Collaborative robot joint model parameter identification method and system and electronic equipment

Abstract

The invention discloses a collaborative robot joint model parameter identification method and system and electronic equipment. The method comprises the steps that components of a collaborative robot joint are equivalent to a double-inertia elastic model, and a kinetic equation is established; performing Laplace transformation on the kinetic equation to obtain a transfer function of the double-inertia elastic model; constructing a control block diagram of the double-inertia elastic model according to the transfer function of the double-inertia elastic model; according to the control block diagram of the double-inertia elastic model, transfer functions between the electromagnetic torque and the motor angular velocity and between the electromagnetic torque and the load angular velocity are obtained; obtaining to-be-identified parameters of the model according to a transfer function between the electromagnetic torque and the load angular velocity; dividing a transfer function between the electromagnetic torque and the angular velocity of the motor into three parts according to frequency domain characteristics of the transfer function between the electromagnetic torque and the angular velocity of the motor; and according to the three parts, parameters of the model are identified. The parameters obtained by the identification method are clear in physical significance and high in accuracy.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a collaborative robot joint model parameter identification method, system and electronic equipment. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing technology, collaborative robots have been widely used in industrial production due to their high flexibility, safety and human-machine collaboration capabilities. As a key component of collaborative robots, the performance of their joints directly affects the overall motion accuracy and dynamic response capability of the robot. Establishing a mathematical model of the resonant robot joint servo system is a prerequisite for its analysis and control. Therefore, accurate and rapid identification of the model parameters of the servo system is very important for controller design and performance optimization. However, the traditional robot joint model parameter identification uses the frequency domain method to identify the amplitude response and phase response curves obtained by the sweep frequency method to obtain the transfer function of the robot joint. The robot joint model contains a flexible part and the system has a high order. It is difficult to directly fit the amplitude response and phase response curves obtained by the sweep frequency method, and the accuracy of parameter identification is not high. In addition, the parameters obtained by the traditional fitting method are polynomial coefficients, which have no obvious physical meaning and are not conducive to controller design and parameter optimization. Summary of the invention

[0003] The main purpose of this application is to overcome the shortcomings and deficiencies of the prior art and to provide a collaborative robot joint model parameter identification method, system and electronic equipment. The transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts according to the characteristics of the transfer function between the electromagnetic torque and the motor angular velocity. Parameter identification is performed based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, the resonant frequency and the anti-resonant frequency. The obtained parameters have clear physical meanings and high accuracy.

[0004] In order to achieve the above objectives, this application adopts the following technical solutions:

[0005] In a first aspect, the present application provides a collaborative robot joint model parameter identification method, comprising the following steps:

[0006] The components of the collaborative robot joint are equivalent to a dual-inertia elastic model;

[0007] Based on the dual-inertia elastic model, a dynamic equation is established;

[0008] Performing Laplace transformation on the dynamic equation to obtain a transfer function of a dual-inertia elastic model;

[0009] Constructing a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model;

[0010] Analyzing the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity;

[0011] According to the transfer function between the electromagnetic torque and the load angular velocity, the parameters to be identified of the collaborative robot joint model are obtained;

[0012] According to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity, the transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts;

[0013] According to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, the parameters of the collaborative robot joint model are identified.

[0014] As a preferred technical solution, the kinetic equation is:

[0015]

[0016] Among them, J M is the inertia of the motor; J L Inertia of load; K s is the stiffness coefficient of the transmission mechanism; N is the reduction ratio of the reducer; T E , T L and T W are the motor input electromagnetic torque, load torque and rotating shaft torsional torque respectively; b M , b L and bs are the damping coefficients of the motor, load and transmission mechanism respectively; θ M ,ω M ,θ L and ω L They are motor angle, motor angular velocity, load angle and load angular velocity respectively; is the first-order derivative of the motor angle; is the second-order derivative of the motor angle; is the first derivative of the load angle; is the second derivative of the load angle.

[0017] As a preferred technical solution, the transfer function of the dual-inertia elastic model is:

[0018]

[0019] Wherein, S represents the Laplace operator.

