Control method and device of unmanned aerial vehicle at sea, electronic equipment and storage medium

Abstract

The invention discloses a control method and device of an offshore unmanned vehicle, electronic equipment and a storage medium, and can be widely applied to the technical field of offshore pier inspection control. According to the method, after the control model of the unmanned marine vehicle is built, the real-time model inertia mass parameter and the real-time model damping parameter of the control model are obtained, the real-time model inertia mass parameter and the real-time model damping parameter are input into the online identification model to obtain the corrected model parameter, and after the sliding mode control surface is built according to the corrected model parameter, the real-time model inertia mass parameter and the real-time model damping parameter of the control model are obtained. The method comprises the following steps: generating first control data of a marine unmanned vehicle through a sliding mode control surface, then obtaining the disturbance variable of the marine unmanned vehicle in the navigation process through a disturbance observer, adjusting the first control data according to the disturbance variable to obtain second control data, and controlling the inspection track of the marine unmanned vehicle through the second control data. Therefore, the working stability of the unmanned aircraft on the sea in a complex environment can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of offshore bridge pier inspection control technology, and in particular to a control method and device, electronic equipment and storage medium for an unmanned marine vehicle. Background Art

[0002] In the relevant technologies, the inspection of offshore bridge piers is important for the safety of bridge structures, smooth traffic and economic impact. The importance of offshore bridge pier inspection is reflected in many aspects such as ensuring bridge safety, maintaining smooth traffic and reducing economic losses. Traditional offshore pier inspection methods rely on manual inspections and basic tools. Although they can provide certain structural health information, they have problems such as low efficiency, high risk and limited detection range. In this regard, choosing a highly automated and intelligent detection equipment can effectively improve detection efficiency and reduce risks. Selecting an unmanned marine vehicle equipped with intelligent detection equipment can maximize the performance and combine the flexibility of the vehicle to improve detection efficiency, reduce manual risks and expand the detection range.

[0003] The inspection task of offshore bridge piers requires marine unmanned vehicles to perform relevant inspections and flaw detection on the piers according to the corresponding trajectory. Various external interferences and influences will be encountered in the working environment, and the control system needs to handle them accordingly. When the traditional PID heading control system is sailing in unknown sea areas, unknown sea conditions, and unknown ship control parameters, it often has problems such as poor response to unknown interference, inability to optimize control parameters, and poor adaptability to unknown sea areas. Traditional sliding mode control is more robust than PID control, but in complex working environments, the control is still unstable.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a control method and device, electronic equipment and storage medium for a marine unmanned vehicle, which can effectively improve the working stability of the marine unmanned vehicle in a complex environment.

[0006] To achieve the above object, one aspect of an embodiment of the present application provides a method for controlling a marine unmanned aerial vehicle, the method comprising the following steps:

[0007] Building a control model for the unmanned marine vehicle;

[0008] Acquiring a real-time model inertial mass parameter and a real-time model damping parameter of the control model;

[0009] Inputting the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter;

[0010] constructing a sliding mode control surface according to the modified model parameters;

[0011] generating first control data of the unmanned marine vehicle through the sliding mode control surface, wherein the first control data includes longitudinal thrust and lateral bow moment;

[0012] Obtaining the interference amount of the unmanned aerial vehicle at sea during navigation by means of an interference observer;

[0013] Adjust the first control data according to the interference amount to obtain second control data;

[0014] The inspection trajectory of the marine unmanned aerial vehicle is controlled by the second control data.

[0015] In some embodiments, the control model includes a three-degree-of-freedom motion model, and the three-degree-of-freedom motion model is as follows:

[0016]

[0017] in, A matrix representing the position and attitude information of the unmanned aerial vehicle at sea; represents the first-order derivative of η, x, y, Respectively represent the north coordinate, east coordinate and heading angle of the unmanned aerial vehicle at sea; υ=[u,v,r] T A velocity information matrix representing the marine unmanned aerial vehicle; The first-order derivatives u, v, and r of υ represent the longitudinal velocity, lateral velocity, and heading angular velocity of the unmanned marine vehicle, respectively; d = [d u ,d v ,d r ] T represents the external interference matrix; τ c =[τ u ,0,τ r ] represents the control quantity matrix composed of control force and control torque; represents the rotation matrix of the bow roll; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

[0018] In some embodiments, inputting the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter includes:

[0019] According to the real-time model inertial mass parameter and the real-time model damping parameter, the previous model parameter value is corrected by recursive least square method to obtain a new model parameter value as the corrected model parameter.

