An underactuated circular disc type AUV path tracking method based on extended line-of-sight guidance, program, device and storage medium

Abstract

The application discloses an underactuated circular disc type AUV path tracking method based on extended line-of-sight guidance, a program, equipment and a storage medium, and belongs to the underwater unmanned vehicle path tracking field. First, a tracking error model in a horizontal plane path tracking problem is established; second, a virtual control item is introduced and compensated into a path tracking algorithm, and an estimated value of a total uncertainty item containing external interference and kinematic modeling error is introduced into the virtual control item; external interference such as sea current and uncertainty such as kinematic modeling error are estimated through a linear extended state observer. Finally, an improved path tracking algorithm outputs a desired heading angle, and accurate tracking of a target path is realized. The application proves the input state stability of a path tracking closed-loop system by using an input state stability theorem and a cascade system theory, and the effectiveness and superiority of the improved path tracking algorithm are verified through simulation experiments. The application solves the path tracking control problem of the underwater unmanned vehicle.

Description

Technical Field

[0001] This invention relates to the field of underwater unmanned vehicle path tracking, and in particular to a path tracking method, program, device and storage medium for underactuated disc-type AUVs based on extended line-of-sight guidance. Background Technology

[0002] Good path tracking capability is a fundamental technical prerequisite for underwater vehicles to complete underwater operations. However, in operations on complex terrains such as the seabed, the working paths of underwater unmanned vehicles are prone to complex curves and are easily affected by unknown disturbances such as ocean currents, which places higher demands on the path tracking control of underwater unmanned vehicles. Therefore, designing path tracking algorithms tailored to the motion characteristics of underwater unmanned vehicles to enable them to accurately track preset paths is of great significance.

[0003] To simplify controller design, the path tracking problem can be decomposed into kinematic and dynamic control problems. At the kinematic control level, the controller only needs to consider the submersible's position and orientation information, and design a guidance law based on the preset desired path information to calculate the submersible's desired attitude angle and velocity at that moment. At the dynamic control level, the controller needs to consider the submersible's dynamic characteristics and adjust the submersible's attitude angle and velocity by controlling the thruster output to meet the desired values ​​at the kinematic level, thus achieving path tracking control. Line-of-sight (LAS) guidance is one of the most commonly used guidance laws in path tracking algorithms. It is a geometrically based guidance method. Its core idea is to use the line connecting the submersible's current position to a point on the desired path as the guidance direction, generating a desired heading angle to achieve path tracking. LAS does not rely on the submersible's system model; it only requires knowledge of the desired path and the submersible's actual position. It is insensitive to high-frequency white noise, has few design parameters, is computationally simple, and is easy to implement. Therefore, it is widely used in submersible path tracking control.

[0004] Classical path-following algorithms are simple and effective in calm water environments, but when a vehicle is subjected to unknown disturbances such as ocean currents, it often deviates from the desired path. This is because the vehicle generates a sideslip angle when subjected to unknown lateral disturbance forces. Since underactuated vehicles cannot directly counteract lateral disturbance forces, the path-following problem under the presence of sideslip angles must be considered.

[0005] Line-of-sight guidance methods often only estimate the sideslip angle or ocean currents, failing to provide suitable solutions for kinematic model errors and unknown environmental disturbances. This invention proposes an improved path tracking algorithm based on an extended state observer. This invention estimates uncertainties such as external disturbances like ocean currents and kinematic modeling errors using a linear extended state observer, compensating for these uncertainties with virtual control terms in the path tracking algorithm, thus designing an improved path tracking algorithm based on an extended state observer. Then, the stability of the input state of the path tracking closed-loop system is proven using the input state stability theorem and cascade system theory. Finally, simulation experiments are used to verify the effectiveness and superiority of the proposed improved path tracking algorithm. This invention solves the path tracking control problem for underwater unmanned vehicles. Summary of the Invention

[0006] To address the shortcomings of existing technologies that cannot estimate kinematic model errors and unknown environmental interference, thus affecting tracking accuracy and rapid response, this invention provides an underactuated disc-type AUV path tracking method based on extended line-of-sight guidance.

