Anti-interference underactuated unmanned surface vehicle control method and device

Abstract

The present application relates to unmanned surface vehicle control technical field, especially pointing to a kind of anti-interference underactuated unmanned surface vehicle control method and device.Anti-interference underactuated unmanned surface vehicle control method includes: obtaining vehicle parameter;The motion state of vehicle is data collection by sensor, and motion state information is obtained;According to vehicle parameter and motion state information, model construction is carried out, and kinematics model and dynamics model are obtained;According to kinematics model and dynamics model, control rate is obtained;According to control rate, controller is designed by backstepping method, and surface underactuated controller is obtained;Obtain desired motion trajectory;According to desired motion trajectory, motion control is carried out by the controller of surface underactuated vehicle.The present application is a kind of underactuated unmanned surface vehicle control method with strong robustness and high computing efficiency.

Description

Technical Field

[0001] This invention relates to the field of unmanned surface vehicle control technology, and in particular to an interference-resistant underactuated unmanned surface vehicle control method and device. Background Technology

[0002] Unmanned surface vessels (USVs) are highly flexible and adaptable and capable of undertaking high-risk missions. As a result, they are widely used in marine monitoring, rescue, resource management and environmental protection, providing support and protection for human marine activities.

[0003] Unmanned surface vessels (USVs) can be categorized into underactuated and fully actuated types. Fully actuated USVs offer greater maneuverability, but their mechanical structure is more complex, resulting in higher energy consumption and cost. In contrast, underactuated USVs require fewer actuators and controllers, have longer operating times, and lower costs, making them promising for a wide range of applications. The core challenge for USVs during missions is motion control. However, underactuated USV systems are very difficult to control. Furthermore, the real-world surface environment is complex and variable, with disturbances caused by wind, waves, or ocean currents. Improper handling of these disturbances can lead to a decline in control performance.

[0004] Currently, extensive research has been conducted both domestically and internationally on the control of unmanned underwater vehicles. Traditional control methods, such as PID control, are characterized by their simple structure, but their parameters have a significant impact on performance and are difficult to apply to complex systems. Fuzzy control methods can be used for nonlinear systems, but often involve complex system modeling and rule design processes. Adaptive control methods are suitable for situations where system parameters vary greatly or are uncertain, but they consume significant computational resources. In addition, some studies have simplified models to reduce system complexity, but in practical applications, model simplification can lead to poor control robustness under different external conditions.

[0005] In the existing technology, there is a lack of a robust and computationally efficient control method for underactuated unmanned surface vehicles. Summary of the Invention

[0006] To address the technical problem of poor control performance of underactuated unmanned surface vehicles (USVs) in real-world environments, embodiments of the present invention provide an interference-resistant underactuated USV control method and apparatus. The technical solution is as follows: On the one hand, an interference-resistant underactuated unmanned surface vehicle control method is provided, which is implemented by the vehicle control device and includes: Acquire vehicle parameters; collect data on the vehicle's motion state through sensors to obtain motion state information; Based on the vehicle parameters and the motion state information, a model is constructed to obtain a kinematic model and a dynamic model. Based on the kinematic and dynamic models, the control law is obtained; based on the control law, a controller is designed using the backstepping method to obtain a surface underactuated controller. Obtain the desired motion trajectory; based on the desired motion trajectory, perform motion control through the controller of the underactuated surface vehicle.

[0007] Optionally, the control law includes a first control law, and obtaining the control law based on the kinematic model and the dynamic model includes: The first position error and the second position error are determined based on the kinematic model and the dynamic model; the first position error and the second position error are used to prevent singularities from occurring in the update rate; Based on the first position error, a function is constructed to obtain the first Lyapunov function; Based on the first position error and the second position error, a function is constructed to obtain the second Lyapunov function and the third Lyapunov function; Based on a preset fuzzy logic system, the unknown disturbance of the thrust is estimated according to the second position error to obtain the first unknown disturbance term estimate; The first control law is obtained by estimating the first Lyapunov function, the second Lyapunov function, the third Lyapunov function, and the first unknown disturbance term.