[0020] In some embodiments, constructing the sliding mode control surface includes:

[0021] Converting the position error information of the unmanned marine vehicle into speed error information;

[0022] The sliding mode control surface is constructed according to the speed error information.

[0023] In some embodiments, converting the position error information of the unmanned marine vehicle into speed error information comprises:

[0024] Determining position error information based on the expected trajectory and actual position information of the marine unmanned aerial vehicle;

[0025] Constructing a stability proof function corresponding to the position error information;

[0026] Calculating a derivative of the stability proof function;

[0027] The speed error information is determined according to the derivative function through a backstepping algorithm.

[0028] In some embodiments, the sliding mode control surface is as follows:

[0029]

[0030] Among them, s 1 、s 2 denote sliding mode functions constructed for longitudinal and lateral velocity errors respectively; τ denotes an integral variable, and the range of the integral variable is [0, t]; λ 1 ,λ 2 ,λ 3 represents the design parameters; u e and v e Indicates the speed error information; Indicates v e The first derivative of .

[0031] In some embodiments, the disturbance observer is as follows:

[0032]

[0033] Where Z = [Z u ,Z v ,Z r ] T Indicates the construction quantity; represents the first-order derivative of Z; L = diag{L u ,L v ,L r} represents the bandwidth diagonal matrix of the interference observer; L u ,L v,L r denote the design parameters of the disturbance observer respectively; d=[d u ,d v ,d r ] T represents the external interference matrix; represents the first-order derivative of d; is the observed value of the disturbance observer to the disturbance, v represents the lateral velocity of the unmanned marine vehicle; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

[0034] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a control device for a marine unmanned aerial vehicle, the device comprising:

[0035] The first module is used to build a control model of the marine unmanned aerial vehicle;

[0036] The second module is used to obtain the real-time model inertial mass parameter and the real-time model damping parameter of the control model;

[0037] A third module is used to input the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter;

[0038] A fourth module is used to construct a sliding mode control surface according to the modified model parameters;

[0039] A fifth module is used to generate first control data of the unmanned marine vehicle through the sliding mode control surface, wherein the first control data includes longitudinal thrust and lateral bow moment;

[0040] The sixth module is used to obtain the interference amount of the unmanned aerial vehicle at sea during navigation through an interference observer;

[0041] A seventh module is used to adjust the first control data according to the interference amount to obtain second control data;

[0042] An eighth module is used to control the inspection trajectory of the marine unmanned aerial vehicle through the second control data.

[0043] To achieve the above object, another aspect of an embodiment of the present application provides an electronic device, including:

[0044] at least one processor;

[0045] at least one memory for storing at least one program;

[0046] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0047] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0048] The embodiments of the present application include at least the following beneficial effects: The present application provides a control method and device, an electronic device and a storage medium for a marine unmanned aerial vehicle. The scheme builds a control model of the marine unmanned aerial vehicle, obtains the real-time model inertial mass parameters and the real-time model damping parameters of the control model, inputs the real-time model inertial mass parameters and the real-time model damping parameters into an online identification model to obtain corrected model parameters, and then constructs a sliding mode control surface according to the corrected model parameters. The first control data of the marine unmanned aerial vehicle is generated through the sliding mode control surface, and then the interference amount of the marine unmanned aerial vehicle during navigation is obtained through an interference observer. The first control data is adjusted according to the interference amount to obtain the second control data, so as to control the inspection trajectory of the marine unmanned aerial vehicle through the second control data, thereby effectively improving the working stability of the marine unmanned aerial vehicle in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of a method for controlling a marine unmanned aerial vehicle provided by an embodiment of the present application;