[0007] This invention provides a path tracking method for an underactuated disc-type AUV based on extended line-of-sight guidance, characterized by comprising the following steps:

[0008] Obtain the current position and desired path information of the vehicle, and establish a tracking error model for the horizontal path tracking problem;

[0009] The design incorporates a virtual control term compensation into the path tracking algorithm. This virtual control term includes an estimate of the total uncertainty term, which incorporates external disturbances and kinematic modeling errors. The total uncertainty term is estimated using a linear extended state observer. The improved path tracking algorithm outputs the desired heading angle, enabling accurate tracking of the target path.

[0010] Furthermore, the underwater unmanned vehicle reaches a point on the two-dimensional parameterized path of the desired path. The orthogonal distance at the location is used to establish the lateral tracking error. for:

[0011]

[0012] in, Path variables representing the desired path; Representing path points Path tangent angle at the location; This indicates the position coordinates of the underwater unmanned vehicle; This represents the expected path in a two-dimensional parameterized system, defined by path variables. The coordinates of the determined path points;

[0013]

[0014] in, , This represents the expected path in a two-dimensional parameterized system, defined by path variables. The coordinates of the determined path points.

[0015] Furthermore, the virtual control term compensation introduced into the path tracking algorithm is as follows:

[0016]

[0017] in, Indicates the forward sight distance; Indicates a virtual control item; Indicates the desired heading angle; Representing path points Path tangent angle at the location; This indicates the lateral tracking error.

[0018] Furthermore, combining the horizontal kinematic equations of the underwater unmanned vehicle, a tracking error variation rate, incorporating the total uncertainty term including external disturbances and kinematic modeling errors, is constructed; to offset the tracking error variation rate... Design virtual control terms for the total uncertainty term. for:

[0019]

[0020] in, This represents the estimated horizontal speed. This represents the estimated value of the total uncertainty term.

[0021] Furthermore, the linearly extended state observer is designed as follows:

[0022]

[0023] in, Indicates lateral tracking error The rate of change of the estimated value; Represents the total uncertainty term The rate of change of the estimated value; Indicates the lateral tracking error The estimate; For the total uncertainty term The estimate; and Indicates the estimated gain; ;

[0024] estimation error , The estimation error dynamic is:

[0025]

[0026] in, Indicates lateral tracking error The rate of change of the estimation error; Indicates the total uncertainty term The rate of change of the estimation error.

[0027] Furthermore, the horizontal kinematic equations of the underwater unmanned vehicle are as follows:

[0028]

[0029]

[0030] in, This indicates the longitudinal velocity of the underwater unmanned vehicle; This indicates the lateral velocity of the underwater unmanned vehicle; Indicates the heading angle of an underwater unmanned vehicle; express The overall kinematic uncertainty in direction; express The overall kinematic uncertainty in direction.

[0031] Furthermore, the rate of change of the tracking error, which includes the total uncertainty term encompassing external disturbances and kinematic modeling errors. for:

[0032]

[0033] in, Represents the total uncertainties. This indicates the actual speed at sea level. This indicates the error due to the lack of modeling of the kinematics of the horizontal plane. Indicates the drift angle. This indicates the heading angle of an underwater unmanned vehicle.

[0034] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance described above.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance described in any of the preceding claims.

[0036] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance as described above.