[0020] As a preferred technical solution, the transfer function between the electromagnetic torque and the motor angular velocity is:

[0021]

[0022] The transfer function between the electromagnetic torque and the load angular velocity is:

[0023]

[0024] As a preferred technical solution, after obtaining the transfer function between the electromagnetic torque and the motor angular velocity and the transfer function between the electromagnetic torque and the load angular velocity, the method further includes:

[0025] Sweep the frequency of the collaborative robot joints to obtain the amplitude response curve and phase response curve of the electromagnetic torque to the motor angular velocity;

[0026] The anti-resonance frequency and the resonant frequency are obtained from the amplitude response curve and the phase response curve.

[0027] As a preferred technical solution, the transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts, including the transfer function between the electromagnetic torque and the motor angular velocity in the low frequency band, the transfer function between the electromagnetic torque and the motor angular velocity in the medium frequency band, and the transfer function between the electromagnetic torque and the motor angular velocity in the high frequency band; the expressions of the three parts are:

[0028]

[0029] Among them, f AR is the anti-resonance frequency, f R is the resonant frequency, offset1 and offset2 are two constants for correcting the defined interval.

[0030] As a preferred technical solution, the parameters of the collaborative robot joint model are identified based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, including:

[0031] According to the transfer function between the electromagnetic torque in the high frequency band and the motor angular velocity, the motor's moment of inertia J is calculated. M , the calculation formula includes:

[0032] Assume that the sweep amplitude at f0 is Mag(f0), where f0>2π(f R +offset2), then we have the equation:

[0033]

[0034] Among them, f0 is the frequency corresponding to the frequency sweep;

[0035] J M The calculation formula is:

[0036]

[0037] According to the anti-resonance frequency and resonant frequency, the load moment of inertia J is calculated. L and stiffness coefficient K s , the calculation formula is:

[0038]

[0039] As a preferred technical solution, the method of identifying the parameters of the collaborative robot joint model according to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity also includes:

[0040] According to the transfer function between the electromagnetic torque in the intermediate frequency band and the motor angular velocity, the transmission mechanism damping coefficient b is calculated. s , the calculation formula is:

[0041]

[0042] Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b s ) takes the minimum value, b s is the identified transmission mechanism damping coefficient;

[0043] According to the transfer function between the electromagnetic torque in the low frequency band and the motor angular velocity, the motor damping coefficient b is calculated. M , the calculation formula is:

[0044]

[0045] Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b M ) takes the minimum value, b M is the identified motor damping coefficient.

[0046] In a second aspect, the present application provides a collaborative robot joint model parameter identification system, which is applied to a collaborative robot joint model parameter identification method, including an equivalent module, an equation establishment module, a transformation module, a block diagram construction module, an analysis module, a module for obtaining parameters to be identified, a division module, and an identification parameter module;

[0047] The equivalent module is used to equate the components of the collaborative robot joint to a dual-inertia elastic model;

[0048] The equation building module is used to build a dynamic equation based on the dual-inertia elastic model;

[0049] The transformation module is used to perform Laplace transformation on the dynamic equation to obtain the transfer function of the dual-inertia elastic model;

[0050] The block diagram building module is used to build a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model;

[0051] The analysis module is used to analyze the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity;

[0052] The module for obtaining parameters to be identified is used to obtain the parameters to be identified of the joint model of the collaborative robot according to the transfer function between the electromagnetic torque and the load angular velocity;

[0053] The division module is used to divide the transfer function between the electromagnetic torque and the motor angular velocity into three parts according to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity;

[0054] The identification parameter module is used to identify the parameters of the collaborative robot joint model according to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity.

[0055] In a third aspect, the present application provides an electronic device, the electronic device comprising:

[0056] at least one processor; and a memory communicatively coupled to the at least one processor;

[0057] Wherein, the memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the method for identifying parameters of a collaborative robot joint model.

