecting the current output of the USV horizontal motion model system and the predicted state at the next moment; correct the predicted state of the system according to the prediction error;

[0075] Control the USV to navigate according to the corrected predicted state.

[0076] To further illustrate the control method provided by the present invention, it is described in conjunction with the accompanying drawings.

[0077] See Figure 1 As shown, a USV trajectory tracking control method introducing an obstacle avoidance function includes:

[0078] Step 1: Construct the mathematical model of the underactuated USV surface motion

[0079] Construct the mathematical model of the underactuated USV surface motion, including the kinematic model of the USV on the horizontal plane and the dynamic model on the horizontal plane.

[0080] Step 1-1: Establish the kinematic and dynamic models of the underactuated USV on the horizontal plane;

[0081] The USV is a surface ship that can move in a two-dimensional plane. In order to study the motion law of the USV with three degrees of freedom on the horizontal plane, the longitudinal velocity, lateral velocity, and yaw angular velocity are used to describe the horizontal motion of the USV. Then, the kinematic model of the USV on the horizontal plane is:

[0082] (1)

[0083] In the formula, x and y are the coordinates of the USV in the global coordinate system; ψ is the heading angle, u , v , r are the longitudinal velocity, lateral velocity, and yaw angular velocity of the USV respectively; The dynamic model of the USV on the horizontal plane is

[0084] (2)

[0085] In the formula, X is the forward thrust of the USV drive device; N is the yaw moment; By adjusting these two variables, the motion of the USV on the horizontal plane is controlled. Since the USV is an underactuated surface ship, the lateral thrust Y = 0; m 11 , m 22 , m 33 represent the generalized mass of the USV including the added mass; d 11 = - X u - X u|u| | u |, d 22 = - Y v - Y v|v| | v |, d 33 = - N r - N r|r| | r |; X u , Y v , Nr is the linear damping coefficient, X u|u| and Y v|v| and N r|r| are the quadratic damping coefficients.

[0086] Step 1-2: Obtain the nonlinear control system model according to the mathematical model of the USV surface motion;

[0087] Specifically, rewrite the horizontal plane model of the USV motion into the form of a nonlinear control system model, denoted as λ (t)= x , y , ψ , u , v , r T is the state vector of the USV at t time, δ (t)= X , N T is the control input vector. Since the horizontal plane model of the USV motion is a nonlinear control, the nonlinear control system model is as follows:

[0088] (3)

[0089] In the formula, t 0 is the initial time of the system; δ ( t ) is the system input; λ ( t ) is the system state; is the first derivative of the system state; f () is the nonlinear system function model.

[0090] Step 2: Model predictive controller design

[0091] Step 2-1: Prediction model

[0092] Construct a prediction model of the nonlinear control system according to the nonlinear control system model;

[0093] In this embodiment, the state equation of the nonlinear control system model at the k time is as follows:

[0094] (4)

[0095] In the formula, δ ( k ) is the system input; Y (​​k ) is the system output; λ ( k ) is the system state; is the mapping function from the system state to the system output.

[0096] According to the model predictive control theory, at the k moment, the initial state of the nonlinear control system λ ( k ) and the current and future system inputs of the system δ ( k ), δ ( k + 1), ……, to construct the prediction model of the nonlinear control system is as follows:

[0097] (5)

[0098] According to Equation (5), starting from i = 1 and recursively deriving, the system output at k + i moment is:

[0099] (6)

[0100] It should be noted that when solving the optimization objective problem, it is often necessary to take the derivative to solve. However, the nonlinear function k + i in the system output at F moment is composed of two nonlinear functions. When taking the derivative, it is often difficult or even impossible to obtain the exact derivative form. And this prediction model often does not require the exact form, only the past input, output information and the current system state information, and can predict the output information at the next moment according to the input information at the next moment.

[0101] In the formula, F () is a nonlinear function composed of the function f () and the function g (). It can be seen from Equation (6) that the system output of the nonlinear system at any future moment is only related to the current state and input of the system and the control input at the future moment.

[0102] Step 2 - 2: Define the obstacle avoidance rules

[0103] Set the reference state of the target point, as well as the position and size information of the obstacle;

[0104] Define the obstacle avoidance rules according to the current position of the USV and the position of the obstacle;

[0105] Such as Figure 2As shown in the figure, in the process of trajectory tracking, the USV may encounter obstacles. Now, the obstacle avoidance problem of the USV is described as follows: Assuming that the geometric center of the obstacle is known, it is necessary to ensure that every point on the running trajectory of the USV maintains a safe distance from the obstacle. Therefore, this problem is transformed into an inequality constraint form in the optimization problem of the prediction model. The specific obstacle avoidance rules are as follows:

[0106] (7)

[0107] In the formula, ( x ( t ) ，y ( t )) is the position coordinate of the USV in the global coordinate system; ( x ob ( t ), y ob ( t )) is the position coordinate of the geometric center of the obstacle; L A is the total length of the USV; L safe is the safe distance that needs to be maintained between the USV and the obstacle; L ob is the longest distance from the geometric center position of the obstacle to the edge of the obstacle.

