A fuzzy control method and device for osteotomy of a joint replacement surgery robot

Abstract

The present application provides a fuzzy control method and device for osteotomy of a joint replacement surgical robot, the method comprising: obtaining the desired position and current position of the mechanical arm of the joint replacement surgical robot; constructing a sliding mode hyperplane, and determining the controller equation of the mechanical arm, the controller equation including the control gain; based on the desired position and current position of the mechanical arm, fuzzifying and defuzzifying the control gain; based on the value of the defuzzified control gain, calculating the output value of the controller; and controlling the mechanical arm. In the present application, the control gain of the mathematical model of the controller is fuzzified and defuzzified, thereby reducing the uncertainty and ambiguity of the input data, increasing the robustness of the controller, and achieving precise control of the controller.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a fuzzy control method and device for osteotomy of a joint replacement surgery robot. Background Art

[0002] The joint replacement surgery robot system is a comprehensive system that integrates multiple modern high-tech means. It has a wide range of uses and is widely used in clinical surgery. Through the joint replacement surgery robot system, hip replacement surgery, knee replacement surgery and other bone and joint surgeries can be completed to treat pain and dysfunction caused by severe joint injuries or diseases, and significantly reduce hip pain, thereby improving the quality of life of patients.

[0003] The core of the joint replacement surgical robot system is the robotic arm, which performs surgical actions to complete joint surgery. However, in the control of the robotic arm, due to the interference of multiple unknown interference items, it is difficult to establish an accurate controller mathematical model to achieve precise control. Summary of the invention

[0004] The problem solved by this application is that it is difficult to establish an accurate controller mathematical model for accurate control of the current multi-joint robotic arm.

[0005] In order to solve the above problems, the first aspect of the present application provides a fuzzy control method for osteotomy of a joint replacement surgery robot, comprising:

[0006] Obtaining the desired position and current position of the robotic arm of the joint replacement surgery robot;

[0007] constructing a sliding mode hyperplane and determining a controller equation of the manipulator, wherein the controller equation includes a control gain;

[0008] Based on the desired position and the current position of the robot arm, fuzzifying and defuzzifying the control gain;

[0009] Based on the defuzzified value of the control gain, the output value of the controller is calculated; and the robotic arm is controlled.

[0010] The second aspect of the present application provides a fuzzy control device for osteotomy of a joint replacement surgery robot, comprising:

[0011] A data acquisition module, which is used to obtain the desired position and current position of the mechanical arm of the joint replacement surgery robot;

[0012] A controller construction module, which is used to construct a sliding mode hyperplane and determine a controller equation of the manipulator, wherein the controller equation includes a control gain;

[0013] A fuzzy processing module, which is used to perform fuzzy processing and defuzzification on the control gain based on the expected position and current position of the robot arm;

[0014] A robot arm control module is used to calculate the output value of the controller based on the value of the control gain after defuzzification; and control the robot arm.

[0015] A third aspect of the present application provides an electronic device, comprising: a memory and a processor;

[0016] The memory is used to store programs;

[0017] The processor, coupled to the memory, is configured to execute the program to:

[0018] Obtaining the desired position and current position of the robotic arm of the joint replacement surgery robot;

[0019] constructing a sliding mode hyperplane and determining a controller equation of the manipulator, wherein the controller equation includes a control gain;

[0020] Based on the desired position and the current position of the robot arm, fuzzifying and defuzzifying the control gain;

[0021] Based on the defuzzified value of the control gain, the output value of the controller is calculated; and the robotic arm is controlled.

[0022] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the above-mentioned fuzzy control method for osteotomy of a joint replacement surgical robot.

[0023] In the present application, the control gain of the mathematical model of the controller is fuzzified and defuzzified, thereby reducing the uncertainty and fuzziness of the input data, increasing the robustness of the controller, and achieving precise control of the controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0025] Figure 2 A flowchart constructed for a controller of a fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0026] Figure 3 It is a flowchart of fuzzy processing of the fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0027] Figure 4A schematic diagram of a membership function of a fuzzy input of a fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0028] Figure 5 It is a schematic diagram of the membership function of the fuzzy output of the fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0029] Figure 6 A comparison diagram of fuzzy rules of a fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0030] Figure 7 It is a structural block diagram of a fuzzy control device for osteotomy of a joint replacement surgery robot according to an embodiment of the present application;

[0031] Figure 8 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below in conjunction with the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0033] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by technicians in the field to which this application belongs.

