High-precision navigation control methods, devices, equipment and media for underwater self-propelled models

Abstract

The present application relates to the field of underwater self-propelled model control technology, and discloses a high-precision navigation control method, device, equipment and medium for underwater self-propelled models. Among them, the velocity vector of the underwater self-propelled model at the previous moment, the current energy state data and the current operation task type are obtained; weight distribution is performed according to the current energy state data and the current operation task type to obtain a first weight and a second weight; the output of the hard constraint model prediction controller is adjusted using the first weight, and the output of the soft constraint model prediction controller is adjusted using the second weight to obtain the expected speed change at the current moment, and the expected speed change is added to the velocity vector at the previous moment to obtain the current moment velocity vector of the underwater self-propelled model; the velocity error vector between the current moment velocity vector and the reference velocity vector is input into the adaptive terminal sliding membrane controller with finite time convergence to obtain the control rate, so as to perform high-precision navigation control on the underwater self-propelled model.

Description

Technical Field

[0001] The present application relates to the technical field of underwater self-propelled model control, and in particular to a high-precision navigation control method, device, equipment and medium for an underwater self-propelled model. Background Art

[0002] In the related technology, in order to adapt to the needs of different application environments, underwater self-propelled models are designed into two types of configuration modes: underactuated and fully-actuated, and the design of the corresponding motion control methods also has its own characteristics. The characteristic of the underactuated self-propelled model is that the control input is less than the dimension of the configuration space. Its outstanding advantages are that it can reduce the system mass, save development costs, reduce energy consumption, and have high system reliability. Streamlined fully-driven self-propelled models often use orthogonal groove propellers and other methods to maintain the streamline of underwater self-propelled models and achieve the purpose of drag reduction. Its outstanding advantage is good maneuverability under low-speed conditions, which is suitable for fine operations under variable speed and low-speed conditions.

[0003] However, the motion control method of the underwater self-propelled model in the related art needs to be improved in terms of adaptability to different working conditions. Summary of the invention

[0004] The present application provides a high-precision navigation control method, device, equipment and medium for an underwater self-propelled model, which solves the technical problem in the related art that the underwater self-propelled model controller jitters when facing complex working conditions, so that the underwater self-propelled model controller can adapt to various different working conditions when performing complex tasks, thereby improving the adaptability of the underwater self-propelled model controller.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] The present application provides a high-precision navigation control method for an underwater self-propelled model, the method comprising:

[0007] Obtain the previous moment velocity vector, current energy state data and current operation task type of the underwater self-propelled model;

[0008] Performing weight allocation according to the current energy state data and the current job task type to obtain a first weight of a hard constraint model predictive controller and a second weight of a soft constraint model predictive controller;

[0009] The output of the hard constraint model predictive controller is adjusted using the first weight, and the soft constraint model predictive controller is adjusted using the second weight to obtain the expected speed change at the current moment, and the expected speed change is added to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model; wherein there is a speed error vector between the current moment speed vector and the reference speed vector;

[0010] The speed error vector is input into an adaptive terminal sliding film controller with finite time convergence to obtain a control rate so as to perform high-precision navigation control on the underwater self-propelled model; wherein the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in a preset control gain range.

[0011] In a second aspect, an embodiment of the present application provides a high-precision navigation control device for an underwater self-propelled model, the device comprising:

[0012] A data acquisition module is used to obtain the previous moment velocity vector, current energy state data and current operation task type of the underwater self-propelled model;

[0013] A weight allocation module, used for performing weight allocation according to the current energy state data and the current job task type to obtain a first weight of a hard constraint model predictive controller and a second weight of a soft constraint model predictive controller;

[0014] A speed prediction module, used to adjust the output of the hard constraint model prediction controller using the first weight, and adjust the soft constraint model prediction controller using the second weight, to obtain the expected speed change at the current moment, and add the expected speed change to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model; wherein there is a speed error vector between the current moment speed vector and the reference speed vector;

[0015] The power control module inputs the speed error vector into an adaptive terminal sliding film controller with finite time convergence to obtain a control rate so as to perform high-precision navigation control on the underwater self-propelled model; wherein the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in a preset control gain range.

[0016] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the method in the above embodiment by executing the computer instructions.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the method in the above embodiment.

[0018] In the present application, first, by obtaining the velocity vector of the underwater self-propelled model at the previous moment, the current energy state data and the current operation task type; secondly, according to the current energy state data and the current operation task type, weight distribution is performed to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller; the output of the hard constraint model predictive controller is adjusted by the first weight, and the soft constraint model predictive controller is adjusted by the second weight to obtain the expected speed change at the current moment. Then, the expected speed change is added to the velocity vector at the previous moment to obtain the current moment velocity vector of the underwater self-propelled model. Finally, the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the velocity error vector, the minimum control gain value and the maximum control gain value in the preset control gain range, and the velocity error vector between the current moment velocity vector and the reference velocity vector is input into the adaptive terminal sliding film controller with finite time convergence to obtain the control rate, so as to perform high-precision navigation control on the underwater self-propelled model. The adaptability of the underwater self-propelled model to different working conditions is improved from two aspects: weight distribution according to the current energy state data and the current operation task type, and adaptive dynamic adjustment rules of the coefficient matrix in the adaptive terminal synovial controller, thereby reducing the probability of jitter of the underwater self-propelled model controller when facing complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 The control framework of the high-precision navigation control method of the underwater self-propelled model provided in the embodiment of this specification;

[0021] Figure 2 A schematic flow chart of a high-precision navigation control method for an underwater self-propelled model provided in an embodiment of this specification;