[0037] The beneficial effects of this invention are as follows:

[0038] This invention addresses the path tracking problem of underwater unmanned vehicles (UAVs) in complex marine environments. It establishes a tracking error model for the horizontal path tracking problem; designs and introduces a virtual control term to compensate for this error in the path tracking algorithm, incorporating an estimate of the total uncertainty term including external disturbances and kinematic modeling errors; estimates uncertainties such as ocean currents and kinematic modeling errors using a linear extended state observer; and proposes an improved path tracking algorithm based on the extended state observer. The stability of the input state of the path tracking closed-loop system is proven using the input state stability theorem and cascade system theory. Simulation experiments verify the effectiveness and superiority of the proposed improved path tracking algorithm, thus solving the path tracking control problem for underwater UAVs. Attached Figure Description

[0039] Figure 1 This is a flowchart of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance according to the present invention;

[0040] Figure 2 This is the desired path diagram for Example 2;

[0041] Figure 3 The simulation results show the trajectory tracking performance of the improved path tracking algorithm; among them, Figure 3 (a) is a horizontal path trajectory diagram. Figure 3 (b) is the lateral tracking error curve. Figure 3 (c) is the curve showing the expected and actual values ​​of the heading angle. Figure 3 (d) represents the heading angle tracking error curve;

[0042] Figure 4 The simulation results show the dynamic response of the closed-loop system for the improved path tracking algorithm; among them, Figure 4 (a) is the trajectory angle curve. Figure 4 (b) is the curve of the uncertain term and its estimated value. Figure 4 (c) is the velocity curve on the horizontal plane. Figure 4 (d) represents the output curve of the horizontal thruster;

[0043] Figure 5 Simulation results of motion stability and drift compensation for the improved path tracking algorithm; among which, Figure 5 (a) is the roll and pitch angle curve. Figure 5(b) is the drift angle curve. Figure 5 (c) is the depth curve. Figure 5 (d) is the virtual control input curve;

[0044] Figure 6 For different Simulation results of the improved path tracking algorithm with improved parameters; among which, Figure 6 (a) is the horizontal path trajectory diagram. Figure 6 (b) is the lateral tracking error curve;

[0045] Figure 7 Simulation results of the improved path tracking algorithm under different estimator gain parameters are presented; among them, Figure 7 (a) is the horizontal path trajectory diagram. Figure 7 (b) is the lateral tracking error curve. Figure 7 (c) is the error curve for estimating the uncertainty term. Figure 7 (d) is the lateral tracking error estimation error curve. Figure 7 (e) represents the desired heading angle curve. Figure 7 (f) is the expected heading angle curve from 0 to 100 s;

[0046] Figure 8 This is a curve representing the direction of ocean currents;

[0047] Figure 9 The simulation results of various path tracking algorithms are compared; among them, Figure 9 (a) is a horizontal path trajectory diagram. Figure 9 (b) is a magnified view of the horizontal path trajectory. Figure 9 (c) is the lateral tracking error curve. Figure 9 (d) is the drift angle curve. Figure 9 (e) represents the desired heading angle curve. Figure 9 (f) is the local magnification curve of the desired heading angle. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings. The embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0049] A path tracking method for an underactuated disc-type AUV based on extended line-of-sight guidance includes the following steps:

[0050] Step 1: Establish a tracking error model for the horizontal path tracking problem;

[0051] Step 2: Incorporate a virtual control term into the path tracking algorithm. The virtual control term includes an estimate of the total uncertainty term, which incorporates external disturbances and kinematic modeling errors. This total uncertainty term is estimated using a linear extended state observer.

[0052] Step 3: Output the desired heading angle using the improved path tracing algorithm to achieve accurate tracking of the target path;

[0053] Step 4: Use the input state stability theorem and cascade system theory to prove the input state stability of the path tracking closed-loop system.

[0054] In step one, the tracking error model is specifically as follows:

[0055] The desired path in curve path tracking control is typically represented as a parameterized geometric curve, allowing... Representing a path variable, consider a two-dimensional parametric path in the horizontal plane. It passes through a series of discrete points along the path. .

[0056] Similar to a straight path, a point on a curved path The path tangent angle at point is

[0057]

[0058] in .

