Snakelike robot control method and device, medium and program product

Abstract

The invention relates to the technical field of robots, and particularly provides a snake-shaped robot control method and device, a medium and a program product, and the method comprises the steps: building a tracking error kinetic model of a snake-shaped robot based on a kinetic model and a tracking error of the snake-shaped robot; introducing a nonlinear function and fractional order differential, and constructing a nonlinear fractional order sliding mode surface; designing a superhelix approaching rate based on the nonlinear fractional order sliding mode surface; and according to the tracking error dynamic model, the nonlinear fractional order sliding mode surface and the super-spiral approaching rate, a super-spiral nonlinear fractional order sliding mode controller of the snake-shaped robot is constructed to control the snake-shaped robot. According to the method, through a nonlinear fractional order sliding mode surface control strategy, an error saturation suppression and amplification mechanism is ingeniously utilized, tracking errors are effectively reduced, the stability of the system is enhanced, and it is ensured that the snakelike robot can be rapidly and stably converged to the sliding mode surface through the super-spiral approaching rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot technology, in particular to a control method, device, medium and program product of a snake robot. BACKGROUND

[0002] Bionic snake robots are a kind of multi-joint series, flexible and highly adjustable mobile robots, which have the advantages of long structure, various motion forms, and flexible movement in narrow and unstructured environments. In recent years, they have been widely concerned and applied in power transmission line inspection, pipeline detection, post-disaster search and rescue, and online monitoring of complex industrial scenes.

[0003] However, in practical applications, due to the multi-degree-of-freedom, highly coupled, strong nonlinear and parameter time-varying dynamics of snake robots, the modeling process is complex and the solution is difficult, which brings great challenges to the design of their control algorithms. Complex control algorithms are often difficult to achieve efficient tracking control of the robot, while simple control algorithms are difficult to meet the demand for high-precision control. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, provide a control method, device, medium and program product of a snake robot, realize the overall coordination from modeling, observation to control, and improve the high-precision tracking and rapid response capability of the snake robot in complex power grid inspection and other scenes.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme:

[0006] In a first aspect, the present application provides a control method of a snake robot, comprising:

[0007] Based on the dynamics model of the snake robot and the tracking error, a tracking error dynamics model of the snake robot is constructed;

[0008] A nonlinear function and a fractional derivative are introduced to construct a nonlinear fractional order sliding surface;

[0009] Based on the nonlinear fractional order sliding surface, a super-helix approach rate is designed;

[0010] According to the tracking error dynamics model, the nonlinear fractional order sliding surface and the super-helix approach rate, a super-helix nonlinear fractional order sliding mode controller of the snake robot is constructed to control the snake robot;

[0011] The super-helix nonlinear fractional order sliding mode controller includes observation compensation for the uncertain terms and unknown disturbances of the tracking error dynamics model.

[0012] In some embodiments of the present application, based on the dynamic model of the snake robot, a tracking error is introduced, a tracking error dynamic model of the snake robot is constructed, comprising:

[0013] The original dynamic model of the snake robot is as follows:

[0014] ;

[0015] wherein, is the second-order differential of , , represents a joint angle vector of the snake robot; represents a joint angular velocity vector; represents the th joint angle of the snake robot, represents the number of joints of the snake robot; represents the centroid coordinates of the snake robot; represents the inertia matrix of the system; represents the Coriolis force and centripetal force matrix; represents the gravity and friction force matrix; represents the friction force matrix; represents the driving torque of each joint;

[0016] The tracking error of the snake robot is:

[0017] ;

[0018] wherein, represents the tracking error of the snake robot; represents the target trajectory of the snake robot, ; represents the actual trajectory of the snake robot;

[0019] The second-order differential of the tracking error of the snake robot is as follows:

[0020] ;

[0021] wherein, is the second-order differential of , the second-order differential of the second-order differential of

[0022] Based on the original dynamic model of the snake robot, a dynamic model of the snake robot is constructed;

[0023] The dynamic model of the snake robot is as follows:

[0024] ;

[0025] in, This represents the dimensionless parameter tuning gain matrix. ; This represents the combined impact of uncertainties and unknown disturbances. ; This represents the control input vector of the snake robot, corresponding to the driving torque of each joint;

[0026] By substituting the dynamic model of the snake robot into the second derivative of the tracking error, a dynamic model of the tracking error of the snake robot is constructed.