[0008] The formula for calculating the first update rate is as follows (1): (1) in, It is a unit vector. This is for virtual input.

[0009] The mathematical expression for the estimation of the first unknown interference term is as follows (2): (2) in, It is a diagonal positive definite matrix; s1 is the fuzzy basis function vector; s2 is the second position error; For the rigid body mass of the aircraft; This is a mapping function.

[0010] Optionally, the control law includes a second control law, and obtaining the control law based on the kinematic model and the dynamic model includes: The third tracking error is determined based on the kinematic and dynamic models. Based on the first position error, the second position error and the third tracking error, a function is constructed to obtain the fourth Lyapunov function and the fifth Lyapunov function. Based on a preset fuzzy logic system, the unknown interference of torque is estimated according to the third tracking error to obtain the second unknown interference term estimate; The second update rate is obtained by estimating based on the fourth Lyapunov function, the fifth Lyapunov function, and the second unknown interference term.

[0011] The formula for calculating the second update rate is as follows (3): (3) in, for Fuzzy logic estimation, s3 is the third tracking error; ,in, It is a unit vector. It is a constant matrix. For rotation matrix, For the desired trajectory, It is a constant vector.

[0012] The mathematical expression for the estimation of the second unknown interference term is as follows (4): (4) in, It is a diagonal positive definite matrix; is a fuzzy basis function vector.

[0013] On the other hand, an interference-resistant underactuated unmanned surface vehicle (USV) control device is provided, which is applied to an interference-resistant underactuated USV control method. The device includes: The data acquisition module is used to acquire vehicle parameters; it collects motion state data from sensors to obtain motion state information. The model building module is used to build a model based on the vehicle parameters and the motion state information to obtain a kinematic model and a dynamic model. The controller design module is used to obtain the control law based on the kinematic model and the dynamic model; and to design the controller using the backstepping method based on the control law to obtain the underactuated surface controller. The vehicle motion module is used to acquire the desired motion trajectory; based on the desired motion trajectory, the motion control is performed by the controller of the underactuated surface vehicle.

[0014] Optionally, the controller design module is further configured to: The first position error and the second position error are determined based on the kinematic model and the dynamic model; the first position error and the second position error are used to prevent singularities from occurring in the update rate; Based on the first position error, a function is constructed to obtain the first Lyapunov function; Based on the first position error and the second position error, a function is constructed to obtain the second Lyapunov function and the third Lyapunov function; Based on a preset fuzzy logic system, the unknown disturbance of the thrust is estimated according to the second position error to obtain the first unknown disturbance term estimate; The first control law is obtained by estimating the first Lyapunov function, the second Lyapunov function, the third Lyapunov function, and the first unknown disturbance term.

[0015] The formula for calculating the first update rate is as follows (1): (1) in, It is a unit vector. This is for virtual input.

[0016] The mathematical expression for the estimation of the first unknown interference term is as follows (2): (2) in, It is a diagonal positive definite matrix; s1 is the fuzzy basis function vector; s2 is the second position error; For the rigid body mass of the aircraft; This is a mapping function.

[0017] Optionally, the controller design module is further configured to: The third tracking error is determined based on the kinematic and dynamic models. Based on the first position error, the second position error and the third tracking error, a function is constructed to obtain the fourth Lyapunov function and the fifth Lyapunov function. Based on a preset fuzzy logic system, the unknown interference of torque is estimated according to the third tracking error to obtain the second unknown interference term estimate; The second update rate is obtained by estimating based on the fourth Lyapunov function, the fifth Lyapunov function, and the second unknown interference term.

[0018] The formula for calculating the second update rate is as follows (3): (3) in, for Fuzzy logic estimation, s3 is the third tracking error; ,in, It is a unit vector. It is a constant matrix. For rotation matrix, For the desired trajectory, It is a constant vector.

[0019] The mathematical expression for the estimation of the second unknown interference term is as follows (4): (4) in, It is a diagonal positive definite matrix; is a fuzzy basis function vector.