[0059] Assume the position coordinates of the underwater unmanned vehicle are Lateral tracking error is the distance from the underwater unmanned vehicle to a certain point on its path. Orthogonal distance at the location. Lateral tracking error. The expression can be written as:

[0060]

[0061] Expanding the first line will give you a path through. and Normal:

[0062]

[0063] Expanding the second line yields the expression for the lateral tracking error:

[0064]

[0065] In step two, the design of introducing virtual control term compensation into the path tracking algorithm is as follows:

[0066] Considering kinematic modeling errors such as roll and pitch, the horizontal plane kinematic equations of the underwater unmanned vehicle can be written as:

[0067]

[0068]

[0069] in, This indicates the longitudinal velocity of the underwater unmanned vehicle. This indicates the lateral velocity of the underwater unmanned vehicle. Indicates the heading angle of an underwater unmanned vehicle. express Comprehensive kinematic uncertainty in direction, express The overall kinematic uncertainty in direction.

[0070] Differentiating with respect to the given information, we get:

[0071]

[0072] The part inside the brackets can be derived from the equation of the normal. It is 0.

[0073] Based on the path tangent angle, we can obtain:

[0074]

[0075] but It can be written as:

[0076]

[0077] in, This indicates the actual speed at sea level. This indicates the unmodeled error in horizontal plane kinematics such as roll and pitch.

[0078] Because the underwater unmanned vehicle is not equipped with a Doppler log, it cannot measure its own speed and can only estimate its approximate speed based on the set forward thrust. Furthermore, it cannot measure the drift angle. Therefore, it can Rewritten as:

[0079]

[0080] in, This represents the estimated horizontal speed. This represents the total uncertainties.

[0081] Path tracking algorithms typically operate only at the kinematic level, with the goal of... Provide the desired sailing angle so that Converging to a sufficiently small value ,Right now

[0082]

[0083] This invention introduces virtual control term compensation into the improved path tracing algorithm, which is as follows:

[0084]

[0085] in, It is a custom forward look-ahead distance. It is a virtual control input. It is not a physical control input, but a design variable used to adjust the dynamics of the closed-loop system. It also provides integral action to compensate for unknown uncertainties.

[0086] For ease of design, the following assumptions are adopted.

[0087] Assumption 1: The heading angle controller can perfectly track the desired heading angle, making the actual heading angle approach the desired heading angle. .

[0088] Assumption 2: Lateral tracking error Measurable path tangential angle It can be seen that, You can choose for yourself.

[0089] but It can be rewritten as

[0090]

[0091] in .

[0092] To offset Uncertain terms in The design utilizes virtual control input for:

[0093]

[0094] Will As an unknown, solving it can yield a feasible solution.

[0095]

[0096] To ensure To prevent imaginary solutions from occurring, the following condition must be met: .

[0097] Substitute the feasible solution into Then there is

[0098]

[0099] In step two, the specific method for designing the extended state observer is as follows:

[0100] In order to design an extended state observer estimation The following hypotheses are proposed.

[0101] Assumption 3: Uncertainty Both the absolute value of the derivative and the absolute value of the derivative have a positive upper limit of a constant. ,Right now .

[0102] against The extended state observer is designed as follows:

[0103]

[0104] in, Indicates the lateral tracking error The estimate, For the total uncertainty term The estimate, and It is a positive design parameter, representing the estimated gain.

[0105] Define estimation error , The estimation error dynamic is

[0106]

[0107] Theorem 1: If we take , View it as a system state. Treat it as system input, if there is And there exists a positive number. satisfy Therefore, the system is input-state stable;

[0108] prove:

[0109] definition .

[0110] in, This represents the estimation error state vector of the extended observer; This represents the estimated error of the lateral tracking error; This represents the estimation error of the total uncertainty term; Represents the total uncertainty term The derivative with respect to time is used as the input to this error dynamic system.

[0111] Consider Lyapunov candidate functions

[0112]

[0113] in , making

[0114]

[0115] So

[0116]

[0117] in

[0118] make If ,So It's just an increasing function. ,and It has a maximum value .

[0119] definition

[0120]

[0121] Then the second-order principal minor of Q can be rewritten as

[0122]

[0123] In conclusion, if we make Then there exists a positive constant.