[0050] Figure 2 It is a schematic diagram of the working principle of the online system identification model based on navigation status data feedback provided by an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of the control principle of the sliding mode control surface provided in an embodiment of the present application;

[0052] Figure 4 This is a comparison diagram between the method of the present application and PID control under the linear working condition provided by the embodiment of the present application;

[0053] Figure 5 This is a comparison diagram between the method of the present application and PID control under the circular working condition provided in the embodiment of the present application;

[0054] Figure 6 This is a comparison diagram between the method of the present application and PID control under the curve working conditions provided in the embodiment of the present application;

[0055] Figure 7 is a schematic diagram of the structure of a control device for a marine unmanned aerial vehicle provided in an embodiment of the present application;

[0056] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] In the related technologies, the traditional offshore pier inspection method relies on manual inspection and basic tools. Although it can provide certain structural health information, it has problems such as low efficiency, high risk, and limited detection range. In this regard, choosing a highly automated and intelligent detection equipment can effectively improve the detection efficiency and reduce the risk. The selection of marine unmanned vehicles equipped with intelligent detection equipment can maximize the performance and combine the flexibility of the vehicle to improve the detection efficiency, reduce manual risks, and expand the detection range.

[0060] The inspection task of offshore bridge piers requires marine unmanned vehicles to conduct relevant inspections and flaw detection on the piers according to the corresponding trajectory. Various external interferences and influences will be encountered in the working environment, and the control system needs to handle them accordingly. When the traditional PID (score-integral-differential) heading control system is sailing in unknown sea areas, unknown sea conditions, and unknown ship control parameters, it often has problems such as poor response to unknown interference, inability to optimize control parameters, and poor adaptability to unknown sea areas. Traditional sliding mode control is more robust than PID control, but in complex working environments, the control is still unstable.

[0061] It can be seen that the prior art has the following problems:

[0062] Insufficient inspection coverage: Current manual and instrument inspections may not ensure full coverage of the environment around the piers, resulting in some areas being missed, affecting the integrity of the inspection.

[0063] Poor environmental adaptability: Existing control methods may not be able to effectively handle the impact of complex underwater environments, such as wind, waves, and currents, on vehicle trajectory tracking, which may cause the vehicle to deviate from the track and fail to complete the mission safely and effectively.

[0064] Stability issues: The uncertainty and complexity of the underwater environment means that marine unmanned vehicles face more interference factors when performing tasks. During the execution of their tasks, they need to keep their posture relatively stable when inspecting around bridge piers in order to more easily complete the flaw detection task, which puts higher demands on the controller.

[0065] In view of this, a control method and device, an electronic device and a storage medium for a marine unmanned aerial vehicle are provided in an embodiment of the present application. After building a control model of the marine unmanned aerial vehicle, the present application obtains the real-time model inertial mass parameters and the real-time model damping parameters of the control model, inputs the real-time model inertial mass parameters and the real-time model damping parameters into an online identification model to obtain corrected model parameters, constructs a sliding mode control surface according to the corrected model parameters, generates first control data of the marine unmanned aerial vehicle through the sliding mode control surface, and then obtains the interference amount of the marine unmanned aerial vehicle during navigation through an interference observer, adjusts the first control data according to the interference amount to obtain second control data, and controls the inspection trajectory of the marine unmanned aerial vehicle through the second control data, thereby effectively improving the working stability of the marine unmanned aerial vehicle in a complex environment.

[0066] The control method of the unmanned aerial vehicle at sea provided in the embodiment of the present application relates to the field of offshore bridge pier inspection control technology. The control method of the unmanned aerial vehicle at sea provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the control method of the unmanned aerial vehicle at sea, etc., but is not limited to the above forms.