[0058] In summary, compared with the prior art, the effective effects brought about by the technical solution provided by this application include at least:

[0059] The present application proposes a method for identifying parameters of a collaborative robot joint model. The present application divides the transfer function between the electromagnetic torque and the motor angular velocity into three parts, namely, the transfer function between the electromagnetic torque and the motor angular velocity in the low frequency band, the medium frequency band and the high frequency band, according to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity. Parameter identification is performed based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, the resonant frequency and the anti-resonant frequency. The obtained parameters have clear physical meanings and high identification accuracy, which can play a good guiding role in controller design and parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 A flowchart of a collaborative robot joint model parameter identification method provided in one embodiment of the present application;

[0062] Figure 2 A structural diagram of a dual-inertia elastic model provided for one embodiment of the present application;

[0063] Figure 3 A control block diagram of a dual-inertia elastic model provided for one embodiment of the present application;

[0064] Figure 4 A Bode diagram of the actual single-axis FRF sweep of a collaborative robot joint model provided in one embodiment of the present application;

[0065] Figure 5 A comparison diagram of a joint model identification Bode diagram obtained by the identification method of the present application provided in one embodiment of the present application and a Bode diagram of an actual FRF frequency sweep of the joint model;

[0066] Figure 6 A block diagram of a collaborative robot joint model parameter identification system provided for one embodiment of the present application. DETAILED DESCRIPTION

[0067] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0068] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0069] Example:

[0070] See also Figure 1In one embodiment of the present application, a collaborative robot joint model parameter identification method is provided, comprising the following steps:

[0071] S1. The components of the collaborative robot joint are equivalent to a dual-inertia elastic model.

[0072] Furthermore, the collaborative robot joint consists of three parts: the motor, the harmonic reducer, and the robotic arm. Since the stiffness of the harmonic reducer is the weakest, the collaborative robot joint can be equivalent to a dual-inertia elastic model, the flexible transmission component in the joint is equivalent to a torsion spring, and the motor and the load are equivalent to two inertias respectively; the dual-inertia elastic model structure diagram can be seen in Figure 2 .

[0073] S2. Based on the dual-inertia elastic model, a dynamic equation is established.

[0074] Furthermore, the kinetic equation is:

[0075]

[0076] Among them, J M is the inertia of the motor; J L Inertia of load; K s is the stiffness coefficient of the transmission mechanism; N is the reduction ratio of the reducer; T E , T L and T W are the motor input electromagnetic torque, load torque and rotating shaft torsional torque respectively; b M , b L and bs are the damping coefficients of the motor, load and transmission mechanism respectively; θ M ,ω M ,θ L and ω L They are motor angle, motor angular velocity, load angle and load angular velocity respectively; is the first-order derivative of the motor angle; is the second-order derivative of the motor angle; is the first derivative of the load angle; is the second derivative of the load angle.

[0077] S3. Performing Laplace transformation on the dynamic equation to obtain a transfer function of a dual-inertia elastic model.

[0078] Furthermore, by Laplace transforming the dynamic equation of the above formula (2.1), the transfer function of the dual-inertia elastic model can be obtained. The transfer function of the dual-inertia elastic model is:

[0079]

[0080] Wherein, S represents the Laplace operator.

[0081] S4. Constructing a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model.

[0082] For details, please refer to the control block diagram of the dual inertia elastic model. Figure 3 .

[0083] S5. Analyze the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity.

[0084] Furthermore, the electromagnetic torque T E and the motor angular velocity ω M The transfer function between them is:

[0085]

[0086] Electromagnetic torque T E and load angular velocity ω L The transfer function between them is:

[0087]

[0088] Furthermore, by analyzing the control block diagram of the dual-inertia elastic model, the motor angular velocity ω can also be obtained. M Load angular velocity ω L The transfer function between them is:

[0089]

[0090] In this embodiment, to simplify the analysis, the damping coefficients b of the motor, load and transmission mechanism are ignored. M , b L and bs, electromagnetic torque T E and the motor angular velocity ω M The transfer function between them can be simplified as:

[0091]

[0092] To simplify the analysis, the damping coefficient b of the motor, load and transmission mechanism is ignored. M , b L and bs, electromagnetic torque T E and load angular velocity ω L The transfer function between them can be simplified as:

[0093]

[0094] Specifically, according to the above formula (2.6), the electromagnetic torque TE and the motor angular velocity ω M The transfer function between them shows that the magnetic torque T E and the motor angular velocity ω M There is a set of conjugate poles and zeros s between the transfer functions 1,2 and a set of conjugate poles s 3,4 , which will produce anti-resonance peaks and resonant peaks in the Bode plot amplitude response (Bode plot refers to the amplitude response diagram and phase response diagram), and the expressions of its conjugate zeros and poles are as follows:

[0095]

[0096] Here, j represents an imaginary unit.