[0108] Step 2-3: Rolling optimization

[0109] Set the cost function of the prediction model according to the current prediction state and reference state of the prediction model;

[0110] It should be noted that for the prediction model of the non-linear control system, that is, the trajectory tracking prediction model, its control is to solve an optimal control problem in the receding time domain under constraint conditions, aiming to determine the next acceptable control behavior. The algorithm requires that at t time, the state of the system λ (t) can be measured. Based on λ (t), the control sequence (δ*(t), δ*(t + 1), …, δ*(t + P - 1)) within the prediction time domain P can be calculated, and then the first element δ*(t) of the control vector is applied in the system. The above process is continuously repeated at all future times. Among them, a predicted control vector is output at each time point. The goal of this control algorithm is to minimize the following cost function, where the cost function is as follows:

[0111] (8)

[0112] In the formula, , where τ represents the differential symbol with respect to the time variable, and ΔT represents the prediction horizon, represents the system input at time τ, represents the current predicted output of the prediction model and the reference output the deviation between; Q W×W and R N×N represent positive definite symmetric weight diagonal matrices respectively.

[0113] As described in Step 1, for the horizontal motion model of the underactuated USV, the dimension of the state variable is 6, so W = 6; the dimension of the control variable is 2, so N = 2.

[0114] Based on the input of the nonlinear control system at the current moment through the prediction model, based on the obstacle avoidance rules, and minimizing the cost function within the prediction horizon, the predicted state at the next moment is obtained; among them, the prediction horizon refers to the time period between the current moment and the next moment;

[0115] When obtaining the predicted state at the next moment, it includes:

[0116] The input of the nonlinear control system at the current moment includes the current state of the system and the control sequence within the prediction horizon obtained according to the current state; among them, the control sequence refers to the control vector of the USV at each time point within the prediction horizon;

[0117] According to the current state of the system and each control vector in the control sequence, the predicted state corresponding to each control vector is obtained through the prediction model;

[0118] Based on the obstacle avoidance rules, the cost function is minimized according to the deviation between the current predicted state and the reference state, and the control vector corresponding to the minimization is obtained;

[0119] According to the current state of the system and the control vector corresponding to the minimization, the predicted state at the next moment is obtained through the prediction model.

[0120] Specifically, the obstacle avoidance constraint (7) in Step 2-2 is added to the cost function (8), that is, the design of the USV trajectory tracking model predictive controller with obstacle avoidance function is completed.

[0121] Step 3: Feedback correction

[0122] According to the present invention, after obtaining the predicted state at the next moment, it further includes:

[0123] The prediction error is obtained by detecting the current output of the USV horizontal motion model system and the predicted state at the next moment; the predicted state of the system is corrected according to the prediction error;

[0124] Control the USV to navigate according to the corrected predicted state.

[0125] During the feedback correction process, by detecting the current output of the USV horizontal motion model system and feeding it back to the predicted output for correction, the current k prediction error at the current moment is expressed as

[0126] (9)

[0127] In the formula, e ( k ) is the output prediction error at the current moment, Y ( k ) is the actual output of the system at the current moment, ( k | k -1) is the predicted output at the k th moment.

[0128] Then, according to the error information at the k th moment, predict the error at the k + i th moment, and the prediction error can be expressed as

[0129] (10)

[0130] In the formula, E i () is the error function at the k + i th moment, l is the length of the historical error moment.

[0131] Then, correct the predicted output formula (6) of the system through the prediction error formula (10) to obtain the following feedback prediction of the output

[0132] (11)

[0133] Substitute into in formula (8) to achieve feedback correction.

[0134] To further illustrate a USV trajectory tracking control method with an obstacle avoidance function provided by the present invention, specific embodiments are now given to verify the effect of the technical solution of the present invention:

[0135] Now assume that the USV starts from the starting point, and the initial state λ 0(t)= x 0, y 0, ψ 0, u 0, v 0, r 0] T=[0,0,0,0,0,0] T , the reference state of the target point to be finally reached Y d (t)= x d , y d , ψ d , u d , v d , r d T =[70,70,pi / 4,1.5,0,0] T . The obstacle is assumed to be circular, and its geometric center is the center of the circle, with coordinates ( x ob (t), y ob (t)) set to (30,30), and the radius L ob is set to 2m. The safety distance that the USV needs to maintain from the obstacle L safe = 3m. Let the total length of the USV L A be 3m, and the thrust of the thruster X and the yaw moment Z have upper and lower limits, and the range is set to ±20N(m).