[0022] Figure 3 A schematic flow chart of a high-precision navigation control method for an underwater self-propelled model provided in an embodiment of this specification;

[0023] Figure 4a A schematic diagram of the framework of the control module of the underwater self-propelled model provided in the implementation mode of this specification;

[0024] Figure 4bA schematic diagram of the flow chart of the control module of the underwater self-propelled model provided in the implementation mode of this specification;

[0025] Figure 5 A schematic diagram of the framework of a high-precision navigation control device for an underwater self-propelled model provided in an embodiment of this specification;

[0026] Figure 6 A schematic diagram of the structure of a computer device provided for an embodiment of this specification. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0028] Aiming at the motion characteristics of the underwater self-propelled model and the high-precision tracking control of the parameterized route output by the planning system, a three-dimensional time-varying guidance method is designed in this application at the kinematic and dynamic levels, combined with the motion characteristics of the underwater self-propelled model, to achieve coupled control of the six degrees of freedom of the underwater self-propelled model. Considering the nonlinear characteristics of the motion equation and drive system of the underwater self-propelled model, combined with the drive input of the equilibrium system, etc., predicting the motion state of the underwater self-propelled model, and coordinating and distributing control of the multiple drive systems, the underwater self-propelled model can achieve high-precision autonomous navigation control of the planned route. This application intends to combine the complex dynamic characteristics of the underwater self-propelled model, unknown environmental disturbances, etc., to overcome the difficulties of kinematic guidance and dynamic control, and design a three-dimensional navigation control algorithm with excellent robustness and control accuracy.

[0029] From the perspective of the expected motion mode of the underwater self-propelled model, the control problems of the underwater self-propelled model can be divided into four categories: horizontal plane orientation, vertical plane depth setting, path following, and trajectory tracking. Among them, the horizontal plane orientation and vertical plane depth setting problems can be attributed to point stabilization problems from the control level. Path following and trajectory tracking problems involve the time-varying expected output of the control object, which requires higher controller performance. Path following and trajectory tracking problems can be attributed to the stabilization problem of tracking errors. Commonly used analysis methods include Lyapunov direct method, backstepping method and cascade system method. In related technologies, different analysis methods are used for the two types of problems, and research is carried out in combination with the external environment and the characteristics of underwater self-propelled models. In addition, the trajectory tracking problem has more stringent constraints on the expected path characteristics. Line of sight guidance is derived from traditional manual steering experience and is a common and effective method to solve the problem of tracking time-varying expected path points from the kinematic level. Regarding the motion space of underwater self-propelled models, on the one hand, underwater self-propelled models can simply navigate in the horizontal plane or in the vertical plane. On the other hand, the spatial motion of underwater self-propelled models can be simplified and decomposed into two forms of horizontal and vertical plane motion. Therefore, the research on underwater navigation motion control is carried out from three directions: horizontal plane, vertical plane and three-dimensional space. The research on the horizontal plane motion control of underwater self-propelled models can be attributed to the motion mode that only changes or maintains the pitch and depth. Its research method is similar to the motion control method of surface ships. The vertical plane motion control can be attributed to the control mode that only changes or maintains the pitch and depth without changing the heading. One of the special working conditions is to track the seabed surface according to the seabed terrain. At this time, the dynamics of the underwater self-propelled model will change. The design of the motion controller should take into account the impact of the corresponding model changes. The above-mentioned horizontal plane control and vertical plane control are still different from the point stabilization problem of fixed depth and orientation, which is mainly reflected in the difference caused by the expected track. Tracking control in three-dimensional space increases the difficulty of designing the motion controller due to strong model coupling and increased control items.

[0030] In order to complete three-dimensional high-precision navigation control, first of all, this application designs a suitable weight distribution mechanism. Secondly, this application also proposes a new tracking control method based on parallel model predictive control and finite-time terminal sliding mode control. This method fully utilizes the advantages of parallel model predictive control in dealing with constraints by cascading two control links, while utilizing the fast response characteristics of the finite-time terminal sliding mode controller to improve the control accuracy and robustness of the system. In addition, this application also designs a fast RBF neural network disturbance observer to further improve the performance of the control system when facing different optimization objectives and unknown complex disturbances.

[0031] See also Figure 1, the tracking control method proposed in this application is divided into two parts: kinematic link and dynamic link. In the kinematic link, a parallel model predictive controller and a weight distributor are used to generate the desired speed / angular velocity instructions of the underwater self-propelled model; in the dynamic link, a finite-time terminal sliding mode controller, a disturbance observer and a rudder angle optimization distributor are used to ensure that the six-degree-of-freedom speed / angular velocity error converges to zero within a finite time. Compared with the tracking controller in the related art, this controller has three main advantages: first, the controller ensures that the underwater self-propelled model meets the requirements of the corresponding stage in the process of performing autonomous navigation tasks through soft constraints, hard constraints, weight distributors and other methods; second, the composite control framework reduces the number of iterations of the nonlinear solver in the model predictive controller, thereby reducing the requirements for the controller chip, which is conducive to practical applications; finally, the RBF neural network disturbance observer can effectively suppress noise and interference, thereby improving control accuracy.

[0032] The kinematic prediction model is illustrated as an example. The model of the underwater self-propelled model is nonlinear. In order to meet the motion requirements of the underwater self-propelled model, the model is linearized and discretized with a sampling period, and the linear time-varying state space expression is obtained as follows:

[0033]

[0034] in, ; ; yes The position vector at time, yes The velocity vector at time. for Transformation matrix at time; introducing increment , the kinematic prediction model expression of the underwater self-propelled model can be obtained as:

[0035]

[0036] in, , , .