[0020] On the other hand, a vehicle control device is provided, the vehicle control device comprising: a processor; a memory storing computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, any one of the above-described interference-resistant underactuated unmanned surface vehicle control methods is implemented.

[0021] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described interference-resistant underactuated unmanned surface vehicle control methods.

[0022] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: This invention proposes a robust and computationally efficient control method for underactuated unmanned surface vessels (USVs). Based on fuzzy logic, it performs virtual estimation of the drive input, effectively reducing the impact of disturbances and making it suitable for handling more complex time-varying interferences. By constructing relevant Lyapunov functions, the control laws for thrust and torque are iteratively obtained. Compared with the traditional backstepping method, the proposed control scheme requires fewer backstepping iterations. This invention approximates the velocity error to a small constant vector range, rather than driving it close to zero, thus ensuring that the system error is eventually uniformly bounded. This invention provides a robust and computationally efficient control method for underactuated USVs. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of an anti-interference underactuated unmanned surface vehicle control method provided by an embodiment of the present invention; Figure 2 This is a block diagram of an anti-interference underactuated unmanned surface vehicle control device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0026] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0027] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0028] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0029] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0030] This invention provides an anti-interference underactuated unmanned surface vehicle (USV) control method, which can be implemented by a vehicle control device, such as a terminal or a server. Figure 1 The flowchart shown is a control method for an anti-interference underactuated unmanned surface vehicle. The processing flow of this method may include the following steps: S1. Acquire vehicle parameters; collect data on the vehicle's motion state through sensors to obtain motion state information.

[0031] In one feasible implementation, the underactuated unmanned surface vehicle used in this invention has a length of 1.2 meters, a mass of 17.5 kg, and a rigid body mass of [missing information]. Inertia is In addition, other inherent parameters of the aircraft are collected through sensors mounted on the aircraft, such as linear velocity and angular velocity.

[0032] S2. Based on the vehicle parameters and motion state information, construct a model to obtain a kinematic model and a dynamic model.

[0033] In one feasible implementation, after obtaining the basic information, a global coordinate system is constructed with the aircraft as the origin. and the carrier coordinate system The mathematical expression for the kinematic model of the underactuated unmanned surface vehicle is shown in equation (1) below: (1) in, The center of gravity; Let be the linear velocity in the carrier coordinate system, where For surge velocity, For the swing speed, Indicates from arrive Rotation matrix of coordinate system The expression is given by the following equation (2): (2) in, Let yaw rate be angular velocity. The dynamic model of the underactuated unmanned surface vehicle is as follows: (3) (3) in, , , , and These are the rigid body mass and moment of inertia, respectively. and This indicates a corresponding increase in mass and inertia. The driving input is... and Unknown items are used and Indicate, and satisfy , ,in, and Zhengding.

[0034] S3. Obtain the control law based on the kinematic and dynamic models; based on the control law, design the controller using the backstepping method to obtain the underactuated surface controller.

[0035] In one feasible implementation, the present invention is designed and The control law enables the underactuated unmanned surface vehicle to operate in unknown conditions. and In the presence of such a trajectory, it can track the desired path. .

[0036] Optionally, the first update rate is obtained based on the kinematic model and the dynamic model, including: The first position error and the second position error are determined based on the kinematic model and the dynamic model; the first position error and the second position error are used to prevent singularities from occurring in the update rate. The first Lyapunov function is obtained by constructing a function based on the first position error. Based on the first position error and the second position error, a function is constructed to obtain the second Lyapunov function and the third Lyapunov function; Based on a preset fuzzy logic system, the unknown disturbance of the thrust is estimated according to the second position error, and the first unknown disturbance term is estimated. The first control law is obtained by estimating the first Lyapunov function, the second Lyapunov function, the third Lyapunov function, and the first unknown disturbance term.

[0037] In one feasible implementation, the position error is defined based on the parameters and information obtained in the above steps, and the mathematical expression of the first position error is as follows (4): (4) The position error reflects the difference between the desired trajectory and the actual trajectory of the unmanned surface vehicle. To achieve the goal of tracking the desired trajectory, this error should be as small as possible. Based on the first position error, the first Lyapunov function is defined as follows, and its mathematical expression is as follows (5): (5) Since the Lyapunov function is non-negative and correlated with the error term, its first derivative expression is further analyzed to minimize the function value. Differentiating equation (5) yields equation (6), which is shown below: (6) in, , It is a positive definite matrix.