[0097] According to the expressions of conjugate zeros and poles in equation (2.8), the anti-resonance frequency ω AR and the resonant frequency ω R The formula is as follows:

[0098]

[0099] And the anti-resonance frequency ω AR and the resonant frequency ω R There are the following relationships:

[0100]

[0101] According to the above formula (2.6), the electromagnetic torque T can be obtained by plotting E and the motor angular velocity ω M The Bode diagram (Bode diagram, also known as Bode diagram, includes amplitude response diagram and phase response diagram) of the electromagnetic torque T E To the motor angular velocity ω M There will be two asymptotes in the Bode diagram, and the transfer function expressions of the two asymptotes are as follows:

[0102]

[0103] S6. Obtain the parameters to be identified of the collaborative robot joint model according to the transfer function between the electromagnetic torque and the load angular velocity.

[0104] Furthermore, after obtaining the transfer function between the electromagnetic torque and the motor angular velocity and the transfer function between the electromagnetic torque and the load angular velocity, it also includes:

[0105] Sweep the frequency of the collaborative robot joints to obtain the amplitude response curve and phase response curve (Bode plot) of the electromagnetic torque to the motor angular velocity. Please refer to Figure 4 ; Figure 4(a) is the amplitude response diagram of the actual FRF frequency sweep of the collaborative robot joint model. Figure 4 (b) is the phase response diagram of the actual single-axis FRF sweep of the collaborative robot joint model; the anti-resonance frequency and the resonant frequency are obtained from the amplitude response curve diagram and the phase response curve diagram.

[0106] Furthermore, frequency sweeping is also called frequency scanning or frequency sweep test, which gradually changes the frequency of the signal within a certain time interval and observes the response of the system to signals of different frequencies. The amplitude-frequency response of the system is obtained through the relationship between the given signal and the system response.

[0107] Figure 4 is the sweep frequency curve of the robot joint under a certain load. The anti-resonance frequency f can be easily obtained according to the sweep frequency curve. AR =13Hz, the resonant frequency is f R =19Hz; where the anti-resonance frequency is the minimum point of the sweep frequency curve, and the resonant frequency is the maximum point of the sweep frequency curve. The derivative can be obtained by differentiating the amplitude response curve, and the minimum and maximum values ​​can be obtained according to the change of the derivative; therefore, the reduction ratio N of the robot joint can be obtained, and the load damping coefficient b L =0.

[0108] Specifically, according to the transfer function between the electromagnetic torque and the load angular velocity in the above formula (2.4), the parameters to be identified of the collaborative robot joint model are obtained, and the parameters to be identified include: the motor moment of inertia J M (Unit: kg·m2), load moment of inertia J L (Unit: kg·m2), torsional stiffness K s (unit: N·m / rad), motor damping coefficient b M (unit: N·m·s / rad) and transmission mechanism damping coefficient b s (Unit: N·m·s / rad).

[0109] S7. Divide the transfer function between the electromagnetic torque and the motor angular velocity into three parts according to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity.

[0110] Further, the transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts, including the transfer function between the electromagnetic torque and the motor angular velocity in the low frequency band, the transfer function between the electromagnetic torque and the motor angular velocity in the medium frequency band, and the transfer function between the electromagnetic torque and the motor angular velocity in the high frequency band. The expressions of the three parts are:

[0111]

[0112] Among them, f ARis the anti-resonance frequency, f R is the resonant frequency, offset1 and offset2 are two constants for correcting the defined interval, which can be modified according to the actual frequency sweep curve; is the transfer function between the electromagnetic torque and the motor angular velocity in the low frequency band; is the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency band; It is the transfer function between the electromagnetic torque and the motor angular velocity in the high frequency band.