[0136] The sampling period of the algorithm optimization parameter is 0.2s, and the prediction horizon length P = 12, that is, the prediction time is obtained as 2.4s. The diagonal matrix Q 6×6 = diag(0.001,0.001,0.001,100,100,10), R 2×2 = diag(0.001,0.001).

[0137] The simulation result diagram of the USV obstacle avoidance is as shown in Figure 3 . During the simulation process, the speed and angular velocity changes of the USV are as shown in Figure 4 . Among them, Figure 4 a in it is the longitudinal speed change of the USV, Figure 4 b in it is the lateral speed change of the USV, Figure 4 c in it is the yaw angular velocity change of the USV. It can be seen that when the USV approaches the obstacle, the speed and angular velocity change to avoid the obstacle, and then move forward towards the target point. Finally, the position of the USV coincides with the target point. During the simulation process, the forward force and moment of the USV thruster are as shown in Figure 5 . Among them,​Figure 5 where a is the change in the forward thrust of the USV, Figure 5 and b is the change in the yaw moment of the USV. The position change of the USV is achieved by adjusting the two-dimensional control variables, and the simulation results verify the effectiveness and correctness of the present invention.

[0138] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A USV trajectory tracking control method introducing an obstacle avoidance function, characterized in that: include: Construct a mathematical model of underactuated USV motion on the water surface; Obtain a nonlinear control system model based on the mathematical model of USV surface motion; Construct a prediction model of nonlinear control system based on the nonlinear control system model; Set the reference state of the target point, as well as the location and size information of the obstacle; Define obstacle avoidance rules based on the current location of the USV and the location of the obstacle; Setting the cost function of the prediction model according to the current prediction state and the reference state of the prediction model; According to the input of the nonlinear control system model at the current moment, through the prediction model, based on the obstacle avoidance rule, and when the cost function is minimized within the prediction time domain, the prediction state at the next moment is obtained; wherein the prediction time domain refers to the period between the current moment and the next moment; Control the USV to navigate according to the predicted state at the next moment; The obstacle avoidance rules are as follows: In the formula, ( x ( t ) ，y ( t )) is the position coordinate of USV in the global coordinate system; ( x ob ( t ), y ob ( t )) is the geometric center coordinate of the obstacle; L A is the total length of the USV; L safe The safe distance that needs to be maintained between the USV and obstacles; L ob is the longest distance between the geometric center of the obstacle and the edge of the obstacle; When obtaining the predicted status of the next moment, it includes: The input of the nonlinear control system at the current moment includes the current state of the system and the control sequence within the prediction time domain obtained according to the current state; wherein the control sequence refers to the control vector of the USV at each time point in the prediction time domain; According to the current state of the system and each control vector in the control sequence, the prediction state corresponding to each control vector is obtained through the prediction model; Based on the obstacle avoidance rule, the cost function is minimized according to the deviation between the current predicted state and the reference state, and the control vector corresponding to the minimization is obtained; According to the current state of the system and the control vector corresponding to the minimization, the predicted state at the next moment is obtained through the prediction model; The cost function is as follows: In the formula, , τ Indicates the differential symbol with respect to the time variable; Δ T represents the prediction time domain; represents the system input at time τ; Indicates the current prediction status of the prediction model and reference state The deviation between Q and R denote positive definite symmetric weight diagonal matrices respectively.

2. The USV trajectory tracking control method with obstacle avoidance function according to claim 1 is characterized in that: A mathematical model of the underactuated USV's motion on the water surface is constructed, including the kinematic model and the dynamic model of the USV on the horizontal plane.

3. The USV trajectory tracking control method with obstacle avoidance function introduced in claim 1 is characterized in that: The nonlinear control system model is as follows: In the formula, t 0 is the initial time of the system; δ ( t ) is the system input; λ ( t ) is the system status; is the first-order derivative of the system state; f () is the nonlinear system function model.

4. The USV trajectory tracking control method with obstacle avoidance function introduced in claim 1 is characterized in that: The prediction model of the nonlinear control system is as follows: In the formula, λ ( k ) is the system status; δ ( k ) is the system input; F ( ) is the prediction k+i Nonlinear functions in the system output at time instant; i represents 1, 2, ..., n ; Represents the prediction status of the prediction model output.

5. The USV trajectory tracking control method with obstacle avoidance function introduced in claim 1 is characterized in that: After obtaining the predicted state of the next moment, it also includes: The prediction error is obtained by detecting the current output of the USV horizontal motion model system and the predicted state at the next moment; and the predicted state of the system is corrected according to the prediction error; The USV is controlled to navigate according to the corrected predicted state.

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