[0027] The tracking error dynamic model of the snake robot is as follows:

[0028] .

[0029] In some embodiments of the present invention, nonlinear functions and fractional derivatives are introduced to construct a nonlinear fractional sliding surface, including:

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] , ;

[0037] ;

[0038] in, Represents a nonlinear fractional sliding surface; The gain indicates the parameter tuning of the nonlinear term; The gain indicates the parameter tuning of the fractional derivative term; Represents a nonlinear function; express Nonlinear powers of functions express The width of the linear interval of the function satisfies: , ; To represent a fractional derivative, The order of the fractional derivative;

[0039] The nonlinear fractional sliding surface The first-order differential is as follows:

[0040] ;

[0041] ;

[0042] ;

[0043] in, yes Regarding time The first-order differential, They are Regarding time The first differential.

[0044] In some embodiments of the present invention, the superhelical convergence rate is designed as follows:

[0045] ;

[0046] ;

[0047] in, and The parameter tuning gain represents the superhelical approach rate. , ; , .

[0048] In some embodiments of the present invention, the superspiral nonlinear fractional sliding mode controller is as follows:

[0049] when hour:

[0050] ;

[0051] when hour:

[0052] ;

[0053] in, This represents the observation compensation for the uncertainty terms and unknown disturbances in the tracking error dynamics model.

[0054] In some embodiments of the present invention, after constructing a tracking error dynamic model of a snake robot based on the dynamic model and tracking error of the snake robot, the method further includes: using a radial basis function neural network observer to observe and compensate for the uncertainties and unknown disturbances in the tracking error dynamic model.

[0055] In some embodiments of the present invention, the radial basis function neural network observer is defined as follows:

[0056] ;

[0057] ;

[0058] ;

[0059] in, This represents an approximation of the uncertainty term and unknown disturbance in the tracking error dynamics model; , This represents the hidden layer activation function of the neural network observer. This represents the optimal weights of the neural network observer from the hidden layer to the output layer. Indicates being between and The approximate error, express The estimated value;

[0060] Wherein, the hidden layer activation function For cubic B-spline basis functions, Defined as:

[0061] ;

[0062] ;

[0063] ;

[0064] when and hour,

[0065] ;

[0066] when and hour,

[0067] ;

[0068] when and hour,

[0069] ;

[0070] in, Represents the Euclidean norm; This represents the distance between the input vector and the center vector; Represents the input vector; Represents the center vector; This represents the width of the cubic B-spline basis function.

[0071] Secondly, the present invention also provides an electronic device, including: a processor, and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the above-described control method for a snake-like robot.

[0072] Thirdly, the present invention also provides a non-transitory machine-readable medium storing computer instructions for causing the computer to execute the above-described control method for a snake robot.

[0073] Fourthly, the present invention also provides a computer program product, including a computer program / instructions, characterized in that, when the computer program / instructions are executed by a processor, the above-mentioned control method for the snake robot is implemented.

[0074] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0075] The snake robot control method provided by this invention accurately describes the deviation of the snake robot from the expected trajectory during motion by constructing an error dynamics model. It introduces a nonlinear fractional sliding surface control strategy, cleverly utilizing error saturation suppression and amplification mechanisms to effectively reduce tracking errors and significantly enhance system stability. Combined with the design of a superspiral approach rate, it ensures that the robot can converge to the sliding surface quickly and stably. Furthermore, it uses a radial basis function neural network observer to approximate the uncertainties and external disturbances in the system model, achieving high-precision and fast tracking control of the snake robot. Attached Figure Description

[0076] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0077] Figure 1 This is a flowchart illustrating a control method for a snake-like robot provided in an embodiment of the present invention;

[0078] Figure 2 This is a schematic diagram illustrating the principle framework of the control method for the snake robot according to an embodiment of the present invention;

[0079] Figure 3This is a comparison diagram showing the effect of the drivable joint angle 1 of the three-degree-of-freedom snake robot in the embodiment of the present invention tracking the target trajectory 1 by the control method of the embodiment of the present invention and the traditional PID control method;

[0080] Figure 4 This is a comparison diagram showing the effect of tracking the target trajectory 2 by the control method of the three-degree-of-freedom snake robot in the embodiment of the present invention and the traditional PID control method, respectively.