[0034] The joint replacement surgery robot system is a comprehensive system that integrates multiple modern high-tech means. It has a wide range of uses and is widely used in clinical surgery. Through the joint replacement surgery robot system, hip replacement surgery, knee replacement surgery and other bone and joint surgeries can be completed to treat pain and dysfunction caused by severe joint injuries or diseases, and significantly reduce hip pain, thereby improving the quality of life of patients.

[0035] The core of the joint replacement surgical robot system is the robotic arm, which performs surgical actions to complete joint surgery. However, in the control of the robotic arm, due to the interference of multiple unknown interference items, it is difficult to establish an accurate controller mathematical model to achieve precise control.

[0036] In response to the above problems, the present application provides a new fuzzy control method for osteotomy of a joint replacement surgical robot, which can fuzzify and defuzzify the mathematical model of the controller, thereby solving the problem that it is difficult to establish an accurate controller mathematical model for precise control of current multi-joint robotic arms.

[0037] The present application embodiment provides a fuzzy control method for osteotomy of a joint replacement surgical robot. The specific scheme of the method is as follows: Figure 1 As shown, the method can be performed by a fuzzy control device for osteotomy of a joint replacement surgery robot, and the fuzzy control device for osteotomy of a joint replacement surgery robot can be integrated into electronic devices such as computers, servers, computers, server clusters, and data centers. Figure 1 As shown, it is a flow chart of a fuzzy control method for osteotomy of a joint replacement surgery robot according to an embodiment of the present application; wherein the fuzzy control method for osteotomy of a joint replacement surgery robot comprises:

[0038] S101, obtaining a desired position and a current position of a robotic arm of a joint replacement surgery robot;

[0039] In the present application, the expected position and current position of the robot arm include not only the expected position and current position of the coordinates of the end of the robot arm, but also the expected position and current position of each joint angle of the robot arm.

[0040] It should be noted here that the fuzzy control method for osteotomy of a joint replacement surgical robot of the present application can not only be used to control osteotomy, but also can be used to control toy cutting, workpiece processing, etc. in factories; the expected position and current position of the robotic arm in the present application cannot be considered to necessarily require interaction with the actual surgical process of the current patient to be obtained, and can also be used in other scenarios such as simulated osteotomy experiments in laboratories to obtain the expected position and current position of osteotomy.

[0041] In this application, osteotomy (which can also be considered as simulated osteotomy) is used as an application scenario to describe the control process of the robotic arm.

[0042] S102, constructing a sliding mode hyperplane and determining a controller equation of the robot arm, wherein the controller equation includes a control gain;

[0043] S103, based on the desired position and current position of the robot arm, fuzzifying and defuzzifying the control gain;

[0044] S104, calculating the output value of the controller based on the defuzzified value of the control gain; and controlling the robotic arm.

[0045] In the present application, controlling the robot arm means using the output value of the controller as a control instruction and controlling the action unit of the robot arm to execute the control instruction.

[0046] In the present application, the control gain of the mathematical model of the controller is fuzzified and defuzzified, thereby reducing the uncertainty and fuzziness of the input data, increasing the robustness of the controller, and achieving precise control of the controller.

[0047] In one embodiment, in combination Figure 2 As shown, the S102 constructs a sliding mode hyperplane and determines the controller equation of the manipulator, wherein the controller equation contains a control gain, including:

[0048] S201, construct the dynamic equation of the robot arm including the unknown cutting force disturbance;

[0049] In this application, the dynamic equation of the robot arm can be established based on the dynamic link characteristics of the robot arm. Among them, the link characteristics of the robot arm are that the robot arm is formed by connecting several rods (the place where two rods are connected is a joint), and these rods in the robot arm are called links. When the rotation angle of the first link is determined, the specific position of the first link can be calculated according to the spatial coordinates of the base, and then the position of the end robot arm can be obtained by calculating backwards one level at a time.