[0113] S8. According to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, the parameters of the collaborative robot joint model are identified.

[0114] Furthermore, according to the above formula (3.1), the amplitude of the transfer function in the high frequency band is only related to the rotational inertia of the motor, from which the rotational inertia J of the motor can be calculated. M Therefore, according to the transfer function between the electromagnetic torque in the high frequency band and the motor angular velocity, the motor's moment of inertia J is calculated. M , the calculation formula includes:

[0115] Assume that the sweep amplitude at f0 is Mag(f0), where f0>2π(f R +offset2), then we have the equation:

[0116]

[0117] Among them, f0 is the frequency corresponding to the frequency sweep, in Hz;

[0118] Then J M The calculation formula is:

[0119]

[0120] In this embodiment, in order to further improve J M To improve the accuracy of identification, you can also take multiple sets of f0 sweep amplitudes and take J M The mean of .

[0121] According to equations (2.9) and (2.10), the load moment of inertia J can be calculated L and stiffness coefficient K s , and its calculation formula is:

[0122]

[0123] The load damping factor b mentioned above L =0, then formula (3.1) can be written as:

[0124]

[0125] The transfer function between the electromagnetic torque in the low frequency band and the motor angular velocity in equation (3.5) is used to calculate the transmission mechanism damping coefficient b s For identification, the damping coefficient of general motors is very small, and a rough range can be given. The optimization function is:

[0126]

[0127] Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b M ) takes the minimum value, b M is the identified motor damping coefficient.

[0128] Using the transfer function between the electromagnetic torque in the mid-frequency band and the motor angular velocity in equation (3.5), the transmission mechanism damping coefficient b is s To identify, b s It is related to the maximum value of the resonance peak, and its range can also be set at (0,100). The optimization function is:

[0129]

[0130] Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b s ) takes the minimum value, b s is the identified transmission mechanism damping coefficient.

[0131] Specifically, according to the above formulas (3.3), (3.4), (3.5), (3.6) and (3.7), the motor moment of inertia J of the collaborative robot joint model can be obtained: M 0.0007kg·m2, load moment of inertia J L 8.1125kg·m2, torsional stiffness K s is 54125.4790N·m / rad, motor damping coefficient b M is 0.0110N·m·s / rad, the transmission mechanism damping coefficient b s is 22.1300N·m·s / rad, load damping coefficient b L The speed is 0N·m·s / rad and the reduction ratio is 101.

[0132] Substituting the identified parameters into the above formula (2.6), the model identification curve obtained by the parameter identification method of the present application can be plotted; please refer to Figure 5 , which is a comparison diagram of the model identification curve obtained by the identification method of this application and the actual frequency sweep curve of the model, Figure 5(a) A comparison of the amplitude response of the joint model identification Bode diagram obtained by the identification method of the present application and the Bode diagram of the actual FRF frequency sweep of the joint model, Figure 5 (b) Phase response comparison diagram of the joint model identification Bode diagram obtained by the identification method of the present application and the Bode diagram of the actual FRF sweep of the joint model; It can be seen that the amplitude response obtained by the identification method adopted in the present application can well reflect the resonant frequency and anti-resonant frequency of the system, and the amplitude characteristics in the entire frequency band are basically consistent with the actual system. Before the high frequency band, the phase angle characteristics are basically consistent with the actual system. After the high frequency band, the phase angle of the identification model remains unchanged at -90°. This is due to the existence of the low-pass filter of the speed feedback loop in the actual system, which makes the phase angle of the actual system continue to decay in the high frequency band, but this has little effect on the system modeling and controller optimization design of the robot joint; Therefore, the identification method adopted in the present application has high accuracy in identifying parameters.

[0133] It should be noted that, for the sake of convenience of description, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously.

[0134] Based on the same idea as the collaborative robot joint model parameter identification method in the above-mentioned embodiment, the present application also provides a collaborative robot joint model parameter identification system, which can be used to execute the above-mentioned collaborative robot joint model parameter identification method. For ease of explanation, the structural diagram of an embodiment of a collaborative robot joint model parameter identification system only shows the parts related to the embodiment of the present application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the system, and may include more or fewer components than shown in the diagram, or combine certain components, or arrange the components differently.