[0081] Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0082] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0083] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a control method for a snake-like robot. Figure 1 This is a flowchart illustrating the control method for the snake-like robot. This flowchart only shows the logical sequence of the method described in this embodiment. In other possible embodiments of the invention, different methods may be used, provided there are no conflicts. Figure 1 Complete the steps shown or described in the order indicated. Figure 2 This is a schematic diagram illustrating the principle framework of the control method for the snake-like robot.

[0084] See Figure 1 The method of this invention specifically includes the following steps:

[0085] Step S101: Based on the dynamic model and tracking error of the snake robot, construct the tracking error dynamic model of the snake robot.

[0086] The original dynamic model of the snake robot is as follows:

[0087] ;

[0088] in, for The second derivative, , Represents the joint angle vector of the snake robot; The vector of joint angular velocity is the first derivative of the change of the joint angles of the snake robot with time. The snake-like robot's first One joint angle, This indicates the number of joints in a snake-like robot; () represents the centroid coordinates of the snake robot.

[0089] Represents the system's inertia matrix. ,in, Represents the joint constraint matrix. Represents the joint space inertia matrix; Represents the equivalent moment of inertia of the motor; This represents a 2×2 identity matrix.

[0090] The matrix represents the Coriolis force and centripetal force, used to describe the inertial coupling forces of a snake robot under varying joint angular velocities. ,in, Represented by matrix The diagonalized matrix is ​​used to extract the angular velocity coupling term; This represents the coupling result of joint angular velocity under the action of the constraint matrix.

[0091] This represents the gravity matrix, used to describe the force distribution of the system under the influence of gravity. ,in, Represents the identity matrix or the zero matrix of the corresponding dimension; This represents the coefficient matrix related to the attitude angle; This represents the stiffness matrix, used to characterize the mechanical coupling stiffness between joints or links. This represents the direction cosine matrix (attitude angle selection matrix) related to the joint attitude angle.

[0092] Represents the friction force matrix. ,in, Indicates the tangential friction coefficient; Indicates the normal friction coefficient; This represents the attitude angle of the i-th joint (or body segment) of the snake robot, used to determine the angle between the direction of friction and the ground; and These represent the sine and cosine of the joint attitude angle, respectively, and are used to decompose the frictional force in the tangential and normal directions.

[0093] This represents the driving torque of each joint. .

[0094] The tracking error of the snake robot is expressed as:

[0095] ;

[0096] in, This represents the tracking error of the snake robot. ; This represents the target trajectory of the snake-like robot. ; This represents the actual trajectory of the snake-like robot.

[0097] The second derivative of the snake robot's tracking error is obtained as follows:

[0098] ;

[0099] in, for The second derivative, The second derivative, The second derivative;

[0100] Based on the original dynamic model of the snake robot described above, the dynamic model of the snake robot of this invention is constructed; the dynamic model is expressed as:

[0101] ;

[0102] in, This represents the dimensionless parameter tuning gain matrix. ; This represents the combined impact of uncertainties and unknown disturbances. ; This represents the control input vector of the snake robot, corresponding to the driving torque of each joint.

[0103] By substituting the dynamic model of the snake robot into the second derivative of the tracking error, the tracking error dynamic model of the snake robot of the present invention is constructed, and the model is expressed as follows:

[0104] .

[0105] Step S102: Introduce a nonlinear function and a fractional derivative to construct a nonlinear fractional sliding surface.

[0106] The nonlinear fractional-order sliding surface constructed in this invention as follows:

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] , ;

[0114] ;

[0115] in, The gain indicates the parameter tuning of the nonlinear term; The gain indicates the parameter tuning of the fractional derivative term; Represents a nonlinear function; express Nonlinear powers of functions express The width of the linear interval of the function satisfies: , ; To represent a fractional derivative, It represents the order of the fractional derivative.

[0116] Nonlinear fractional sliding surface The first-order differential is as follows:

[0117] ;

[0118] ;

[0119] ;

[0120] in, yes Regarding time The first-order differential, They are Regarding time The first differential.

[0121] In designing the nonlinear fractional sliding surface, this invention comprehensively considers the characteristics of snake robots, such as high-dimensional coupling, time-varying parameters, and strong nonlinearity. By introducing fractional derivative terms into the sliding surface, the system gains memory for historical error information, thereby improving the smoothness of control and its ability to resist disturbances. Simultaneously, a nonlinear function is used to adjust the convergence speed of errors at different stages, accelerating the response when errors are large and suppressing chattering when errors are small, forming an "error adaptive amplification and suppression" mechanism. This design not only enhances the robustness and compliance of the sliding surface but also effectively overcomes the frictional unevenness and coupling disturbance problems of snake robots in scenarios such as complex terrain inspection and movement in confined spaces, enabling them to maintain stable motion while possessing better path tracking accuracy and energy utilization efficiency.