[0050] In this application, the cutting force is the force of interaction between the end effector of the robot and the material during the cutting process. Since the robot cutting process is relatively complicated, the cutting force is difficult to measure accurately.

[0051] In this application, the cutting force is set as an unknown disturbance, so as to facilitate the removal of this part of the disturbance and achieve accurate setting of the control equation of the robot arm.

[0052] It should be noted that the unknown disturbance in this application is the sum of unknown bounded disturbances including cutting force, which represents the overall disturbance of the unknown bounded disturbance of the model, so that the unknown disturbance can be solved / eliminated without analyzing and modeling the specific situation.

[0053] In one embodiment, the dynamic equation of the robotic arm is constructed as:

[0054]

[0055] Where: τ is the joint torque vector, H(q) is the inertia matrix of the robot, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, F c is the unknown cutting force, which is treated as a disturbance term, q is the current position, is the derivative of q, is the current speed, for The derivative of is the current acceleration.

[0056] S201, setting a sliding mode hyperplane;

[0057] In this application, the sliding mode hyperplane reflects the error between the system state and the desired state, and is set as:

[0058]

[0059] Where: q d and are the expected values ​​of joint position and velocity, respectively, λ is the design parameter and control gain. q is the current position, is the derivative of q, is the current joint velocity, and s is the hyperplane.

[0060] S201, calculating the derivative of the sliding mode hyperplane;

[0061] Among them, the expression of the derivative of the sliding mode hyperplane is:

[0062]

[0063] Where s is a hyperplane, is the derivative of s, λ is the design parameter and control gain, are the expected values ​​of the joint velocities, is the expected value of the joint acceleration, is the current joint velocity, is the current joint acceleration.

[0064] S201, constructing a controller equation of the robotic arm according to the derivative of the sliding mode hyperplane and the robotic arm dynamics equation.

[0065] In one embodiment, the controller equation is:

[0066]

[0067] Among them, K and λ are control gains, H(q) is the inertia matrix of the robot arm, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, s is the sliding mode hyperplane, q is the current position, q d is the expected position, is the derivative of q, for q d The derivative of , τ is the manipulator joint torque vector.

[0068] In the present application, by setting the sum of unknown disturbances and eliminating the unknown disturbances by sliding the modal hyperplane, the unknown disturbances can be solved / eliminated without analyzing and modeling the specific situation, thereby achieving accurate setting of the control equations of the robotic arm.

[0069] In this application, fuzzy logic is used to adjust the value of the gain in the controller so that it can better adapt to the dynamic changes of the system and the uncertainty of the external environment.

[0070] In one embodiment, in combination Figure 3 As shown, the S103, based on the desired position and current position of the robot arm, fuzzy processing and defuzzification of the control gain includes:

[0071] S301, fuzzifying the control gain, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs;

[0072] In the present application, taking the sliding mode hyperplane and its derivative as the fuzzy input means taking the sliding mode hyperplane and the derivative of the sliding mode hyperplane as two fuzzy inputs.

[0073] In the present application, there are two control gains, so the fuzzy output is the output of the two control gains.

[0074] In this application, the sliding mode hyperplane and the derivative of the sliding mode hyperplane are used as two fuzzy inputs. The sliding mode hyperplane is used to reflect the error between the system state and the expected state, and the fuzziness of the entire system is fed back to the greatest extent, thereby increasing the accuracy of fuzzy control.

[0075] In one implementation, the fuzzy processing formula of the control gain is:

[0076] K=K0+ΔK

[0077] λ=λ0+Δλ

[0078] Among them, K and λ are control gains, K0 and λ0 are initial values ​​of control gains, and ΔK and Δλ are variables of control gains.

[0079] In the present application, the control gain is a fuzzy output, and the variable of the control gain is specifically set to be the fuzzy output, thereby increasing the correspondence and robustness of the fuzzy input and the fuzzy output.

[0080] S302, determining the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative;

[0081] In one embodiment, the domain of the sliding mode hyperplane and its derivative is [-5, 5].

[0082] Furthermore, the domain of the control gain, the sliding mode hyperplane and its derivative is set as:

[0083]

[0084] In the present application, the domains of the control gain, the sliding mode hyperplane and their derivatives are all set to [-5, 5], thereby increasing the correspondence between the fuzzy input and the fuzzy output.