[0037] According to the prediction model expression, when the input sequence is given When The state sequence within a moment ,in For the prediction time domain, and The definition is as follows:

[0038]

[0039] in and At the sampling time Predicted The input and state quantities at the moment. Therefore, the predicted position coordinates and Euler angles for:

[0040]

[0041]

[0042] The parallel model predictive controller is explained as an example. The parallel model predictive controller mainly consists of three parts, namely the soft constraint model predictive controller, the hard constraint model predictive controller and the weight allocator. The basic principle is to introduce the reference path into the controller design based on the kinematic prediction model of the underwater self-propelled model, construct the objective function, obtain the predictive control input sequence by solving the constraint optimization problem at each moment, apply the first set of control sequences to the controlled object as the system input, and at the same time roll the prediction time domain, repeat the above steps to achieve the path convergence of the underwater self-propelled model. The expression of the parallel model predictive controller is as follows:

[0043]

[0044]

[0045]

[0046] in, Predict controller outputs for hard-constrained models; Predict controller outputs for soft constraint models; and For the weight allocator The corresponding weight of the output at that moment. is the first weight of the hard-constrained model predictive controller, Second weight of the soft-constrained model predictive controller.

[0047] In the process of trajectory tracking, the soft constraint model predictive controller is mainly responsible for the trajectory tracking control in the long-distance search phase. Since the requirements for posture in the search phase are relatively loose, the posture constraints are reduced by introducing relaxation variables. In addition, for the long-distance search process, energy consumption is also an important factor that cannot be ignored, so an energy consumption model is established. And introduce it into the objective function to be optimized:

[0048]

[0049] Defining the predicted path coordinates And the reference path coordinates as follows:

[0050]

[0051]

[0052] The objective function and constraints of the soft constraint model predictive controller are as follows:

[0053]

[0054] in ; and is the weight coefficient matrix; For the prediction time domain, To control the time domain; is the speed increment; and They represent the upper and lower limits of the input, respectively, and are used to constrain the smoothness of the velocity; is the weight of each part of the objective function; is the slack variable.

[0055] The hard constraint model predictive controller is used for the precise control stage during autonomous navigation. In this stage, the underwater autonomous model must strictly meet the attitude hard constraints, which are expressed as follows:

[0056]

[0057] in are the upper and lower limits of the posture constraint.

[0058] The dynamic tracking controller is illustrated by way of example. If the response speed of the dynamic controller is slow, the underwater self-propelled model will not be able to move along the desired path or position and attitude deviations will occur during the movement. This may cause the underwater self-propelled model to be unable to complete the intended task or require more time and energy to correct the deviation. Therefore, timely tracking of the desired speed generated by the guidance law is a key factor in ensuring the trajectory tracking control accuracy and system stability of the underwater self-propelled model. The present application improves the tracking performance and stability of the controller by designing a terminal sliding surface so that the system state can converge to zero within a finite time. In addition, the RBF neural network disturbance observer can effectively suppress noise and interference, thereby improving control accuracy.

[0059] Unknown external disturbances such as wind and waves will bring uncertainty to the mathematical model, which in turn affects the tracking control performance. In order to ensure the rapidity and stability of dynamic control, fast and accurate disturbance estimation is required. This project proposes a finite-time convergent RBF neural network disturbance observer, which uses a radial basis function neural network to estimate unknown disturbances and provides fast and accurate disturbance estimation by using an RBF adaptive algorithm to approximate unknown nonlinear functions.

[0060] Usually, the disturbance cannot be measured directly. In order to accurately estimate the disturbance of the external position, a dynamic estimation model is established as follows:

[0061]

[0062] in,

[0063] , is the output of the disturbance observer and is defined as follows:

[0064]

[0065] in, is the input of the disturbance observer; is the number of neurons in the hidden layer; and are the center vector and the width vector respectively; is the weight coefficient and according to the observation error renew.

[0066]

[0067] in is the output of the dynamics model, Output of the kinetic estimation model.

[0068] The adaptive terminal sliding mode controller with finite time convergence is exemplified. In order to improve the response speed of the controller, this scheme proposes a terminal sliding mode control rate with finite time convergence, which improves the system stability. Define the reference speed , then the velocity error vector It can be expressed as:

[0069]

[0070] Taking the derivative of the above formula, we can get:

[0071]

[0072] The terminal sliding surface is designed as follows:

[0073]

[0074] in, , ; represents the error vector The elements; Represents the coefficient vector No. elements; is the coefficient vector No. elements.

[0075]

[0076] In order to make the speed tracking error converge to zero in a finite time, the control rate is designed as follows:

[0077]

[0078] in , is the output of the disturbance observer, The definition is as follows:

[0079]

[0080] in, , , , respectively, matrices , , The elements in .

[0081] In order to make the coefficient matrix Dynamically adjust with the size of the error, set the coefficient matrix to change according to the error within a certain range. When the error is large, the coefficient matrix increases to strengthen the control; when the error is small, the coefficient matrix decreases to avoid over-control or chattering.

[0082] Define the coefficient matrix The dynamic adjustment rules are as follows:

[0083]

[0084] in, is the minimum value of the control gain, used to avoid Too small will cause the controller to not respond enough. is the maximum value of the control gain, which is used to limit Not too large to cause system instability. is a small positive number used to avoid singular situations when the error is close to zero.