[0124]

[0125] Make Q > 0. Then we have

[0126]

[0127] in .

[0128] And because

[0129]

[0130] in .

[0131] So

[0132]

[0133] if Then there is .definition Class function Then there is

[0134]

[0135] choose Class function and .

[0136] The input state is stable, and there exists a... Class function and Class function Make

[0137]

[0138] in, .

[0139] The proof is complete.

[0140] Theorem 2: If we take Consider it as the state of the system. Treat it as system input; if a normal number exists... satisfy:

[0141]

[0142] Therefore, the system is stable in terms of input state.

[0143] prove:

[0144] Consider Lyapunov candidate functions .but The derivative is

[0145]

[0146] if Then there is .definition Class function Then there is

[0147]

[0148] in, Indicates a kind of definition Class function; express The argument of a function; It is a positive number;

[0149] choose Class function .

[0150] The system input state is stable, and there exists a Class function and Class function Make

[0151]

[0152] in, .

[0153] The improved path tracking algorithm design is now complete.

[0154] In step four, the stability of the cascaded system of the extended state observer and the improved path tracing algorithm is proven, and the specific method is as follows:

[0155] Rewrite the extended state observer and guidance law as a cascaded system.

[0156]

[0157] The following theorem describes the stability of the input state of a closed-loop cascaded system.

[0158] Theorem 3: A closed-loop system consisting of the horizontal motion equations of an underwater unmanned vehicle, an extended state observer, a guidance law, and virtual inputs, under the conditions of Theorem 1 and Theorem 2, makes the solution of the cascaded system stable under the input state.

[0159] prove:

[0160] Theorem 2 explains the subsystem (Status is) The stimulus input is The input state is stable; Theorem 1 states that the subsystem is stable. (Status is) The stimulus input is The input state is stable; therefore, the cascaded system (the state is...) The stimulus input is () is stable in its input state. That is, it exists. Class function and Class function Make

[0161]

[0162] in, .

[0163] because quilt Limitations, lateral tracking error and estimation error Both are bounded.

[0164] It is particularly important to note that when hour, and The tracking error is ultimately bounded, and its upper bound can be expressed as:

[0165]

[0166] This means that the control objective has been achieved.

[0167] Example 1

[0168] The method of this invention can be used for path tracking of underwater unmanned vehicles. The method of use is as follows:

[0169] (1) Input status:

[0170] in The coordinates of the current position. Given the desired location coordinates, For the custom forward look-ahead distance, and These are positive design parameters. This refers to the speed of navigation on the horizontal plane.

[0171] (2) The heading angle is calculated as follows:

[0172]

[0173] in

[0174]

[0175] (3) Output: Desired heading angle .

[0176] Example 2

[0177] To verify the effectiveness of the present invention, a simulation experiment of curved path tracking was conducted on an underwater unmanned vehicle in the presence of ocean currents.

[0178] To better verify the guidance law algorithm, the expected depth of the underwater unmanned vehicle was controlled to 0 in the simulation experiment, and only horizontal path tracking simulation experiments were conducted. The expected path points in the simulation experiment were [10,−80]→[180,−50]→[200,−20]→[150,−5]→[100,−20]→[10,−25]→[100,20]→[30,50]→[−25,50]→[−20,30]→[10,0]→[−60,−30]→[−100,−60]. The expected path was a cubic spline interpolated curve path passing through these paths, as shown below. Figure 2 As shown. The desired paths selected in the simulation experiments included both gently curvature and steeply curvature sections, which fully verified the tracking performance of the improved path tracking algorithm on curved paths. In all simulation experiments, the longitudinal thrust was set to a fixed 50N, and the longitudinal velocity eventually stabilized at 1.0397m / s in still water. No speed control was performed in the simulation experiments. The bow angle controller used was an adaptive backstepping sliding mode controller. The forward look distance was consistently selected as [value missing]. , , , .