[0122] Step S103: Based on the nonlinear fractional sliding surface, design the superspiral approach rate.

[0123] The superhelical convergence rate designed in this embodiment of the invention is as follows:

[0124] ;

[0125] ;

[0126] in, and The parameter tuning gain represents the superhelical approach rate. , ; , .

[0127] This invention addresses the complex operating conditions of snake robots in environments characterized by multi-joint coupling, frictional nonlinearity, and frequent external disturbances. It introduces a combination of nonlinear fractional sliding surface and superspiral control. While traditional linear reaching laws can achieve error convergence, they are prone to system chattering and have limited convergence speed. Therefore, the superspiral reaching law designed in this invention employs a composite form of nonlinear power terms and integral terms. This accelerates the system response speed when the error is large, and smoothly approximates the sliding surface when the error is small, significantly reducing chattering. This method not only ensures rapid convergence within a finite time but also enhances the robustness of the control system to parameter uncertainties and external disturbances. Considering the complex motion characteristics of snake robots in applications such as power grid transmission line inspection and pipeline inspection, the proposed superspiral reaching law enables more coordinated joint movements and more accurate path tracking, thereby achieving compliant, stable, and efficient control.

[0128] Step S104: Based on the tracking error dynamics model, the nonlinear fractional sliding surface, and the superspiral approach rate, construct a superspiral nonlinear fractional sliding controller for the snake robot to control the snake robot.

[0129] Controlling the snake-like robot in step S104 involves acquiring the driving torque of each drivable joint angle of the snake-like robot. This enables the driving of each joint of the snake robot.

[0130] In this embodiment of the invention, the superspiral nonlinear fractional sliding mode controller is as follows:

[0131] when :

[0132] ;

[0133] when :

[0134] ;

[0135] in, This represents the observation compensation for the uncertainty terms and unknown disturbances in the tracking error dynamics model.

[0136] Furthermore, this invention also uses a radial basis function neural network observer to observe and compensate for the uncertainties and unknown disturbances in the tracking error dynamics model, i.e., to obtain... .

[0137] In this embodiment of the invention, the radial basis function neural network observer is defined as follows:

[0138] ;

[0139] ;

[0140] ;

[0141] in, This represents an approximation of all unknown and uncertain terms in the tracking error dynamics model. , This represents the hidden layer activation function of the neural network observer. This represents the optimal weights of the neural network observer from the hidden layer to the output layer. Indicates being between and The approximate error, express The estimated value.

[0142] Wherein, the hidden layer activation function of the present invention Using cubic B-spline basis functions, Defined as:

[0143] ;

[0144] ;

[0145] ;

[0146] when and hour,

[0147] ;

[0148] when and hour,

[0149] ;

[0150] when and hour,

[0151] ;

[0152] in, Represents the Euclidean norm; This represents the distance between the input vector and the center vector; Represents the input vector; Represents the center vector; This represents the width of the cubic B-spline basis function.

[0153] The control method of this invention first constructs an error dynamics model to track the deviation from the predetermined trajectory during actual motion. To improve control accuracy and response speed, a nonlinear fractional sliding surface control strategy is introduced. This strategy cleverly utilizes error saturation suppression and amplification mechanisms to effectively reduce tracking errors and significantly enhance system stability. Simultaneously, the design of a superspiral approach rate ensures that the robot can converge quickly and stably to the sliding surface. Furthermore, this invention employs a radial basis function neural network observer to address internal uncertainties and external environmental disturbances. Leveraging the powerful approximation capability of the radial basis function neural network observer, it accurately estimates the system's uncertainties and disturbances in real time, thereby improving system robustness and achieving high-precision, fast tracking control.

[0154] To verify and illustrate the technical effects of the method of the present invention, one embodiment of the present invention selects to compare the control method of the present invention with the traditional PID control method, and compares the test results with scientific demonstration methods to verify the real effect of the method.