[0085] In the present application, the fuzzy sets of the control gain, the sliding mode hyperplane and their derivatives are set to {NB, NM, NS, Z, PS, PM, PB}, which respectively represent negative large, negative medium, negative small, zero, positive small, positive medium and positive large; thereby increasing the homology and similarity between the fuzzy input and the fuzzy output and improving the accuracy of the fuzzy conversion.

[0086] S303, calculating the sliding mode hyperplane and its derivative based on the expected position and current position of the robotic arm;

[0087] In the present application, when the desired position and current position of the robot arm are known, the current sliding mode hyperplane and its derivatives can be determined based on the sliding mode hyperplane and its derivatives.

[0088] S304, converting the sliding mode hyperplane and its derivative into input fuzzy values;

[0089] In one embodiment, the fuzzy set of the sliding mode hyperplane and its derivatives are constructed using a triangular membership function, and the boundaries are constructed using a Gaussian membership function.

[0090] In the present application, a triangular membership function is constructed to achieve accurate conversion of the sliding mode hyperplane and its derivatives with the input fuzzy value.

[0091] like Figure 4 As shown, it is a schematic diagram of the membership function of the fuzzy input; based on the schematic diagram, when the sliding mode hyperplane is known, the input fuzzy value can be found through the diagram. Similarly, based on the schematic diagram, when the derivative of the sliding mode hyperplane is known, the input fuzzy value can be found through the diagram.

[0092] It should be noted here that the sliding mode hyperplane and its derivative are independent parameters. In this application, the fuzzy mapping relationship between the two parameters is set to a similar relationship, which can greatly reduce the workload of setting the mapping relationship by utilizing the homology between the two data.

[0093] Preferably, the domain and value threshold of the mapping relationship between the sliding mode hyperplane and its derivative are the same, but the specific mapping relationship is different. For example, Figure 4 Schematic diagram of the membership function of the sliding mode hyperplane. The schematic diagram of the membership function of the derivative of the sliding mode hyperplane has the same coordinate axes as the schematic diagram, but the specific images are different.

[0094] S305, determining an output fuzzy value corresponding to the input fuzzy value according to a preset fuzzy rule;

[0095] In this application, the input fuzzy value is one of NB, NM, NS, Z, PS, PM, and PB (each control gain has an input fuzzy value), and the output fuzzy value is also one of NB, NM, NS, Z, PS, PM, and PB (the sliding mode hyperplane and the derivative of the sliding mode hyperplane correspond to an output fuzzy value respectively).

[0096] In this application, if Figure 6 As shown, it is a comparison chart of fuzzy rules. Based on the comparison chart, the output fuzzy value can be determined according to the input fuzzy value. For example, the input fuzzy value of the sliding mode hyperplane is NB, and the input fuzzy value of its derivative is PS. Then, NB is found in the value of the sliding mode hyperplane of the comparison chart, and the horizontal column corresponding to the value is selected (that is, the first row of the output fuzzy value). PS is found in the value of the derivative of the sliding mode hyperplane of the comparison chart, and the vertical column corresponding to the value is selected (that is, the fifth column of the output fuzzy value). The value at the intersection of the horizontal column and the vertical column is NM, that is, the output fuzzy value is NM.

[0097] In the present application, the output fuzzy values ​​in the comparison diagram are summarized as the same value, thereby increasing the homology between the output control gains and further increasing the accuracy of the control.

[0098] S306, defuzzifying the output fuzzy value to obtain a value of the control gain.

[0099] In one embodiment, the fuzzy set of control gains is constructed by using a Gaussian membership function.

[0100] In the present application, a Gaussian membership function is constructed to achieve accurate conversion between control gain and output fuzzy value.

[0101] like Figure 5 As shown, it is a schematic diagram of the membership function of the fuzzy output; based on this schematic diagram, when the output fuzzy value of the control gain is known, the value of the control gain can be found through this diagram.

[0102] It should be noted here that the two control gains are independent parameters. In this application, the fuzzy mapping relationship between the two parameters is set to a similar relationship, and the workload of setting the mapping relationship can be greatly reduced by utilizing the homology between the two data.