[0067] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0068] Figure 1 is an optional flow chart of a method for controlling a marine unmanned aerial vehicle provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S180:

[0069] Step S110, building a control model for the marine unmanned aerial vehicle;

[0070] Step S120, obtaining the real-time model inertial mass parameter and the real-time model damping parameter of the control model;

[0071] Step S130, inputting the real-time model inertial mass parameter and the real-time model damping parameter into the online identification model to obtain the modified model parameter;

[0072] Step S140, constructing a sliding mode control surface according to the modified model parameters;

[0073] Step S150, generating first control data of the marine unmanned vehicle through a sliding mode control surface, wherein the first control data includes a longitudinal thrust and a lateral bow moment;

[0074] Step S160, obtaining the interference amount of the unmanned aerial vehicle at sea during navigation by using an interference observer;

[0075] Step S170, adjusting the first control data according to the interference amount to obtain second control data;

[0076] Step S180: Control the inspection trajectory of the marine unmanned aerial vehicle through the second control data.

[0077] In the embodiment of the present application, during the inspection and flaw detection of bridge piers, the plane trajectory tracking task is given priority, so a three-degree-of-freedom motion model is selected as the control model of the aircraft. The three-degree-of-freedom motion model is shown in formula (1):

[0078]

[0079] In the formula, A matrix representing the position and attitude information of the unmanned aerial vehicle at sea; represents the first-order derivative of η, x, y, Respectively represent the north coordinate, east coordinate and heading angle of the unmanned aerial vehicle at sea; υ=[u,v,r] T A velocity information matrix representing the marine unmanned aerial vehicle; The first-order derivatives u, v, and r of υ represent the longitudinal velocity, lateral velocity, and heading angular velocity of the unmanned marine vehicle, respectively; d = [d u ,d v ,d r ] T represents the external interference matrix; τ c =[τ u ,0,τ r ] represents the control quantity matrix composed of control force and control torque; represents the rotation matrix of the bow roll; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

[0080] It can be understood that the rotation matrix of the bow roll is shown in formula (2), the inertia coefficient matrix is ​​shown in formula (3), the Coriolis force and centripetal force matrix is ​​shown in formula (4), and the damping coefficient matrix is ​​shown in formula (5):

[0081]

[0082] In the formula, m 11 ,m 22 ,m 33 ,d 11 ,d 22 ,d 33 represent the model inertial mass parameter and model damping parameter respectively.

[0083] It can be understood that the three-degree-of-freedom motion model gives the longitudinal thrust τ u and the lateral bow moment τ r and the spacecraft state x, y, The nonlinear relationship between u, v, and r. Based on the three-degree-of-freedom model, the fourth-order Runge-Kutta integration method is used to integrate the position information, heading angle information, and related speed information of the vehicle. Based on this principle, a trajectory tracking control system simulation platform can be built.

[0084] In an embodiment of the present application, an online system identification model can be fed back based on the navigation status data to obtain real-time model inertial mass parameters and model damping parameters, thereby realizing adaptive adjustment of control parameters of the trajectory tracking control system as the external environment changes.

[0085] It can be understood that, in this embodiment, according to the real-time model inertial mass parameter and the real-time model damping parameter, the previous model parameter value is corrected by the recursive least square method to obtain a new model parameter value as the corrected model parameter. Specifically, the working principle of the online system identification model based on the navigation state data feedback is as follows: Figure 2 As shown. The online identification method selects the recursive least squares method. The recursive least squares method can be used to correct the control parameters of the unmanned aerial vehicle during navigation in real time. After each new system input and output is obtained, the parameter value of the previous identification is corrected using the new system input and output on the basis of the previous parameter identification value, and then a new parameter identification value is obtained recursively. By introducing this online identification algorithm based on navigation data correction, the control parameters are dynamically adjusted and optimized to provide a practical, fast and efficient parameter optimization method.

[0086] In the embodiment of the present application, the three-degree-of-freedom model dynamic equation in formula (1) is expanded and deformed to obtain formula (6):

[0087]

[0088] Modify formula (6) to the matrix form of Y = Hθ, and let:

[0089] Y=[τ u ,0,τ] T ;

[0090] θ=[m 11 ,m 22 ,m 33 ,d 11 ,d 22 ,d 33 ] T ;

[0091]

[0092] In the formula, Y is the output matrix of the system; H is the input matrix of the system, and θ is the parameter matrix of the system.