[0038] To ensure that equation (6) is not greater than zero, a second position error is defined, and its mathematical expression is as follows: equation (7): (7) in, It is a constant vector.

[0039] Because of the non-integrable acceleration constraint in the underactuated unmanned surface vehicle model, the system cannot be stabilized by a smooth time-invariant feedback control law. Traditional backstepping can lead to singularities in the control law. To solve this problem, based on formulas (4) and (7), the velocity error is approximated to a small constant vector. Instead of driving it close to zero, the systematic error is eventually uniformly bounded within a certain range.

[0040] Substitute the above equation (7) into Equation (8) is obtained, and equation (8) is shown below: (8) To obtain the input thrust of the unmanned surface vehicle Meanwhile, the error term is also considered. and With the introduction of , the second Lyapunov equation is defined as follows (9): (9) Differentiating it, we get equation (10) as shown below: (10) in, , It is a positive definite matrix. , , . It can be regarded as a virtual input, but due to the fact that in equation (10) The unknown is approximated using fuzzy logic systems; unknown interference. The estimate is shown in equation (11): (11) in, For an ideal weight matrix, It is a constant. This represents the reconstruction error.

[0041] because and Unknown but bounded It can be approximated by equation (12), which is shown in the following equation: (12) in, It is an estimate of the ideal weight matrix, with an approximate estimation error of... , In order to obtain The first update law, while considering the approximate estimation error. The introduction of Lyapunov defines the third Lyapunov function as shown in equation (13): (13) in, It is a diagonal positive definite constant matrix. For Differentiating yields equation (14): (14) choose The desired virtual input is as follows (15): (15) in, By setting appropriately , It is always reversible.

[0042] The formula for calculating the first update rate is as follows (16): (16) in, It is a unit vector. This is for virtual input.

[0043] The formula for calculating the first update rate thrust is as shown in equation (16) above: At this point, the control input thrust of the underactuated surface vehicle... The update rate has been obtained. Thrust is the power generated by the thruster, used to propel surface vehicles and thus perform various tasks.

[0044] The desired angular velocity is given by the following formula (17): (17) in, , .

[0045] The mathematical expression for the estimation of the first unknown disturbance term is as follows (18): (18) in, It is a diagonal positive definite matrix; s1 is the fuzzy basis function vector; s2 is the second position error; For the rigid body mass of the aircraft; This is a mapping function.

[0046] in,

[0047] In one feasible implementation, in the above update rate Guaranteed Bounded; and .

[0048] Optionally, a second update rate is obtained based on the kinematic model and the dynamic model, including: The third tracking error is determined based on the kinematic and dynamic models. Based on the first position error, the second position error, and the third tracking error, functions are constructed to obtain the fourth Lyapunov function and the fifth Lyapunov function. Based on a pre-defined fuzzy logic system, the second update rate is obtained according to the fourth and fifth Lyapunov functions.

[0049] In one feasible implementation, based on the constructed kinematic and dynamic models, since equation (17) still contains unknown terms, a third tracking error is defined, and its mathematical expression is as follows (19): (19) Considering the error term With the introduction of , the corresponding fourth Lyapunov function is defined as follows (20): (20) Differentiating equation (20) yields equation (21): (twenty one) set up , It can be approximated by the following equation (22) using a fuzzy logic system: (twenty two) Solve The update law is used to define the fifth Lyapunov function, as shown in equation (23): (twenty three) Differentiating equation (23) yields equation (24) as shown below: (twenty four) in, , .

[0050] The formula for calculating the second update rate is as follows (25): (25) in, for Fuzzy logic estimation, s3 is the third tracking error; ,in, It is a unit vector. It is a constant matrix. For rotation matrix, For the desired trajectory, It is a constant vector.