[0135] See also Figure 6 In another embodiment of the present application, a collaborative robot joint model parameter identification system is provided, the system comprising an equivalent module 101, an equation building module 102, a transformation module 103, a block diagram building module 104, an analysis module 105, a module for obtaining parameters to be identified 106, a division module 107 and an identification parameter module 108;

[0136] The equivalent module 101 is used to equate the components of the collaborative robot joint to a dual-inertia elastic model;

[0137] The equation building module 102 is used to build a dynamic equation based on the dual inertia elastic model;

[0138] The transformation module 103 is used to perform Laplace transformation on the dynamic equation to obtain the transfer function of the dual-inertia elastic model;

[0139] The block diagram building module 104 is used to build a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model;

[0140] The analysis module 105 is used to analyze the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity;

[0141] The module 106 for obtaining parameters to be identified is used to obtain parameters to be identified of the joint model of the collaborative robot according to the transfer function between the electromagnetic torque and the load angular velocity;

[0142] The division module 107 is used to divide the transfer function between the electromagnetic torque and the motor angular velocity into three parts according to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity;

[0143] The identification parameter module 108 is used to identify the parameters of the collaborative robot joint model according to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity.

[0144] It should be noted that a collaborative robot joint model parameter identification system of the present application corresponds one-to-one to a collaborative robot joint model parameter identification method of the present application. The technical features and beneficial effects described in the embodiment of the above-mentioned collaborative robot joint model parameter identification method are applicable to the embodiment of a collaborative robot joint model parameter identification system. For specific contents, please refer to the description in the embodiment of the method of the present application. It will not be repeated here. This is hereby declared.

[0145] In addition, in the implementation of a collaborative robot joint model parameter identification system in the above embodiment, the logical division of each program module is only an example. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the configuration requirements of the corresponding hardware or the convenience of software implementation. That is, the internal structure of the collaborative robot joint model parameter identification system is divided into different program modules to complete all or part of the functions described above.

[0146] In another embodiment, an electronic device for implementing a collaborative robot joint model parameter identification method is provided, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; when the processor executes the computer program, a collaborative robot joint model parameter identification method of any embodiment of the present application is implemented.

[0147] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more module elements may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the device.

[0148] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0149] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0150] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0151] Accordingly, the present application also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a collaborative robot joint model parameter identification method as described in any one of the above embodiments.

[0152] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0153] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above embodiments are preferred implementation modes of the present application, but the implementation modes of the present application are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present application should be equivalent replacement methods and are included in the protection scope of the present application.

Claims

1. A collaborative robot joint model parameter identification method, characterized in that: The steps include: The components of the collaborative robot joint are equivalent to a dual-inertia elastic model; Based on the dual-inertia elastic model, a dynamic equation is established; Performing Laplace transformation on the dynamic equation to obtain a transfer function of a dual-inertia elastic model; Constructing a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model; Analyzing the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity; According to the transfer function between the electromagnetic torque and the load angular velocity, the parameters to be identified of the collaborative robot joint model are obtained; According to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity, the transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts; According to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, the parameters of the collaborative robot joint model are identified.

2. A collaborative robot joint model parameter identification method according to claim 1, characterized in that: The kinetic equation is: Among them, J M is the inertia of the motor; J L Inertia of load; K s is the stiffness coefficient of the transmission mechanism; N is the reduction ratio of the reducer; T E 、T L and T W are the motor input electromagnetic torque, load torque and rotating shaft torsional torque respectively; b M 、b L and bs are the damping coefficients of the motor, load and transmission mechanism respectively; θ M ,ω M ,θ L and ω L They are motor angle, motor angular velocity, load angle and load angular velocity respectively; is the first-order derivative of the motor angle; is the second-order derivative of the motor angle; is the first derivative of the load angle; is the second derivative of the load angle.