[0085] After determining the control rate, the tracking control method in the present application provides two rudder angle allocation methods: X-rudder optimization allocation and cross-rudder task allocation. In which, it is judged whether the underwater self-propelled model adopts an X-rudder or a cross-rudder; if the underwater self-propelled model adopts an X-rudder, the rudder angle is allocated based on the control rate to obtain a first rudder angle suitable for the X-rudder; if the underwater self-propelled model adopts a cross-rudder, the rudder angle is allocated based on the control rate to obtain a second rudder angle suitable for the cross-rudder.

[0086] According to an embodiment of the present application, a high-precision navigation control method for an underwater self-propelled model is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0087] See also Figure 2 The high-precision navigation control method of the underwater self-propelled model includes the following steps:

[0088] S210, obtaining the previous moment velocity vector, current energy state data and current operation task type of the underwater self-propelled model.

[0089] The current energy state data is any one of the first energy level, the second energy level and the third energy level. The first energy level is lower than the second energy level, and the second energy level is lower than the third energy level. Exemplarily, the first energy level corresponds to low energy, the second energy level corresponds to medium energy, and the third energy level corresponds to high energy, and high energy is defined as the remaining energy being greater than 60%, medium energy being the remaining energy being between 30% and 60%, and low energy being the remaining energy being less than 30%.

[0090] The current task type is any one of a search task and a precise task. In the search task and the third energy level, the importance of the soft constraint model predictive controller is higher than that of the hard constraint model predictive controller; in the precise task and the first energy level and the second energy level, the importance of the hard constraint model predictive controller is higher than that of the soft constraint model predictive controller.

[0091] In some cases, it is necessary to control the underwater self-propelled model in combination with the actual situation of the underwater self-propelled model. In order to accurately grasp the actual situation of the underwater self-propelled model, it is necessary to select parameters that are easy to quantify, and the energy level of the underwater self-propelled model can be directly obtained through sensors. Different control methods are required for different operation tasks, which is crucial for the safety of the underwater self-propelled model, such as forcibly performing some operation tasks when the energy is insufficient. In addition, in the related art, a controller and a set of control parameters are debugged for a certain specific task situation. Then, when facing different other task situations, only the same controller and the same set of control parameters can be used. It is understandable that the control parameters and controller may no longer be suitable for other task situations. Therefore, it is necessary to allocate the controller according to different operation task types to flexibly adapt to various different task situations. Specifically, the current energy state data and the current operation task type of the underwater self-propelled model are obtained to provide a data basis for subsequent controller allocation.

[0092] S220. Perform weight allocation according to current energy state data and current job task type to obtain a first weight of a hard constraint model predictive controller and a second weight of a soft constraint model predictive controller.

[0093] Specifically, in order to ensure smooth switching between the two controllers, weights are assigned according to the current energy state data and the current job task type, such as by using a hierarchy analysis method. The energy level and the job task type are used as two criteria, and the weights of the hard constraint model predictive controller and the soft constraint model predictive controller are assigned based on these criteria.

[0094] In this embodiment, first, the hierarchical structure of the decision problem needs to be clarified, as follows:

[0095] 1) Assign controller weights at the target layer: assign weights to the hard-constrained model predictive controller and the soft-constrained model predictive controller

[0096] 2) Standard layer:

[0097] Energy level: high energy, medium energy, low energy.

[0098] Task types: search tasks (prefer soft constraint controllers) and precision work tasks (prefer hard constraint controllers).

[0099] 3) Solution layer:

[0100] Soft constraint controller: better suited for long-distance search tasks and low-energy situations.

[0101] Hard constraint controller: better suited for precision tasks and high energy situations.

[0102] Secondly, construct a judgment matrix. 1) In order to determine the importance of each criterion in the overall controller weight allocation, pairwise comparisons are performed at the criterion level to evaluate the relative importance between energy level and task type. When energy is insufficient, more emphasis is placed on the importance of energy level, and when energy is sufficient, more emphasis is placed on the importance of task type. 2) Construct a judgment matrix under energy levels, where energy levels are divided into high energy, medium energy, and low energy. These energy levels are used to compare the relative importance of soft constraint controllers and hard constraint controllers pairwise. 3) Construct a judgment matrix of task types, where task types are divided into search tasks and precise operation tasks. Search tasks prefer to use soft constraint controllers because they are more flexible. Precise operation tasks prefer hard constraint controllers because they require more precise control and path planning. Construct a judgment matrix of task types based on the characteristics of the task types.

[0103] Finally, normalization and weight calculation are performed based on the constructed judgment matrix, and weight calculation is performed according to the current energy state data and the current job task type to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller.

[0104] S230. Use the first weight to adjust the output of the hard constraint model predictive controller, and use the second weight to adjust the soft constraint model predictive controller to obtain the expected speed change at the current moment, and add the expected speed change to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model.

[0105] Among them, there is a speed error vector between the current speed vector and the reference speed vector. The hard constraint model predictive controller is used for the precise control stage during autonomous navigation, and the soft constraint model predictive controller is mainly responsible for the trajectory tracking control in the long-distance search stage. Specifically, the parallel model predictive controller includes a soft constraint model predictive controller, a hard constraint model predictive controller and a weight distributor. The hard constraint model predictive controller is parallel to the soft constraint model predictive controller. The weight distributor outputs the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller. The output of the hard constraint model predictive controller and the output of the soft constraint model predictive controller are obtained. The output of the hard constraint model predictive controller is adjusted by the first weight, and the output of the soft constraint model predictive controller is adjusted by the second weight. The parallel model predictive controller outputs the expected speed change at the current moment. Then, the expected speed change is added to the speed vector of the previous moment to obtain the current moment speed vector of the underwater self-propelled model, and the reference speed vector is subtracted from the current moment speed vector to obtain the speed error vector.