[0093] Then, through the recursive least squares identification method, the parameter matrix can be obtained through the real-time changing input matrix, and the real-time update of the model parameters can be realized, thereby solving the problem of parameter uncertainty, and at the same time increasing the robustness of the control system and improving the adaptability to environmental interference.

[0094] In the embodiment of the present application, the modified model parameter θ obtained by the above steps is 11 ,m 22 ,m 33 ,d 11 ,d 22 ,d 33 ] T , and based on this, a trajectory tracking sliding mode controller is designed. Specifically, the position error information of the unmanned aerial vehicle at sea is first converted into speed error information, and then a sliding mode control surface is constructed according to the speed error information. The control principle of the sliding mode control surface is as follows Figure 3 shown.

[0095] It can be understood that this embodiment determines the position error information according to the expected trajectory and actual position information of the unmanned aerial vehicle at sea, and the process is shown in formula (7):

[0096]

[0097] Then, a stability proof function (Lyapunov function) corresponding to the position error information is constructed as shown in formula (8):

[0098]

[0099] Calculate the derivative of the stability proof function as shown in formula (9):

[0100]

[0101] Then, the control law of virtual lateral velocity and longitudinal velocity is designed as shown in formula (10) by the idea of ​​backstepping algorithm:

[0102]

[0103] Among them, k 1 ,k 2 , C is a positive number, let α u ,α v are the virtual longitudinal and lateral desired velocities respectively. At this point, the trajectory tracking problem is transformed into a velocity tracking problem, and the velocity error is shown in formula (11):

[0104]

[0105] Among them, u erepresents the longitudinal velocity tracking error, v e represents the lateral velocity tracking error;

[0106] According to the speed error information, the sliding mode control surface is constructed as shown in formula (12):

[0107]

[0108] Among them, s 1 、s 2 They represent the sliding mode functions constructed for the longitudinal and lateral velocity errors respectively; τ represents the integral variable, and the range of the integral variable is [0, t]; λ 1 ,λ 2 ,λ 3 Represents design parameters, all greater than 0; u e and v e Indicates the speed error information; Indicates v e The first derivative of .

[0109] After taking the derivative of formula (12), we get formula (13):

[0110]

[0111] in, is α u The first derivative of v The relevant first-order and second-order derivatives of are shown in formula (14):

[0112]

[0113] In formula (14), is the first-order derivative of p, and the expression of p is shown in formula (15):

[0114]

[0115] The designed exponential sliding mode approach rate is shown in formula (16):

[0116]

[0117] Among them, ε 1 ,ε 2 ,η 1 ,η 2 is the designed control parameter; sign(.) is the function sign. Due to the discontinuity of the sign function, the sliding mode controller will have a chattering problem. The hyperbolic tangent function is selected to replace the sign function to design the sliding mode reaching law and obtain a new sliding mode reaching law. Finally, the longitudinal thrust and bow moment of the vehicle are obtained by formula (17):

[0118]

[0119] In formula (17), Yes e The first derivative of Represents the second derivative of the lateral velocity.

[0120] In the embodiment of the present application, due to the complexity of the motion environment of the marine unmanned aerial vehicle, it is often affected by external interference. The parameter identification part can offset part of the external interference and the influence of internal uncertainty, but there is still a certain deviation during the navigation process. The nonlinear interference observer can estimate and compensate for the external interference and model uncertainty more accurately. Therefore, this embodiment improves the system's tolerance to uncertain interference by designing a nonlinear interference observer, thereby improving the control ability of the marine vehicle trajectory tracking control system. It can be understood that the interference observer is shown in formula (18):

[0121]

[0122] Formula (18), Z = [Z u ,Z v ,Z r ] T Indicates the construction quantity; represents the first-order derivative of Z; L = diag{L u ,L v ,L r} represents the bandwidth diagonal matrix of the interference observer; L u ,L v ,L r They represent the design parameters of the disturbance observer, which are all greater than 0; d=[d u ,d v ,d r ] T represents the external interference matrix; represents the first-order derivative of d; is the observed value of the disturbance observer to the disturbance, v represents the lateral velocity of the unmanned marine vehicle; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

[0123] In an embodiment of the present application, after the interference amount is obtained by the interference observer, the first control data of the aircraft is adjusted again to obtain the second control data, so that the inspection trajectory of the aircraft can be controlled by the second control data.