[0179] The improved path tracing algorithm mainly has three design parameters: observer gain. and Estimated speed The observer gain must satisfy... The estimated speed can be selected as a constant value or the actual speed.

[0180] First, a verification simulation experiment of the improved path tracking algorithm is conducted, as follows:

[0181] The ocean current is set to a magnitude of 0.2 m / s and a direction of 90°, i.e., due east. The design parameters for the improved path-following algorithm are as follows: , , The initial position of the underwater unmanned vehicle is (0, −81), with all other states set to zero. Simulation results are as follows: Figure 3-5 As shown. From Figure 3 As can be seen in (a), the improved path tracking algorithm resulted in good control of the vehicle throughout the entire path tracking process, with the actual trajectory converging well onto the desired trajectory. Combined with... Figure 3 The lateral tracking error results in (b) show that, initially, the lateral tracking error converges quickly to 0. Subsequently, throughout the path tracking process, the lateral tracking error is limited to within 0.4m, and for most of the time, it is limited to within 0.1m. Combined with... Figure 4 (a) The flight path angle of the aircraft and Figure 3 (a) can correlate each point in the vehicle's trajectory with time. It can be found that... Figure 3 The five larger peaks in (b) correspond to points with greater curvature in the curve path, and the highest tracking error peak in the 400-500s range corresponds to the point with the greatest curvature in the curve path (9.4, −25.3), which is consistent with... Figure 3 (c) and Figure 3 The results in (d) also correspond one-to-one. The greater the curvature of the curve path, the more rapidly the desired heading angle changes. However, due to the saturation limitation of the horizontal thrusters, the vehicle cannot immediately track the desired heading angle, thus resulting in a large lateral tracking error. The saturation limitation of the vehicle's horizontal thrusters is from... Figure 4 This can also be seen in (d). Of course, aside from these inflection points with large curvature, the actual heading angle generally tracks the desired heading angle very well for the remaining time period, which conforms to hypothesis one. From Figure 3 (b) and Figure 4As can be seen in (b), the estimated value of the lateral tracking error can basically converge to the true value throughout the entire path tracking stage, while the uncertainty term... The estimated value gradually converges to the true value over time, which demonstrates the effectiveness of the ELOS observer designed in this paper. Figure 4 (c) shows that when a vehicle travels with the current, its speed is faster than in still water, and vice versa. The relationship between the vehicle's direction of travel and the ocean current can also be seen from... Figure 5 As can be seen from the drift angles in (b), the downstream drift angle is positive and the upstream drift angle is negative. It is worth noting that when the lateral tracking error converges to 0, Figure 5 The virtual control input for the time period corresponding to (d) is not zero, which shows that the virtual control input plays a role in offsetting steady-state errors. In addition, it can be found that when the vehicle trajectory is perpendicular to the direction of the ocean current, the lateral tracking error can still converge to near 0, which effectively demonstrates the resistance to currents of the improved path tracking algorithm.

[0182] The above analysis shows that the improved path tracking algorithm still has a strong ability to track curved paths in the presence of ocean currents. However, there will be some tracking error fluctuations when the curvature of the path inflection point is particularly large. In other cases, it performs well.

[0183] Secondly, comparative experiments were conducted on the three design parameters of the improved path tracing algorithm, as follows:

[0184] (one) Parameter testing experiment

[0185] By setting different The parameters are compared with the path tracking performance of the improved path tracking algorithm to verify the results. Impact on the improved path tracking algorithm. The values ​​were set to 0.4, 0.8, 1.2, 1.6, and after first-order inertial filtering, respectively. In this experiment, the starting point of the underwater unmanned vehicle was set as the starting point of the desired path, and other conditions were the same as in the previous experiment. The simulation results are as follows: Figure 6 As shown. Even if the estimated speed is set. With real speed Inconsistency, various Under the given parameters, the improved path tracking algorithm can effectively track the desired path and achieve good path tracking control, which also demonstrates the characteristic that the improved path tracking algorithm does not depend on the actual speed of the vehicle.