[0106] S240, inputting the speed error vector into an adaptive terminal sliding film controller with finite time convergence to obtain a control rate, so as to perform high-precision navigation control on the underwater self-propelled model.

[0107] The coefficient matrix in the adaptive terminal sliding membrane controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in the preset control gain range.

[0108] Specifically, in the dynamic link, the velocity error vector is input into the adaptive terminal sliding membrane controller with finite time convergence, which outputs the control rate, and the control rate is input into the rudder angle distributor for rudder angle distribution. Furthermore, in order to allow the coefficient matrix to dynamically adjust with the size of the error, the coefficient matrix is ​​set to change according to the error within a certain range. When the error is large, the coefficient matrix increases to strengthen the control strength; when the error is small, the coefficient matrix decreases to avoid excessive control or chattering, so as to achieve high-precision navigation control of the underwater self-propelled model.

[0109] In the above implementation, first, by obtaining the velocity vector of the underwater self-propelled model at the previous moment, the current energy state data and the current operation task type; secondly, weight allocation is performed according to the current energy state data and the current operation task type to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller; the output of the hard constraint model predictive controller is adjusted by the first weight, and the output of the soft constraint model predictive controller is adjusted by the second weight to obtain the expected speed change at the current moment. Then, the expected speed change is added to the velocity vector at the previous moment to obtain the current moment velocity vector of the underwater self-propelled model. Finally, the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the velocity error vector, the minimum control gain value and the maximum control gain value in the preset control gain range, and the velocity error vector between the current moment velocity vector and the reference velocity vector is input into the adaptive terminal sliding film controller with finite time convergence to obtain the control rate, so as to perform high-precision navigation control on the underwater self-propelled model. The adaptability of the underwater self-propelled model to different working conditions is improved by weighting according to the current energy state data and the current operation task type, and the adaptive dynamic adjustment rules of the coefficient matrix in the adaptive terminal synovial controller are adopted to reduce the probability of jitter of the underwater self-propelled model controller when facing complex working conditions.

[0110] In some embodiments, weights are assigned based on current energy state data and current job task type to obtain a first weight for a hard constraint model predictive controller and a second weight for a soft constraint model predictive controller, including: obtaining a first preset weight corresponding to the current energy state data and a second preset weight corresponding to the current job task type; weighting and calculating the first preset weight and the second preset weight based on an energy task judgment matrix to obtain a first weight and a second weight.

[0111] Among them, the elements in the energy task judgment matrix are used to characterize the relative importance between the energy level and the job task type. Specifically, since the energy level judgment matrix and the task type judgment matrix have been constructed in advance for the preset energy level and job task type, when the current energy state data and the current job task type are obtained, the first preset weight corresponding to the current energy state data and the second preset weight corresponding to the current job task type can be known. Furthermore, as mentioned above, the energy level and the job task type have different importance in different situations. Therefore, the energy task judgment matrix, the first preset weight and the second preset weight are integrated to perform controller weight allocation to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller.

[0112] In some embodiments, see Figure 3 , weights are assigned according to the current energy state data and the current job task type, and the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller are obtained, including:

[0113] S310. Perform weight calculation according to the energy level judgment matrix and the task type judgment matrix to determine a first preset weight corresponding to the current energy state data and a second preset weight corresponding to the current operation task type.

[0114] Among them, the elements in the energy level judgment matrix are used to characterize the relative importance between the energy level and the hard constraint model predictive controller and the soft constraint model predictive controller; the elements in the task type judgment matrix are used to characterize the relative importance between the job task type and the hard constraint model predictive controller and the soft constraint model predictive controller.

[0115] S320. Perform a weighted sum calculation on the first preset weight and the second preset weight according to the energy task judgment matrix to obtain a first weight and a second weight.

[0116] Among them, the elements in the energy task judgment matrix are used to characterize the relative importance between energy levels and work task types.

[0117] Specifically, when comparing two by two at the criterion level and evaluating the relative importance between energy level and task type, high energy and medium energy are considered as one, as sufficient energy, and low energy is considered as insufficient energy. When energy is insufficient, the importance of energy level is emphasized, and when energy is relatively sufficient, the importance of task type is emphasized. Therefore, the judgment matrix is ​​defined as follows:

[0118]

[0119] It should be noted that “Energy level (high, medium)” is not as important as “Task type”, so energy level (high, medium) is less important than task type and is given 1 / 3.

[0120] The importance of the task type relative to the energy level (low) is low, giving 1 / 3.

[0121]

[0122] It should be noted that “Energy level (low)” is more important than “Task type”, so energy level (low) is more important than task type and is given as 3.

[0123] The following is an example of how to construct a judgment matrix under energy levels, which are divided into high energy, medium energy and low energy. These energy levels are used to compare the relative importance of soft constraint controllers and hard constraint controllers.

[0124] High Energy: At high energy, a hard-constrained controller is preferred because high energy is required for accurate operation.

[0125] Medium Energy: At medium energy, the importance of soft and hard constraint controllers is balanced.

[0126] Low energy: At low energy, soft constraint controllers are preferred because long-distance search tasks are more flexible at low energy.

[0127] To this end, the following judgment matrix is ​​constructed to compare the "soft constraint controller" with the "hard constraint controller" pairwise:

[0128]

[0129] It should be noted that "1 / 5" means that at high energy, the importance of the soft constraint controller is much lower than that of the hard constraint controller; while "5" means that at low energy, the importance of the soft constraint controller is much higher than that of the hard constraint controller.