[0124] In some embodiments, the control method of the present application embodiment is experimentally compared with the existing PID control method, and the following results can be obtained: Figure 4 , Figure 5 and Figure 6 As shown. Figure 4 , Figure 5 and Figure 6 It can be seen that under the conditions of straight line disclosure, circular disclosure or curve disclosure, the actual navigation trajectory of the aircraft controlled by the method provided in the embodiments of the present application is closer to the expected trajectory.

[0125] It can be seen that the method of the embodiment of the present application realizes the adaptive optimization adjustment of the control parameters of the ocean vehicle when facing a complex ocean environment by adding the navigation data feedback correction model parameter algorithm and the interference observer algorithm to the control model of the vehicle, which greatly improves the accuracy and stability of the trajectory tracking control of the ocean vehicle. At the same time, the embodiment of the present application adopts the fourth-order Runge-Kutta integral method to build the control model, and adopts the identification method based on the navigation state data feedback in the trajectory tracking control algorithm to obtain the real-time model parameters and correct the control model; at the same time, the position tracking error is transferred to the speed tracking error through the design principle of the backstepping method, and the sliding mode controller is designed on this basis. The inverse tangent function is used to optimize the approach rate in the selection of the approach rate, reduce the jitter of the sliding mode control, increase the stability of the control, and ensure the smooth navigation of the vehicle during the flaw detection process, and improve the detection efficiency.

[0126] Reference Figure 7 The present application provides a control device for a marine unmanned aerial vehicle, the device comprising:

[0127] The first module 710 is used to build a control model of the marine unmanned aerial vehicle;

[0128] The second module 720 is used to obtain the real-time model inertial mass parameter and the real-time model damping parameter of the control model;

[0129] The third module 730 is used to input the real-time model inertial mass parameter and the real-time model damping parameter into the online identification model to obtain the modified model parameter;

[0130] A fourth module 740 is used to construct a sliding mode control surface according to the modified model parameters;

[0131] A fifth module 750 is used to generate first control data of the marine unmanned vehicle through a sliding mode control surface, wherein the first control data includes a longitudinal thrust and a lateral bow moment;

[0132] The sixth module 760 is used to obtain the interference amount of the unmanned aerial vehicle at sea during navigation through the interference observer;

[0133] The seventh module 770 is used to adjust the first control data according to the interference amount to obtain second control data;

[0134] The eighth module 780 is used to control the inspection trajectory of the marine unmanned aerial vehicle through the second control data.

[0135] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0136] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the control method of the above-mentioned unmanned aerial vehicle at sea when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0137] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0139] The processor 810 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0140] The memory 820 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 820 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 820, and the processor 810 calls and executes the control method of the unmanned marine vehicle in the embodiment of the present application;

[0141] Input / output interface 830, used to implement information input and output;

[0142] The communication interface 840 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0143] bus 850 , which transmits information between the various components of the device (e.g., processor 810 , memory 820 , input / output interface 830 , and communication interface 840 );

[0144] The processor 810 , the memory 820 , the input / output interface 830 and the communication interface 840 are connected to each other in communication within the device via a bus 850 .

[0145] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the control method of the above-mentioned unmanned marine vehicle is implemented.

[0146] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0147] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 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 embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor 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.