3. A collaborative robot joint model parameter identification method according to claim 1, characterized in that: The transfer function of the dual-inertia elastic model is: Wherein, S represents the Laplace operator.

4. The collaborative robot joint model parameter identification method according to claim 1, characterized in that: The transfer function between the electromagnetic torque and the motor angular velocity is: The transfer function between the electromagnetic torque and the load angular velocity is:

5. The collaborative robot joint model parameter identification method according to claim 1, characterized in that: After obtaining the transfer function between the electromagnetic torque and the motor angular velocity and the transfer function between the electromagnetic torque and the load angular velocity, the method further includes: Sweep the frequency of the collaborative robot joints to obtain the amplitude response curve and phase response curve of the electromagnetic torque to the motor angular velocity; The anti-resonance frequency and the resonant frequency are obtained from the amplitude response curve and the phase response curve.

6. The collaborative robot joint model parameter identification method according to claim 1, characterized in that: The transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts, including the transfer function between the electromagnetic torque and the motor angular velocity in the low frequency band, the transfer function between the electromagnetic torque and the motor angular velocity in the medium frequency band, and the transfer function between the electromagnetic torque and the motor angular velocity in the high frequency band; the expressions of the three parts are: Among them, f AR is the anti-resonance frequency, f R is the resonant frequency, offset1 and offset2 are two constants for correcting the defined interval.

7. A collaborative robot joint model parameter identification method according to claim 6, characterized in that: The parameters of the collaborative robot joint model are identified based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity, including: According to the transfer function between the electromagnetic torque in the high frequency band and the motor angular velocity, the motor's moment of inertia J is calculated. M , the calculation formula includes: Assume that the sweep amplitude at f0 is Mag(f0), where f0>2π(f R +offset2), then we have the equation: Among them, f0 is the frequency corresponding to the frequency sweep; J M The calculation formula is: According to the anti-resonance frequency and resonant frequency, the load moment of inertia J is calculated. L and stiffness coefficient K s , the calculation formula is:

8. A collaborative robot joint model parameter identification method according to claim 6, characterized in that: The method of identifying the parameters of the collaborative robot joint model according to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity also includes: According to the transfer function between the electromagnetic torque in the intermediate frequency band and the motor angular velocity, the transmission mechanism damping coefficient b is calculated. s , the calculation formula is: Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b s ) takes the minimum value, b s is the identified transmission mechanism damping coefficient; According to the transfer function between the electromagnetic torque in the low frequency band and the motor angular velocity, the motor damping coefficient b is calculated. M , the calculation formula is: Among them, Mag(2πω) is the sweep amplitude corresponding to the angular velocity ω. When f(b M ) takes the minimum value, b M is the identified motor damping coefficient.

9. A collaborative robot joint model parameter identification system, characterized in that: A collaborative robot joint model parameter identification method applied to any one of claims 1-8, comprising an equivalent module, an equation building module, a transformation module, a block diagram building module, an analysis module, a module for obtaining parameters to be identified, a division module, and a parameter identification module; The equivalent module is used to equate the components of the collaborative robot joint to a dual-inertia elastic model; The equation building module is used to build a dynamic equation based on the dual-inertia elastic model; The transformation module is used to perform Laplace transformation on the dynamic equation to obtain the transfer function of the dual-inertia elastic model; The block diagram building module is used to build a control block diagram of the dual-inertia elastic model according to the transfer function of the dual-inertia elastic model; The analysis module is used to analyze the control block diagram of the dual-inertia elastic model to obtain a transfer function between the electromagnetic torque and the motor angular velocity, and a transfer function between the electromagnetic torque and the load angular velocity; The module for obtaining parameters to be identified is used to obtain the parameters to be identified of the joint model of the collaborative robot according to the transfer function between the electromagnetic torque and the load angular velocity; The division module is used to divide the transfer function between the electromagnetic torque and the motor angular velocity into three parts according to the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity; The identification parameter module is used to identify the parameters of the collaborative robot joint model according to the three parts of the transfer function between the electromagnetic torque and the motor angular velocity.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute a collaborative robot joint model parameter identification method as described in any one of claims 1-8.

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