[0130] For medium energy, the importance of soft constraint and hard constraint controllers is set to be close, and the judgment matrix is ​​constructed as follows:

[0131]

[0132] It should be noted that the relative importance of both controllers is 1, because at medium energy levels, their effects are relatively balanced.

[0133]

[0134] It should be noted that the importance of the soft constraint controller relative to the hard constraint controller is 5, indicating that the soft constraint controller is more important at low energy levels. The importance of the hard constraint controller relative to the soft constraint controller is 1 / 5, indicating that the importance of the hard constraint controller is much lower than that of the soft constraint controller at low energy levels.

[0135] This section describes how to construct a judgment matrix for task types. Task types are divided into search tasks and precision operation tasks. According to the characteristics of task types:

[0136] Search tasks prefer soft constraint controllers because they are more flexible.

[0137] Precision work tasks prefer hard-constrained controllers because they require more precise control and path planning.

[0138] According to this feature, the judgment matrix of task type is constructed as follows:

[0139]

[0140] It should be noted that in the search task, the importance of the soft constraint controller is significantly higher than that of the hard constraint controller.

[0141]

[0142] It should be noted that in precision operation tasks, the importance of hard constraint controller is significantly higher than that of soft constraint controller.

[0143] The normalization of the judgment matrix and the calculation of the weights are exemplified.

[0144] For the judgment matrix at high energy:

[0145]

[0146] Normalize each column:

[0147] Normalize the first column:

[0148]

[0149] The second column is normalized:

[0150]

[0151] Get the normalized matrix:

[0152]

[0153] Calculate the row average of the above formula to get the weights of the soft constraint and hard constraint controllers:

[0154]

[0155] That is, in the high energy case, the weight of the soft constraint controller is 0.167, and the weight of the hard constraint controller is 0.833.

[0156] Similarly, at medium energy levels,

[0157]

[0158] That is, in the case of medium energy, the weights of the soft constraint controller and the hard constraint controller are both 0.5.

[0159] At low energy levels:

[0160]

[0161] That is, in the low energy case, the weight of the soft constraint controller is 0.833, while the weight of the hard constraint controller is 0.167.

[0162] The normalized task type judgment matrix is ​​exemplified. Task type (precision operation) judgment matrix The definition is as follows:

[0163]

[0164] The matrix obtained after normalization is:

[0165]

[0166] So the weight matrix is:

[0167]

[0168] Similarly, under the search task:

[0169]

[0170] Therefore, in the case of the precision task, the weight of the soft constraint controller is 0.125, while the weight of the hard constraint controller is 0.875. In the case of the search task, the weight of the soft constraint controller is 0.875, while the weight of the hard constraint controller is 0.125.

[0171] The decision matrix of normalized energy and task type is illustrated as an example. According to the above calculation formula, when the energy is sufficient: When energy is insufficient, .

[0172] Finally, the final weights of the soft and hard constraint controllers are obtained by weighting the weights of the energy level and the task type. For example, assuming that the current energy level of the system is "low" and the task type is "search task", the final weight is Calculated as:

[0173]

[0174] In some embodiments, after obtaining the control rate, the method may further include: determining whether the underwater self-propelled model adopts an X-rudder or a cross-rudder; if the underwater self-propelled model adopts an X-rudder, allocating the rudder angle based on the control rate to obtain a first rudder angle suitable for the X-rudder; if the underwater self-propelled model adopts a cross-rudder, allocating the rudder angle based on the control rate to obtain a second rudder angle suitable for the cross-rudder.

[0175] In some embodiments, the rudder angle allocation objective function of the cross rudder is expressed in the following manner:

[0176]

[0177] in, is the stern rudder angle, is the depth deviation, is the trim angle deviation. From the above formula, it can be seen that when the depth deviation is large, the depth control is preferentially performed by adjusting the bow rudder angle. When the trim angle deviation is large, the attitude is preferentially adjusted by the stern elevator rudder angle.

[0178] In some embodiments, the dynamic adjustment rule of the coefficient matrix is ​​expressed in the following manner:

[0179]

[0180] in, is the minimum control gain, is the maximum control gain, is a positive number used to avoid singular situations when the error is close to zero. The velocity error vector Expressed as , is the first in the velocity error vector elements.

[0181] In some embodiments, before obtaining the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller, the method may also include: performing mode judgment on the underwater self-propelled model; if the underwater self-propelled model is not in the follow-up control mode, loading the reference path and current position and posture information of the underwater self-propelled model; and through the path point switching function, performing path control on the underwater self-propelled model according to the reference path and current position and posture information; if the underwater self-propelled model is in the follow-up control mode, reading the control instruction, and performing path control on the underwater self-propelled model according to the control instruction.

[0182] For example, see Figure 4a and Figure 4b The high-precision control module detects the current position and posture, performs control algorithm calculations according to the predetermined target, and adjusts the actuator output to achieve precise control of the target position and posture. It can also perform path tracking, posture control and other functions to provide accurate and stable control capabilities. Its main functions and features are as follows Figure 4a As shown, the control flow chart is as follows Figure 4b shown.

[0183] In this example, the main functions include path point switching function, dynamics control function, kinematics control and follow-up control function, and the functions implemented include path tracking, posture control and evaluation index calculation and storage.

[0184] In this example, the underwater self-propelled model is judged to determine whether it is in the follow-up control mode. If it is in the follow-up control mode, the control instruction is read, and the speed is converted into the thrust of the propeller, and the cross rudder angle is converted into the X rudder angle. If it is not in the follow-up control mode, the reference path is loaded, the current boat position and attitude information is loaded, the path point switching function is called, the kinematic control link and the dynamic control link are entered, and the control instruction set is generated.