[0186] The influence of parameters on the control performance of the improved path tracking algorithm can be seen from... Figure 6 As can be seen from (b), The larger the value, the higher the peak fluctuation of the lateral tracking error at the inflection point of the curved path, which is related to the structure of the improved path tracking algorithm. Upper bound of tracking error This indicates lateral tracking error. The upper boundary was Control, and ,Right now Follow If it increases and increases, then that means... The upper boundary follows It increases and then increases, which explains... Figure 6 In (b), the peak value of the lateral tracking error fluctuation varies with The phenomenon of increasing in size. From Figure 6 Larger ones can also be seen in (b). Parameters also contribute to faster convergence. This is because... , and from dynamic equations It can be seen that, The larger, The faster the convergence speed.

[0187] From the above analysis, it can be seen that, The larger the value, the faster the improved path tracing algorithm converges, but a large value... This can also cause chattering with significant lateral tracking errors, therefore, in practical applications, it is necessary to... The parameter settings are weighed to determine the most suitable parameter values.

[0188] (ii) Estimator Gain and Parameter testing experiment

[0189] Experimental setup for this group , . Set to 0.8, other parameters are the same as... The results are identical to those in the simulation experiment. The simulation results are as follows: Figure 7 As shown. From Figure 7 (c) and Figure 7 As can be seen from (d), the larger the estimator gain, and The faster the convergence to the true value, the faster the lateral tracking error converges, which is closely related to the structure of the improved path tracking algorithm. However, it is worth noting that... Figure 7 (f) shows that a large estimator gain will lead to a large expected heading angle oscillation, which is very detrimental to path tracking. When the heading angle oscillation is too large, the underwater unmanned vehicle may not be able to track the expected heading angle calculated by the improved path tracking algorithm in time, which will cause fluctuations or even instability in the lateral tracking error.

[0190] Finally, a comparative simulation experiment of various path tracking algorithms was conducted, as follows:

[0191] This simulation experiment simulates ocean currents with a fixed magnitude and random direction variations between 60° and 120°. Figure 8 As shown in Table 1, the forward look-ahead distance is set the same for all four controller parameters. The values ​​of other design parameters are shown in Table 1. The integral term gain κ in both the integral path tracking algorithm and the adaptive path tracking algorithm is selected as the parameter that performs best in the simulation experiment.

[0192] Table 1 Parameter Table for Different Path Tracking Algorithms

[0193]

[0194] Simulation results under different path tracking algorithms are as follows Figure 9 As shown. Traditional path tracking algorithms do not consider the influence of drift angle, resulting in significant lateral tracking errors throughout the entire path tracking phase. While integral and adaptive path tracking algorithms effectively reduce lateral tracking errors on most relatively smooth paths, they exhibit large fluctuations in lateral tracking errors at several inflection points with high path curvature. Particularly at the point of maximum curvature (9.4, −25.3) in curved paths, both integral and adaptive path tracking algorithms perform even worse than traditional path tracking algorithms. Figure 9 As shown in (b). This is because traditional path tracing algorithms do not consider the drift angle effect; instead, the expected heading angle output is relatively smooth throughout the entire path tracing phase. In contrast, integral path tracing algorithms and adaptive path tracing algorithms, when the path curvature is large, exhibit significant fluctuations in the output expected heading angle due to the drift angle effect. For example... Figure 9 As shown in (f), when the desired heading angle fluctuates too much, the underwater unmanned vehicle cannot perfectly track the desired heading angle due to the saturation limitation of the heading controller, which leads to a large lateral tracking error, such as... Figure 9 As shown in (c) around 438s. However, the improved path tracking algorithm proposed in this invention has a lateral tracking error of less than 0.4m throughout the entire path tracking process, and remains stable within 0.1m for most of the time period, demonstrating excellent path tracking performance and fully showcasing the superiority of the improved path tracking algorithm. Moreover, it is worth noting that the design parameters of the improved path tracking algorithm in the simulation experiment of this invention are not optimal parameters, while the design parameters of the integral path tracking algorithm and the adaptive path tracking algorithm are relatively optimal parameters selected after multiple adjustments. This also fully demonstrates the significant advantages of the designed improved path tracking algorithm.