[0155] This embodiment uses a three-degree-of-freedom (DOF) snake robot. Theoretically, a three-DOF snake robot has three drivable joint angles (i.e., three degrees of freedom). This experiment selects only two of these joint angles (usually the first two segments) as the target trajectory tracking object. The physical parameters of the three-DOF snake robot in this embodiment are: N=4 joint links, and the mass of a single link of the snake robot... Furthermore, the mass of each link is uniformly distributed, and the joint length... ground friction , , .

[0156] The vector and inertia matrices used in the three-degree-of-freedom snake robot system are:

[0157] ; ;

[0158] ;

[0159] ; ;

[0160] ; ;

[0161] ; ; ;

[0162] .

[0163] The input and output vectors of the snake robot are respectively , , , .

[0164] The initial conditions for the snake robot are: , rad, rad.

[0165] The target trajectory (target trajectory 1) of the snake robot with a driveable joint angle of 1 is as follows: The target trajectory (target trajectory 2) for joint angle 2 is as follows: .

[0166] The parameters of the superspiral nonlinear fractional sliding mode controller in this embodiment of the invention are as follows:

[0167] ; ;

[0168] ; ; ; ;

[0169] ; ;

[0170] ; .

[0171] The controller used in comparison is a traditional PID controller: .

[0172] Compare the controller parameters as follows ; ; .

[0173] In the MATLAB 2019a SIMULINK environment, a controlled object model of the snake robot was built based on existing physical parameters and mathematical models of snake robots. The control method of this invention and the traditional PID control method were used to perform tracking control on its driveable joint angles 1 and 2 under different target trajectories, and test results were obtained. Both methods were simulated and tested using automated testing equipment and MATLAB software programming. Simulation data was obtained based on the experimental results. Four sets of data were tested for each method, with each set sampled for 15 seconds. The input target trajectory and output tracking trajectory for each set of data were calculated and compared to verify the feasibility of the proposed algorithm.

[0174] Figure 3 The control method (SMC) of the present invention and the conventional PID control method (PID) are shown to compare the effects of controlling the driveable joint angle 1 to track the target trajectory 1. Figure 4 The invention demonstrates a comparison of the control method (SMC) of the present invention and the conventional PID control method (PID) in terms of the effectiveness of controlling the driveable joint angle 2 to track the target trajectory 2.

[0175] from Figure 3 and Figure 4It is evident that both the control method of this invention and the traditional PID control method demonstrate tracking capabilities for both target trajectory 1 and target trajectory 2. However, upon closer analysis of the control performance, it becomes clear that the control method of this invention significantly outperforms the traditional PID control method in several key indicators. First, from the perspective of convergence speed, the control method of this invention, with its rapid response and precise adjustment capabilities, enables the system to converge to the target trajectory more quickly. This is thanks to the superspiral approach rate designed in this invention, which ensures that the system can reach the sliding surface within a finite time. Combined with the real-time compensation of disturbances and uncertainties by the radial basis function neural network observer, rapid convergence is achieved. Second, regarding steady-state error, the control method of this invention also exhibits a lower error level. This is because the nonlinear fractional sliding surface proposed in this invention allows the system to track the target trajectory more accurately, avoiding the problem of large steady-state errors that may occur in traditional PID control methods under certain circumstances. Finally, from the perspective of stability, the control method of this invention provides a more stable and reliable control effect through the mechanism of error saturation suppression and converse amplification. This means that when facing various complex environments and disturbances, the control method of this invention can maintain the stability and robustness of the system, ensuring that the robot can continuously and accurately track the target trajectory.

[0176] An embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the control method for a snake robot according to an embodiment of the present invention.

[0177] An embodiment of the present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer processor, is used to cause the computer to perform the control method of the snake robot of the embodiment of the present invention.

[0178] An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the control method for a snake-like robot according to an embodiment of the present invention.

[0179] refer to Figure 5The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0180] like Figure 5 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0181] Multiple components in the electronic device are connected to I / O interface 105, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information into the electronic device. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, disks and optical discs. Communication unit 109 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0182] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 102 and / or communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0183] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0184] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0185] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0186] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0187] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0188] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0189] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A control method of a snake robot, characterized by, The method comprises the following steps: Based on the dynamic model of the snake robot and the tracking error, a tracking error dynamic model of the snake robot is constructed; Nonlinear functions and fractional derivatives are introduced to construct a nonlinear fractional sliding surface; Based on the nonlinear fractional sliding surface, a super-helix reaching rate is designed; According to the tracking error dynamic model, the nonlinear fractional sliding surface, and the super-helix reaching rate, a super-helix nonlinear fractional sliding mode controller of the snake robot is constructed to control the snake robot; The super-helix nonlinear fractional sliding mode controller includes observation compensation for uncertain terms and unknown disturbances of the tracking error dynamic model.