[0051] The control input torque of the underactuated surface vehicle is obtained based on the above steps. The update rate. Torque is the torque generated by the servo motor of a surface vehicle, used to control its course. By generating torque in different directions, the unmanned surface vehicle can navigate along a predetermined route.

[0052] Control input thrust of underactuated surface vehicles With torque It plays a crucial role in various tracking tasks of surface vehicles. Thrust drives the underactuated surface vehicle forward, while torque controls its course. The combination of these two aspects enables the underactuated surface vehicle to navigate autonomously and intelligently and perform various tasks.

[0053] The mathematical expression for the estimation of the second unknown disturbance term is as follows (26): (26) in, It is a diagonal positive definite matrix; is a fuzzy basis function vector.

[0054] In one feasible implementation, according to the above steps Transformed into equation (27): (27) By projection achievable Bounded; . Bounded, Established.

[0055] S4. Obtain the desired motion trajectory; based on the desired motion trajectory, perform motion control through the controller of the underactuated surface vehicle.

[0056] The pre-defined fuzzy logic system is used to perform virtual estimation of the vehicle's drive input.

[0057] In one feasible implementation, the present invention considers the impact of underactuated properties, internal model uncertainties, and external disturbances on the system during the model building process. During the design of the control law using the backstepping method, an estimate of the unknown disturbance based on a fuzzy logic system is designed and embedded into the control input of the unmanned surface vehicle, thereby resisting the effects of limited disturbances.

[0058] This invention proposes a robust and computationally efficient control method for underactuated unmanned surface vessels (USVs). Based on fuzzy logic, it performs virtual estimation of the drive input, effectively reducing the impact of disturbances and making it suitable for handling more complex time-varying interferences. By constructing relevant Lyapunov functions, the control laws for thrust and torque are iteratively obtained. Compared with the traditional backstepping method, the proposed control scheme requires fewer backstepping iterations. This invention approximates the velocity error to a small constant vector range, rather than driving it close to zero, thus ensuring that the system error is eventually uniformly bounded. This invention provides a robust and computationally efficient control method for underactuated USVs.

[0059] Figure 2 This is a block diagram illustrating an interference-resistant underactuated unmanned surface vehicle (USV) control device according to an exemplary embodiment. The device is used in an interference-resistant underactuated USV control method. (Refer to...) Figure 2 The device includes a data acquisition module 210, a model building module 220, a controller design module 230, and a vehicle motion module 240. For ease of explanation, Figure 2 Only the main components of the full-process visualization device 200 are shown: The data acquisition module 210 is used to acquire vehicle parameters; it collects motion state data of the vehicle through sensors to obtain motion state information. The model building module 220 is used to build a model based on the vehicle parameters and motion state information to obtain a kinematic model and a dynamic model. The controller design module 230 is used to obtain the control law based on the kinematic model and the dynamic model; and to design the controller using the backstepping method based on the control law to obtain the underactuated surface controller. The vehicle motion module 240 is used to acquire the desired motion trajectory; based on the desired motion trajectory, the motion control is performed by the controller of the underactuated surface vehicle.

[0060] Optionally, the controller is designed with a module rate of 230, which is further used for: The first position error and the second position error are determined based on the kinematic model and the dynamic model; the first position error and the second position error are used to prevent singularities from occurring in the update rate. The first Lyapunov function is obtained by constructing a function based on the first position error. Based on the first position error and the second position error, a function is constructed to obtain the second Lyapunov function and the third Lyapunov function; Based on a preset fuzzy logic system, the unknown disturbance of the thrust is estimated according to the second position error to obtain the first unknown disturbance term; The first control law is obtained based on the first Lyapunov function, the second Lyapunov function, the third Lyapunov function, and the first unknown disturbance term.

[0061] The formula for calculating the first update rate is as follows (1): (1) in, It is a unit vector. This is for virtual input.

[0062] The mathematical expression for the first unknown interference term is as follows (2): (2) in, It is a diagonal positive definite matrix; s1 is the fuzzy basis function vector; s2 is the second position error; For the rigid body mass of the aircraft; This is a mapping function.