[0103] Preferably, the domain and value threshold of the mapping relationship between the two control gains are the same, but the specific mapping relationships are different. For example, Figure 5 Schematic diagram of the membership function of one of the control gains. The schematic diagram of the membership function of the other control gain has the same coordinate axes as the schematic diagram, but the specific images are different.

[0104] In the present application, the fuzzy values ​​are NB, NM, NS, Z, PS, PM, and PB. When mapping, the numerical value in the domain is mapped to one of the above fuzzy values ​​according to the mapping relationship.

[0105] In the present application, the numerical value of the control gain is adjusted by the sliding mode hyperplane and its derivative, so that the corresponding adjustment of the control gain is achieved by using the fuzzy logic principle, so as to better adapt to the dynamic changes of the system and the uncertainty of the external environment.

[0106] An embodiment of the present application provides a fuzzy control device for osteotomy of a joint replacement surgery robot, which is used to execute the fuzzy control method for osteotomy of a joint replacement surgery robot described in the above content of the present application. The fuzzy control device for osteotomy of a joint replacement surgery robot is described in detail below.

[0107] like Figure 7 As shown, the fuzzy control device for osteotomy of a joint replacement surgery robot comprises:

[0108] A data acquisition module 101 is used to acquire the desired position and current position of the robot arm of the joint replacement surgery robot;

[0109] A controller construction module 102, which is used to construct a sliding mode hyperplane and determine a controller equation of the manipulator, wherein the controller equation includes a control gain;

[0110] A fuzzy processing module 103, which is used to perform fuzzy processing and defuzzification on the control gain based on the expected position and current position of the robot arm;

[0111] The robot control module 104 is used to calculate the output value of the controller based on the defuzzified value of the control gain and control the robot.

[0112] In one embodiment, the controller building module 102 is further configured to:

[0113] Constructing a robot arm dynamics equation containing unknown cutting force disturbance; setting a sliding mode hyperplane; calculating the derivative of the sliding mode hyperplane; constructing a robot arm controller equation based on the derivative of the sliding mode hyperplane and the robot arm dynamics equation.

[0114] In one implementation, the fuzzy processing module 103 is further configured to:

[0115] The control gain is fuzzified, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs; the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative are determined; the sliding mode hyperplane and its derivative are calculated based on the expected position and current position of the robot arm; the sliding mode hyperplane and its derivative are converted into input fuzzy values; the output fuzzy value corresponding to the input fuzzy value is determined according to a preset fuzzy rule; the output fuzzy value is defuzzified to obtain the numerical value of the control gain.

[0116] In one embodiment, the controller equation is:

[0117]

[0118] Among them, K and λ are control gains, H(q) is the inertia matrix of the robot arm, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, s is the sliding mode hyperplane, q is the current position, q d is the expected position, is the derivative of q, for q d The derivative of , τ is the manipulator joint torque vector.

[0119] In one implementation, the fuzzy processing formula of the control gain is:

[0120] K=K0+ΔK

[0121] λ=λ0+Δλ

[0122] Among them, K and λ are control gains, K0 and λ0 are initial values ​​of control gains, and ΔK and Δλ are variables of control gains.

[0123] In one embodiment, the domain of the sliding mode hyperplane and its derivative is [-5, 5].

[0124] In one embodiment, the fuzzy set of the sliding mode hyperplane and its derivatives are constructed using a triangular membership function, and the boundaries are constructed using a Gaussian membership function.

[0125] The fuzzy control device for osteotomy of a joint replacement surgery robot provided in the above-mentioned embodiment of the present application has a corresponding relationship with the fuzzy control method for osteotomy of a joint replacement surgery robot provided in the embodiment of the present application. Therefore, the specific content in the device has a corresponding relationship with the fuzzy control method for osteotomy of a joint replacement surgery robot. The specific content can refer to the record in the fuzzy control method for osteotomy of a joint replacement surgery robot, and will not be repeated in this application.