2. The control method of the snake robot according to claim 1, characterized by, Based on the dynamic model of the snake robot, a tracking error is introduced to construct a tracking error dynamic model of the snake robot, which comprises: The original dynamic model of the snake robot is as follows: ； wherein, is a second-order differential, , denotes a joint angle vector of the snake robot; denotes a joint angular velocity vector; denotes the joint angle of the snake robot, denotes the number of joints of the snake robot; denotes the center of mass coordinates of the snake robot; denotes the inertia matrix of the system; denotes the Coriolis and centripetal force matrix; denotes the gravity and friction force matrix; denotes the friction force matrix; denotes the driving torque of each joint; The tracking error of the snake robot is as follows: ； wherein, represents a tracking error of the snake robot; represents a target trajectory of the snake robot, ; represents an actual trajectory of the snake robot; The second-order derivative of the tracking error of the snake robot is as follows: ； wherein is the second derivative of the second derivative of the second derivative of Based on the original dynamic model of the snake robot, a dynamic model of the snake robot is constructed; The dynamic model of the snake robot is as follows: ； wherein, represents a non-dimensional parameter adjustment gain matrix, ; represents a comprehensive influence term of the uncertain term and the unknown disturbance, ; represents a control input vector of the snake robot, corresponding to the driving torque of each joint; The dynamic model of the snake robot is introduced into the second-order derivative of the tracking error to construct a tracking error dynamic model of the snake robot; The tracking error dynamic model of the snake robot is as follows: 。 3. The control method of the snake robot according to claim 2, wherein Nonlinear functions and fractional derivatives are introduced to construct a nonlinear fractional sliding surface, which comprises: ； ； ； ； ； ； ； ； wherein, represents a nonlinear fractional order sliding surface; represents a tuning gain of a nonlinear term; represents a tuning gain of a fractional order derivative term; represents a nonlinear function; represents a nonlinear power of a function, represents a linear interval width of a function, satisfying: , ; represents a fractional order derivative, represents an order of a fractional order derivative; The nonlinear fractional order sliding surface The first order differential is as follows: ； ； ； wherein is the first derivative with respect to time , are the first derivative with respect to time .

4. The control method of the snake robot according to claim 3, characterized by, The super-helix reaching rate is designed as follows: ； ； wherein and represents a tuning gain of the supercoiling approach rate, , ; , .

5. The control method of the snake robot according to claim 4, characterized in that, The super-helix nonlinear fractional sliding mode controller is as follows: When Time: ； When time: ； wherein, represents an observation compensation for the uncertainty term and unknown disturbance of the tracking error dynamics model.

6. The control method of the snake robot according to claim 1, wherein After constructing the tracking error dynamic model of the snake robot based on the dynamic model of the snake robot and the tracking error, the method further comprises observing and compensating for the uncertain terms and unknown disturbances of the tracking error dynamic model through a radial basis neural network observer.

7. The control method of the snake robot according to claim 6, wherein The radial basis neural network observer is defined as follows: ； ； ； wherein, denotes an approximation of the unknown disturbance and the uncertainty term in the tracking error dynamics model; , denotes the hidden layer activation function of the neural network observer, denotes the optimal weights from the hidden layer to the output layer of the neural network observer, denotes the approximation error between and denotes the estimate of ;​ wherein the hidden layer activation function is a cubic B-spline basis function, is defined as: ； ； ； When and then, ； When and then, ； When and then, ； wherein, denotes the Euclidean norm; denotes the distance between the input vector and the center vector; denotes the input vector; denotes the center vector; denotes the width of the cubic B-spline basis function.

8. An electronic device comprising: A processor and a memory storing programs, characterized in that the programs include instructions that, when executed by the processor, cause the processor to execute the control method of the snake robot according to any one of claims 1 to 7.

9. A non-transitory machine-readable medium having stored thereon computer instructions, wherein: The computer instructions are used to cause the computer to execute the control method of the snake robot according to any one of claims 1 to 7.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the control method of the snake robot according to any one of claims 1 to 7.

Images