[0063] Optionally, the controller design module 230 is further used for: The third tracking error is determined based on the kinematic and dynamic models. Based on the first position error, the second position error, and the third tracking error, functions are constructed to obtain the fourth Lyapunov function and the fifth Lyapunov function. Based on a preset fuzzy logic system, the unknown disturbance of torque is estimated according to the third tracking error to obtain the second unknown disturbance term; The second update rate is obtained based on the fourth Lyapunov function, the fifth Lyapunov function, and the second unknown interference term.

[0064] The formula for calculating the second update rate is as follows (3): (3) in, for Fuzzy logic estimation, s3 is the third tracking error; ,in, It is a unit vector. It is a constant matrix. Let be a rotation matrix. For the desired trajectory, It is a constant vector.

[0065] The mathematical expression for the second unknown interference term is as follows (4): (4) in, It is a diagonal positive definite matrix; is a fuzzy basis function vector.

[0066] This invention proposes a robust and computationally efficient control method for underactuated unmanned surface vessels (USVs). Based on fuzzy logic, it performs virtual estimation of the drive input, effectively reducing the impact of disturbances and making it suitable for handling more complex time-varying interferences. By constructing relevant Lyapunov functions, the control laws for thrust and torque are iteratively obtained. Compared with the traditional backstepping method, the proposed control scheme requires fewer backstepping iterations. This invention approximates the velocity error to a small constant vector range, rather than driving it close to zero, thus ensuring that the system error is eventually uniformly bounded. This invention provides a robust and computationally efficient control method for underactuated USVs.

[0067] Figure 3 This is a schematic diagram of the structure of a spacecraft control device provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the aircraft control equipment may include the above-mentioned Figure 2 The illustrated control device for an interference-resistant, underactuated unmanned surface vehicle. Optionally, the vehicle control device 310 may include a processor 2001.

[0068] Optionally, the aircraft control device 310 may also include a memory 2002 and a transceiver 2003.

[0069] The processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0070] The following is combined with Figure 3 A detailed description of each component of the aircraft control equipment 310 is provided below: The processor 2001 is the control center of the aircraft control device 310. It can be a single processor or a collective term for multiple processing elements. For example, the processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0071] Optionally, the processor 2001 can perform various functions of the aircraft control device 310 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0072] In a specific implementation, as one example, the processor 2001 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0073] In a specific implementation, as one example, the aircraft control device 310 may also include multiple processors, for example... Figure 3 The processors 2001 and 2004 are shown. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0074] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0075] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the processor 2001 or exist independently, and may be accessed via the interface circuit of the aircraft control device 310. Figure 3 (Not shown in the image) is coupled to processor 2001, and this embodiment of the invention does not specifically limit this.

[0076] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0077] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0078] Alternatively, the transceiver 2003 can be integrated with the processor 2001, or it can exist independently and be connected to the interface circuit of the aircraft control device 310. Figure 3 (Not shown in the image) is coupled to processor 2001, and this embodiment of the invention does not specifically limit this.

[0079] It should be noted that, Figure 3 The structure of the aircraft control device 310 shown does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0080] Furthermore, the technical effects of the vehicle control device 310 can be referenced from the technical effects of the anti-interference underactuated unmanned surface vehicle control method described in the above method embodiments, and will not be repeated here.