[0185] See also Figure 5 An embodiment of the present application provides a high-precision navigation control device 500 for an underwater self-propelled model. The high-precision navigation control device 500 includes a data acquisition module 510, a weight distribution module 520, a speed prediction module 530 and a power control module 540.

[0186] The data acquisition module 510 is used to obtain the velocity vector of the underwater self-propelled model at the previous moment, the current energy state data and the current operation task type.

[0187] The weight allocation module 520 is used to perform weight allocation according to the current energy state data and the current job task type to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller.

[0188] The speed prediction module 530 is used to adjust the output of the hard constraint model prediction controller using the first weight, and adjust the soft constraint model prediction controller using the second weight to obtain the expected speed change at the current moment, and add the expected speed change to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model; wherein there is a speed error vector between the current moment speed vector and the reference speed vector.

[0189] The power control module 540 inputs the speed error vector into the adaptive terminal sliding film controller with finite time convergence to obtain the control rate so as to perform high-precision navigation control on the underwater self-propelled model; wherein the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in the preset control gain range.

[0190] In some embodiments, the weight allocation module 520 is also used to perform weight calculation based on the energy level judgment matrix and the task type judgment matrix to determine the first preset weight corresponding to the current energy state data and the second preset weight corresponding to the current job task type; wherein the elements in the energy level judgment matrix are used to characterize the relative importance between the energy level and the hard constraint model prediction controller and the soft constraint model prediction controller; the elements in the task type judgment matrix are used to characterize the relative importance between the job task type and the hard constraint model prediction controller and the soft constraint model prediction controller; the first preset weight and the second preset weight are weighted and calculated according to the energy task judgment matrix to obtain the first weight and the second weight; wherein the elements in the energy task judgment matrix are used to characterize the relative importance between the energy level and the job task type.

[0191] In some embodiments, the high-precision navigation control device 500 also includes a rudder angle allocation module, which is used to determine whether the underwater self-propelled model adopts an X-rudder or a cross rudder; if the underwater self-propelled model adopts an X-rudder, the rudder angle is allocated based on the control rate to obtain a first rudder angle suitable for the X-rudder; if the underwater self-propelled model adopts a cross rudder, the rudder angle is allocated based on the control rate to obtain a second rudder angle suitable for the cross rudder.

[0192] In some embodiments, the rudder angle allocation objective function of the cross rudder is expressed in the following manner:

[0193]

[0194] in, , are the stern rudder angle, is the depth deviation, is the pitch angle deviation, , From the above formula, we can see that when the depth deviation is large, the bow rudder angle is adjusted first for depth control, and when the trim angle deviation is large, the stern elevator rudder angle is adjusted first for attitude adjustment.

[0195] In some embodiments, the dynamic adjustment rule of the coefficient matrix is ​​expressed in the following manner:

[0196]

[0197] in, is the minimum control gain, is the maximum control gain, is a positive number used to avoid singular situations when the error is close to zero. The velocity error vector Expressed as , is the first in the velocity error vector elements.

[0198] In some embodiments, the high-precision navigation control device 500 further includes a mode determination module for determining the mode of the underwater self-propelled model;

[0199] If the underwater self-propelled model is not in the follow-up control mode, the reference path and current position and attitude information of the underwater self-propelled model are loaded; and the path point switching function is used to control the path of the underwater self-propelled model according to the reference path and current position and attitude information;

[0200] If the underwater self-propelled model is in the follow-up control mode, the control instruction is read and the path of the underwater self-propelled model is controlled according to the control instruction.

[0201] In some embodiments, the current energy state data is any one of a first energy level, a second energy level, and a third energy level; wherein the first energy level is lower than the second energy level, and the second energy level is lower than the third energy level;

[0202] The current job task type is any one of a search task and a precise job task; wherein, under the search task and the third energy level, the importance of the soft constraint model predictive controller is higher than that of the hard constraint model predictive controller; under the precise job task, the first energy level and the second energy level, the importance of the hard constraint model predictive controller is higher than that of the soft constraint model predictive controller.

[0203] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0204] The high-precision navigation control device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0205] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0206] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0207] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0208] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0209] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0210] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0211] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0212] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0213] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0214] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0215] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0216] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0217] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0218] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0220] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0221] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0222] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0223] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0224] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A high-precision navigation control method for an underwater self-propelled model, characterized in that: A parallel model predictive controller is applied in the kinematics link, the parallel model predictive controller includes a weight distributor, a parallel soft constraint model predictive controller and a hard constraint model predictive controller, and a dynamics tracking controller connected to the parallel model predictive controller is adopted in the dynamics link, the dynamics tracking controller includes a finite time terminal sliding mode controller; the method includes: Obtain the previous moment velocity vector, current energy state data and current operation task type of the underwater self-propelled model; Through the weight allocator, weight allocation is performed according to the current energy state data and the current job task type to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller; specifically: construct an energy level judgment matrix and a task type judgment matrix; wherein the elements in the energy level judgment matrix are used to characterize the relative importance between the energy level and the hard constraint model predictive controller and the soft constraint model predictive controller; the elements in the task type judgment matrix are used to characterize the relative importance between the job task type and the hard constraint model predictive controller and the soft constraint model predictive controller; weight calculation is performed according to the energy level judgment matrix and the task type judgment matrix to determine the first preset weight corresponding to the current energy state data and the second preset weight corresponding to the current job task type; the first preset weight and the second preset weight are weighted and calculated according to the energy task judgment matrix to obtain the first weight and the second weight; wherein the elements in the energy task judgment matrix are used to characterize the relative importance between the energy level and the job task type; The output of the hard constraint model predictive controller is adjusted using the first weight, and the soft constraint model predictive controller is adjusted using the second weight to obtain the expected speed change at the current moment, and the expected speed change is added to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model; wherein there is a speed error vector between the current moment speed vector and the reference speed vector; The speed error vector is input into an adaptive terminal sliding film controller with finite time convergence to obtain a control rate, so as to perform high-precision navigation control on the underwater self-propelled model; wherein the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in a preset control gain range; Among them, the coefficient matrix The dynamic adjustment rules are expressed in the following way: in, is the minimum control gain, is the maximum value of the control gain, is a positive number used to avoid singular situations when the error is close to zero. The velocity error vector Expressed as , is the first in the velocity error vector elements.