[0195] In summary, the improved path tracking algorithm based on the extended state observer proposed in this invention takes into account the total uncertainty, including external disturbances such as ocean currents, kinematic modeling uncertainty, and speed estimation error, and has better path tracking performance in the presence of ocean currents.

[0196] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance described in any of the above embodiments.

[0197] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance described in any of the above embodiments.

[0198] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the underactuated disc-type AUV path tracking method based on extended line-of-sight guidance, which will not be repeated here.

[0199] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0200] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0201] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A path tracking method for an underactuated disc-type AUV based on extended line-of-sight guidance, characterized in that, Includes the following steps: Obtain the current position and desired path information of the vehicle, and establish a tracking error model for the horizontal path tracking problem; The design incorporates a virtual control term compensation into the path tracking algorithm, which includes an estimate of the total uncertainty term that includes external disturbances and kinematic modeling errors. The virtual control term compensation introduced into the path tracking algorithm is as follows: in, Indicates the desired heading angle; Indicates the forward sight distance; Indicates a virtual control item; Representing path points Path tangent angle at the location; Indicates lateral tracking error; Based on the horizontal plane kinematic equations of the underwater unmanned vehicle, a tracking error rate of change, incorporating the total uncertainty term including external disturbances and kinematic modeling errors, is constructed; to offset the tracking error rate of change... Design virtual control terms for the total uncertainty term. for: in, This represents the estimated horizontal speed. This represents the estimated value of the total uncertainty term; The total uncertainty term is estimated using a linear extended state observer; the desired heading angle is output through an improved path tracking algorithm to achieve accurate tracking of the target path.

2. The path tracking method for underactuated disc-type AUVs based on extended line-of-sight guidance according to claim 1, characterized in that, A point on a two-dimensional parameterized path from an underwater unmanned vehicle to the desired path. The orthogonal distance at the location is used to establish the lateral tracking error. for: in, Path variables representing the desired path; This represents the expected path in a two-dimensional parameterized system, defined by path variables. The coordinates of the determined path points; Representing path points Path tangent angle at the location; This indicates the position coordinates of the underwater unmanned vehicle; in, , This represents the expected path in a two-dimensional parameterized system, defined by path variables. The coordinates of the determined path points.

3. The path tracking method for underactuated disc-shaped AUVs based on extended line-of-sight guidance according to claim 1, characterized in that, The linearly extended state observer is designed as follows: in, Indicates lateral tracking error The rate of change of the estimated value; Represents the total uncertainty term The rate of change of the estimated value; Indicates the lateral tracking error The estimate; For the total uncertainty term The estimate; and Indicates the estimated gain; ; estimation error , The estimation error dynamic is: in, Indicates lateral tracking error The rate of change of the estimation error; Indicates the total uncertainty term The rate of change of the estimation error.

4. The path tracking method for underactuated disc-shaped AUVs based on extended line-of-sight guidance according to claim 1, characterized in that, The horizontal kinematic equations of the underwater unmanned vehicle are as follows: in, This indicates the longitudinal velocity of the underwater unmanned vehicle; This indicates the lateral velocity of the underwater unmanned vehicle; Indicates the heading angle of an underwater unmanned vehicle; express The overall kinematic uncertainty in direction; express The overall kinematic uncertainty in direction.

5. The path tracking method for underactuated disc-shaped AUVs based on extended line-of-sight guidance according to claim 1, characterized in that, The rate of change of tracking error, which includes the total uncertainty term including external disturbances and kinematic modeling errors. for: in, Represents the total uncertainties. This indicates the actual speed at sea level. This indicates the error due to the lack of modeling of the kinematics of the horizontal plane. Indicates the drift angle. This indicates the heading angle of an underwater unmanned vehicle.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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