[0126] The fuzzy control device for osteotomy of a joint replacement surgery robot provided in the above-mentioned embodiment of the present application and the fuzzy control method for osteotomy of a joint replacement surgery robot provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0127] The above describes the internal functions and structure of the fuzzy control device for osteotomy of joint replacement surgery robot. Figure 8 As shown, in practice, the joint replacement surgical robot osteotomy fuzzy control device can be implemented as an electronic device, including: a memory 301 and a processor 303.

[0128] The memory 301 may be configured to store programs.

[0129] In addition, the memory 301 may also be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, contact data, phone book data, messages, pictures, videos, etc.

[0130] The memory 301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0131] The processor 303 is coupled to the memory 301 and is configured to execute a program in the memory 301 to:

[0132] Obtaining the desired position and current position of the robotic arm of the joint replacement surgery robot;

[0133] constructing a sliding mode hyperplane and determining a controller equation of the manipulator, wherein the controller equation includes a control gain;

[0134] Based on the desired position and the current position of the robot arm, fuzzifying and defuzzifying the control gain;

[0135] Based on the defuzzified value of the control gain, the output value of the controller is calculated; and the robotic arm is controlled.

[0136] In one implementation, the processor 303 is specifically configured to:

[0137] Constructing a robot arm dynamics equation containing unknown cutting force disturbance; setting a sliding mode hyperplane; calculating the derivative of the sliding mode hyperplane; constructing a robot arm controller equation based on the derivative of the sliding mode hyperplane and the robot arm dynamics equation.

[0138] In one implementation, the processor 303 is specifically configured to:

[0139] The control gain is fuzzified, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs; the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative are determined; the sliding mode hyperplane and its derivative are calculated based on the expected position and current position of the robot arm; the sliding mode hyperplane and its derivative are converted into input fuzzy values; the output fuzzy value corresponding to the input fuzzy value is determined according to a preset fuzzy rule; the output fuzzy value is defuzzified to obtain the numerical value of the control gain.

[0140] In one embodiment, the controller equation is:

[0141]

[0142] Among them, K and λ are control gains, H(q) is the inertia matrix of the robot arm, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, s is the sliding mode hyperplane, q is the current position, q d is the expected position, is the derivative of q, for q d The derivative of , τ is the manipulator joint torque vector.

[0143] In one implementation, the fuzzy processing formula of the control gain is:

[0144] K=K0+ΔK

[0145] λ=λ0+Δλ

[0146] Among them, K and λ are control gains, K0 and λ0 are initial values ​​of control gains, and ΔK and Δλ are variables of control gains.

[0147] In one embodiment, the domain of the sliding mode hyperplane and its derivative is [-5, 5].

[0148] In one embodiment, the fuzzy set of the sliding mode hyperplane and its derivatives are constructed using a triangular membership function, and the boundaries are constructed using a Gaussian membership function.

[0149] In the present application, the processor is also specifically used to execute all the processes and steps of the above-mentioned joint replacement surgical robot osteotomy fuzzy control method. The specific content can be referred to the records in the joint replacement surgical robot osteotomy fuzzy control method, and will not be repeated in this application.

[0150] In this application, Figure 8 Only some components are shown schematically, which does not mean that the electronic device only includes Figure 8 Components shown.

[0151] The electronic device provided in this embodiment is based on the same inventive concept as the fuzzy control method for osteotomy of a joint replacement surgery robot provided in the embodiment of the present application, and has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0153] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0157] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0158] The present application also provides a computer-readable storage medium corresponding to the fuzzy control method for osteotomy of a joint replacement surgery robot provided in the aforementioned embodiment, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the fuzzy control method for osteotomy of a joint replacement surgery robot provided in any of the aforementioned embodiments.

[0159] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0160] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the fuzzy control method for osteotomy of a joint replacement surgery robot provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0161] It should be noted that in the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0162] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0163] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A fuzzy control device for osteotomy of a joint replacement surgery robot, characterized in that: include: A data acquisition module, which is used to obtain the desired position and current position of the mechanical arm of the joint replacement surgery robot; A controller construction module, which is used to construct a sliding mode hyperplane and determine a controller equation of the manipulator, wherein the controller equation includes a control gain; A fuzzy processing module, which is used to perform fuzzy processing and defuzzification on the control gain based on the expected position and current position of the robot arm; A robot arm control module, which is used to calculate the output value of the controller based on the value of the control gain after defuzzification; and control the robot arm; The obfuscation module is also used to: Performing fuzzification processing on the control gain, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs; Determining the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative; Calculating the sliding mode hyperplane and its derivatives based on the desired position and current position of the robotic arm; converting the sliding mode hyperplane and its derivatives into input fuzzy values; Determine the output fuzzy value corresponding to the input fuzzy value according to the preset fuzzy rule; The output fuzzy value is defuzzified to obtain a value of the control gain.