[0081] It should be understood that the processor 2001 in this embodiment of the invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0082] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0083] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0084] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0085] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0086] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0089] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0092] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for an interference-resistant underactuated unmanned surface vehicle, characterized in that, The method includes: Acquire vehicle parameters; collect data on the vehicle's motion state through sensors to obtain motion state information; Based on the vehicle parameters and the motion state information, a model is constructed to obtain a kinematic model and a dynamic model. Based on the kinematic and dynamic models, the control law is obtained; based on the control law, a controller is designed using the backstepping method to obtain a surface underactuated controller. The control law includes a first control law, which is obtained based on the kinematic and dynamic models, and includes: The first position error and the second position error are determined based on the kinematic model and the dynamic model; the first position error and the second position error are used to prevent singularities from occurring in the update rate; Based on the first position error, a function is constructed to obtain the first Lyapunov function; Based on the first position error and the second position error, a function is constructed to obtain the second Lyapunov function and the third Lyapunov function; Based on a preset fuzzy logic system, the unknown disturbance of the thrust is estimated according to the second position error to obtain the first unknown disturbance term estimate; The first control law is obtained by estimating the first Lyapunov function, the second Lyapunov function, the third Lyapunov function, and the first unknown disturbance term. The mathematical expression for the first position error is as follows (4): （4）； in, Represents the transpose of the rotation matrix; The mathematical expression for the second position error is as follows (7): （7）； in, M represents the linear velocity in the carrier coordinate system. -1 This represents the inverse of the rigid body mass matrix; It is a constant vector; It is a positive definite matrix; Introducing the first position error and the second position error, the second Lyapunov equation is defined as follows (9): （9）； , This represents a virtual input, which is then approximated using a fuzzy logic system. For driving input; Indicates yaw rate; , This indicates the approximate estimation error; The ideal weight matrix; This represents an estimate of the ideal weight matrix; Introduction The third Yapnov function is defined as shown in equation (13): （13）； choose The desired virtual input is as follows (15): （15）； in, ; ,in, It is a unit vector. It is a constant matrix; It is a positive definite matrix; Unknown interference Approximate; The formula for calculating the first update rate is as follows (16): （16）； in, It is a unit vector. This is a virtual input; The mathematical expression for the estimation of the first unknown disturbance term is as follows (18): （18）； in, It is a diagonal positive definite matrix; s1 is the fuzzy basis function vector; s2 is the second position error; For the rigid body mass of the aircraft; For mapping functions; The control law includes a second control law, and obtaining the control law based on the kinematic model and the dynamic model includes: The third tracking error is determined based on the kinematic and dynamic models. Based on the first position error, the second position error and the third tracking error, a function is constructed to obtain the fourth Lyapunov function and the fifth Lyapunov function. Based on a preset fuzzy logic system, the unknown interference of torque is estimated according to the third tracking error to obtain the second unknown interference term estimate; The second update rate is obtained by estimating based on the fourth Lyapunov function, the fifth Lyapunov function, and the second unknown interference term; The third tracking error is expressed mathematically as follows (19): （19）； in, Indicates the desired yaw rate; Introducing a third tracking error, we define a fourth Lyapunov function, as shown in equation (20): （20）； in, , Represents the moment of inertia of a rigid body. This indicates an increase in inertia; set up , Indicates an unknown term. It can be approximated by the following equation (22) using a fuzzy logic system: （22）； ； Introduction The fifth Lyapunov function is defined as follows (23): （23）； The mathematical expression for the estimation of the second unknown disturbance term is as follows (26): （26）； in, It is a diagonal positive definite matrix; For fuzzy basis function vectors; Obtain the desired motion trajectory; based on the desired motion trajectory, perform motion control through the controller of the underactuated surface vehicle.

2. The anti-interference underactuated unmanned surface vehicle control method according to claim 1, characterized in that, The formula for calculating the second update rate is as follows (25): （25）； in, for Fuzzy logic estimation, s3 is the third tracking error; Let be a rotation matrix. For the desired trajectory, It is a constant vector.

3. An interference-resistant underactuated unmanned surface vehicle (USV) control device, wherein the interference-resistant underactuated USV control device is used to implement the interference-resistant underactuated USV control method as described in any one of claims 1-2, characterized in that, The device includes: The data acquisition module is used to acquire vehicle parameters; it collects motion state data from sensors to obtain motion state information. The model building module is used to build a model based on the vehicle parameters and the motion state information to obtain a kinematic model and a dynamic model. The controller design module is used to obtain the control law based on the kinematic model and the dynamic model; and to design the controller using the backstepping method based on the control law to obtain the underactuated surface controller. The vehicle motion module is used to acquire the desired motion trajectory; based on the desired motion trajectory, the motion control is performed by the controller of the underactuated surface vehicle.

4. A type of aircraft control device, characterized in that, The vehicle control equipment includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 2.

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