[0148] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0149] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0150] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0152] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0153] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0154] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0158] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for controlling a marine unmanned aerial vehicle, characterized in that: The method comprises the following steps: Building a control model for the unmanned marine vehicle; Acquiring a real-time model inertial mass parameter and a real-time model damping parameter of the control model; Inputting the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter; constructing a sliding mode control surface according to the modified model parameters; generating first control data of the unmanned marine vehicle through the sliding mode control surface, wherein the first control data includes longitudinal thrust and lateral bow moment; Obtaining the interference amount of the unmanned aerial vehicle at sea during navigation by means of an interference observer; Adjust the first control data according to the interference amount to obtain second control data; The inspection trajectory of the marine unmanned aerial vehicle is controlled by the second control data.

2. The method according to claim 1, characterized in that The control model includes a three-degree-of-freedom motion model, and the three-degree-of-freedom motion model is as follows: in, A matrix representing the position and attitude information of the unmanned aerial vehicle at sea; represents the first-order derivative of η, Respectively represent the north coordinate, east coordinate and heading angle of the unmanned aerial vehicle at sea; υ=[u,v,r] T A velocity information matrix representing the marine unmanned aerial vehicle; The first-order derivatives u, v, and r of υ represent the longitudinal velocity, lateral velocity, and heading angular velocity of the unmanned marine vehicle, respectively; d = [d u ,d v ,d r ] T represents the external interference matrix; τ c =[τ u ,0,τ r ] represents the control quantity matrix composed of control force and control torque; represents the rotation matrix of the bow roll; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

3. The method according to claim 1, characterized in that The step of inputting the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter comprises: According to the real-time model inertial mass parameter and the real-time model damping parameter, the previous model parameter value is corrected by recursive least square method to obtain a new model parameter value as the corrected model parameter.

4. The method according to claim 1, characterized in that: The constructing of the sliding mode control surface comprises: Converting the position error information of the unmanned marine vehicle into speed error information; The sliding mode control surface is constructed according to the speed error information.

5. The method according to claim 4, characterized in that The converting the position error information of the unmanned marine vehicle into speed error information comprises: Determining position error information based on the expected trajectory and actual position information of the marine unmanned aerial vehicle; Constructing a stability proof function corresponding to the position error information; Calculating a derivative of the stability proof function; The speed error information is determined according to the derivative function through a backstepping algorithm.

6. The method according to claim 4, characterized in that The sliding mode control surface is as follows: Wherein, s1 and s2 represent sliding mode functions constructed for longitudinal and lateral velocity errors, respectively; τ represents an integral variable, and the range of the integral variable is [0, t]; λ1, λ2, λ3 represent design parameters; u e and v e Indicates the speed error information; Indicates v e The first derivative of .

7. The method according to claim 1, characterized in that The disturbance observer is as follows: Among them, Z=[Zu,Zv,Zr] T Indicates the construction quantity; represents the first-order derivative of Z; L = diag{L u ,L v ,L r } represents the bandwidth diagonal matrix of the interference observer; L u ,L v ,L r denote the design parameters of the disturbance observer respectively; d=[d u ,d v ,d r ] T represents the external interference matrix; represents the first-order derivative of d; is the observed value of the disturbance observer to the disturbance, v represents the lateral velocity of the unmanned marine vehicle; M represents the inertia coefficient matrix; C(υ) represents the Coriolis force and centripetal force matrix; D(υ) represents the damping coefficient matrix.

8. A control device for a marine unmanned aerial vehicle, characterized in that: The device comprises: The first module is used to build a control model of the marine unmanned aerial vehicle; The second module is used to obtain the real-time model inertial mass parameter and the real-time model damping parameter of the control model; A third module is used to input the real-time model inertial mass parameter and the real-time model damping parameter into an online identification model to obtain a modified model parameter; A fourth module is used to construct a sliding mode control surface according to the modified model parameters; A fifth module is used to generate first control data of the unmanned marine vehicle through the sliding mode control surface, wherein the first control data includes longitudinal thrust and lateral bow moment; The sixth module is used to obtain the interference amount of the unmanned aerial vehicle at sea during navigation through an interference observer; A seventh module is used to adjust the first control data according to the interference amount to obtain second control data; An eighth module is used to control the inspection trajectory of the marine unmanned aerial vehicle through the second control data.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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