2. The fuzzy control device for osteotomy of a joint replacement surgery robot according to claim 1, characterized in that: The controller building block is also used to: Construct the dynamic equations of the robot arm including the unknown cutting force disturbance; Set the sliding mode hyperplane; Calculating the derivative of the sliding mode hyperplane; According to the derivative of the sliding mode hyperplane and the dynamic equation of the robot arm, a controller equation of the robot arm is constructed.

3. The fuzzy control device for osteotomy of a joint replacement surgery robot according to claim 1, characterized in that: The controller equation is: Among them, K and λ are control gains, H(q) is the inertia matrix of the robot arm, is the Coriolis force and centrifugal force matrix, G(q) is the gravity term, s is the sliding mode hyperplane, q is the current position, q d is the expected position, is the derivative of q, for q d The derivative of τ is the joint torque vector of the robot arm.

4. The fuzzy control device for osteotomy of a joint replacement surgery robot according to claim 1, characterized in that: The fuzzy processing formula of the control gain is: K=K0+ΔK λ=λ0+Δλ Among them, K and λ are control gains, K0 and λ0 are the initial values ​​of control gains, and ΔK and Δλ are variables of control gains.

5. The fuzzy control device for osteotomy of a joint replacement surgery robot according to claim 1, characterized in that: The domain of the sliding mode hyperplane and its derivative is [-5,5].

6. The fuzzy control device for osteotomy of a joint replacement surgery robot according to claim 1, characterized in that: The fuzzy set of the sliding mode hyperplane and its derivatives are constructed by triangular membership functions, and the boundaries are constructed using Gaussian membership functions.

7. An electronic device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor, coupled to the memory, is configured to execute the program to: Obtaining the desired position and current position of the robotic arm of the joint replacement surgery robot; constructing a sliding mode hyperplane and determining a controller equation of the manipulator, wherein the controller equation includes a control gain; Based on the desired position and the current position of the robot arm, fuzzifying and defuzzifying the control gain; Calculating the output value of the controller based on the defuzzified value of the control gain; Controlling the robotic arm; The fuzzifying and defuzzifying the control gain based on the expected position and the current position of the robot arm includes: Performing fuzzification processing on the control gain, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs; Determining the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative; Calculating the sliding mode hyperplane and its derivatives based on the desired position and current position of the robotic arm; converting the sliding mode hyperplane and its derivatives into input fuzzy values; Determine the output fuzzy value corresponding to the input fuzzy value according to the preset fuzzy rule; The output fuzzy value is defuzzified to obtain a value of the control gain.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by the processor to implement the following fuzzy control method for osteotomy of a joint replacement surgery robot: Obtaining the desired position and current position of the robotic arm of the joint replacement surgery robot; constructing a sliding mode hyperplane and determining a controller equation of the manipulator, wherein the controller equation includes a control gain; Based on the desired position and the current position of the robot arm, fuzzifying and defuzzifying the control gain; Calculating the output value of the controller based on the defuzzified value of the control gain; Controlling the robotic arm; The fuzzifying and defuzzifying the control gain based on the expected position and the current position of the robot arm includes: Performing fuzzification processing on the control gain, wherein the control gain is a fuzzy output, and the sliding mode hyperplane and its derivative are fuzzy inputs; Determining the domain and fuzzy set of the control gain, the sliding mode hyperplane and its derivative; Calculating the sliding mode hyperplane and its derivatives based on the desired position and current position of the robotic arm; converting the sliding mode hyperplane and its derivatives into input fuzzy values; Determine the output fuzzy value corresponding to the input fuzzy value according to the preset fuzzy rule; The output fuzzy value is defuzzified to obtain a value of the control gain.

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