2. The method according to claim 1, characterized in that After obtaining the control rate, the method further includes: Determining whether the underwater self-propelled model adopts an X-rudder or a cross-rudder; If the underwater self-propelled model adopts an X-rudder, a rudder angle is allocated based on the control rate to obtain a first rudder angle suitable for the X-rudder.

3. The method according to claim 2, characterized in that After obtaining the control rate, the method further includes: If the underwater self-propelled model adopts a cross rudder, the rudder angle is allocated based on the control rate to obtain a second rudder angle suitable for the cross rudder.

4. The method according to claim 1, characterized in that: Before obtaining the first weight of the hard-constrained model predictive controller and the second weight of the soft-constrained model predictive controller, the method further includes: Performing mode judgment on the underwater self-propelled model; If the underwater self-propelled model is not in the follow-up control mode, the reference path and current position and posture information of the underwater self-propelled model are loaded; and through the path point switching function, the path control of the underwater self-propelled model is performed according to the reference path and the current position and posture information.

5. The method according to claim 4, characterized in that If the underwater self-propelled model is in the follow-up control mode, the control instruction is read, and the path of the underwater self-propelled model is controlled according to the control instruction.

6. The method according to any one of claims 1 to 5, characterized in that: The current energy status data is any one of a first energy level, a second energy level and a third energy level; wherein the first energy level is lower than the second energy level, and the second energy level is lower than the third energy level.

7. The method according to claim 6, characterized in that The current job task type is any one of a search task and a precise job task; wherein, under the search task and the third energy level, the importance of the soft constraint model predictive controller is higher than the importance of the hard constraint model predictive controller; under the precise job task, the first energy level and the second energy level, the importance of the hard constraint model predictive controller is higher than the importance of the soft constraint model predictive controller.

8. A high-precision navigation control device for an underwater self-propelled model, characterized in that: A parallel model predictive controller is applied in the kinematics link, the parallel model predictive controller includes a weight distributor, a parallel soft constraint model predictive controller and a hard constraint model predictive controller, and a dynamics tracking controller connected to the parallel model predictive controller is adopted in the dynamics link, the dynamics tracking controller includes a finite time terminal sliding mode controller; the device includes: A data acquisition module is used to obtain the previous moment velocity vector, current energy state data and current operation task type of the underwater self-propelled model; A weight allocation module is used to perform weight allocation according to the current energy state data and the current job task type through the weight allocator to obtain the first weight of the hard constraint model predictive controller and the second weight of the soft constraint model predictive controller; specifically: construct an energy level judgment matrix and a task type judgment matrix; wherein the elements in the energy level judgment matrix are used to characterize the relative importance between the energy level and the hard constraint model predictive controller and the soft constraint model predictive controller; the elements in the task type judgment matrix are used to characterize the relative importance between the job task type and the hard constraint model predictive controller and the soft constraint model predictive controller; weight calculation is performed according to the energy level judgment matrix and the task type judgment matrix to determine the first preset weight corresponding to the current energy state data and the second preset weight corresponding to the current job task type; the first preset weight and the second preset weight are weighted and calculated according to the energy task judgment matrix to obtain the first weight and the second weight; wherein the elements in the energy task judgment matrix are used to characterize the relative importance between the energy level and the job task type; A speed prediction module, used to adjust the output of the hard constraint model prediction controller using the first weight, and adjust the soft constraint model prediction controller using the second weight, to obtain the expected speed change at the current moment, and add the expected speed change to the speed vector at the previous moment to obtain the current moment speed vector of the underwater self-propelled model; wherein there is a speed error vector between the current moment speed vector and the reference speed vector; The power control module inputs the speed error vector into the adaptive terminal sliding film controller with finite time convergence to obtain the control rate, so as to perform high-precision navigation control on the underwater self-propelled model; wherein the coefficient matrix in the adaptive terminal sliding film controller is adaptively and dynamically adjusted based on the speed error vector, the minimum control gain value and the maximum control gain value in the preset control gain range; wherein the coefficient matrix The dynamic adjustment rules are expressed in the following way: in, is the minimum control gain, is the maximum value of the control gain, is a positive number used to avoid singular situations when the error is close to zero. The velocity error vector Expressed as , is the first in the velocity error vector elements; The current energy state data is any one of a first energy level, a second energy level and a third energy level; wherein the first energy level is lower than the second energy level, and the second energy level is lower than the third energy level; The current job task type is any one of a search task and a precise job task; wherein, under the search task and the third energy level, the importance of the soft constraint model predictive controller is higher than the importance of the hard constraint model predictive controller; under the precise job task, the first energy level and the second energy level, the importance of the hard constraint model predictive controller is higher than the importance of the soft constraint model predictive controller.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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