A method, system, and electronic device for identifying joint model parameters of a collaborative robot.

Abstract

This application discloses a method, system, and electronic device for identifying joint model parameters of a collaborative robot. The method includes: equating the components of a collaborative robot joint to a dual-inertia elastic model and establishing dynamic equations; performing a Laplace transform on the dynamic equations to obtain the transfer function of the dual-inertia elastic model; constructing a control block diagram of the dual-inertia elastic model based on the transfer function; obtaining the transfer functions between electromagnetic torque and motor angular velocity and load angular velocity based on the control block diagram; obtaining the parameters to be identified based on the transfer function between electromagnetic torque and load angular velocity; dividing the transfer function between electromagnetic torque and motor angular velocity into three parts based on the frequency domain characteristics of the transfer function; and identifying the parameters of the model based on these three parts. The parameters obtained by the method of this application have clear physical meanings and high accuracy.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to a method, system, and electronic device for identifying joint model parameters of a collaborative robot. Background Technology

[0002] With the rapid development of industrial automation and intelligent manufacturing technologies, collaborative robots have been widely used in industrial production due to their high flexibility, safety, and human-robot collaboration capabilities. The joints of collaborative robots, as a key component, directly affect 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, accurately and quickly identifying the model parameters of the servo system is crucial for controller design and performance optimization. However, traditional robot joint model parameter identification uses the frequency domain method, fitting the amplitude and phase response curves obtained by the frequency sweep method to obtain the transfer function of the robot joint. However, robot joint models contain flexible parts and have high system order, making it difficult to directly fit the amplitude and phase response curves obtained by the frequency sweep method, resulting in low accuracy in parameter identification. Furthermore, the parameters obtained by this traditional fitting method are polynomial coefficients with unclear physical meaning, which is detrimental 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 provide a method, system and electronic device for identifying joint model parameters of collaborative robots. By dividing the transfer function between electromagnetic torque and motor angular velocity into three parts according to the characteristics of the transfer function between electromagnetic torque and motor angular velocity, the parameters are identified based on the three parts of the transfer function between electromagnetic torque and motor angular velocity, the resonant frequency and the anti-resonant frequency. The obtained parameters have clear physical meaning and high accuracy.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] In a first aspect, this application provides a method for identifying joint model parameters of a collaborative robot, comprising the following steps:

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

[0007] Based on the aforementioned dual-inertia elastic model, the dynamic equations are established;

[0008] The Laplace transform of the dynamic equations yields the transfer function of the two-inertia elastic model.

[0009] Based on the transfer function of the dual-inertia elastic model, construct the control block diagram of the dual-inertia elastic model;

[0010] The control block diagram of the dual-inertia elastic model is analyzed to obtain the transfer function between electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and load angular velocity.

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

[0012] Based on 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] 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.

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

[0015]

[0016] Among them, J M J represents the inertia of the motor. L Load inertia; K s T is the stiffness coefficient of the transmission mechanism; N is the reduction ratio of the reducer; T E T L and T W These are the motor input electromagnetic torque, load torque, and rotating shaft torsional torque, respectively; b M b L θ and bs are the damping coefficients of the motor, load, and transmission mechanism, respectively; θ M ω M θ L and ω L These are the motor angle, motor angular velocity, load angle, and load angular velocity, respectively. The first derivative of the motor angle; The second derivative of the motor angle; The first derivative of the load angle; It 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] Where 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 electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and load angular velocity, the method further includes:

[0025] Frequency sweeping was performed on the joints of the collaborative robot to obtain the amplitude response curve and phase response curve of electromagnetic torque to motor angular velocity;

[0026] The anti-resonance frequency and resonance frequency are obtained from the amplitude response curve and 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: the transfer function between the electromagnetic torque and the motor angular velocity in the low-frequency range, the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency range, and the transfer function between the electromagnetic torque and the motor angular velocity in the high-frequency range; the expressions for the three parts are as follows:

[0028]

[0029] Among them, f AR f is the anti-resonant frequency. R Here, is the resonant frequency, and offset1 and offset2 are two constants used to correct the defined interval.

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

[0031] Based on the transfer function between electromagnetic torque and motor angular velocity in the high-frequency band, the moment of inertia J of the motor is calculated. M The calculation formula includes:

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

[0033]

[0034] Where f0 is the frequency corresponding to the sweep frequency;

[0035] J M The formula for calculation is:

[0036]

[0037] The load moment of inertia J is calculated based on the anti-resonance frequency and the resonance frequency. L and stiffness coefficient K s The calculation formula is:

[0038]

[0039] As a preferred technical solution, the step of identifying the parameters of the collaborative robot joint model based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity further includes:

[0040] Based on the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency band, the damping coefficient b of the transmission mechanism is calculated. s The calculation formula is:

[0041]

[0042] Where Mag(2πω) is the sweep amplitude corresponding to angular velocity ω, when f(b s When b reaches its minimum value, s The damping coefficient of the transmission mechanism for identification;

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

[0044]

[0045] Where Mag(2πω) is the sweep amplitude corresponding to angular velocity ω, when f(b M When b reaches its minimum value, M The motor damping coefficient is used for identification.

[0046] Secondly, this application provides a collaborative robot joint model parameter identification system, which is applied to the 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 parameter acquisition module, a partitioning module, and a parameter identification module.

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

[0048] The equation-establishing module is used to establish dynamic equations based on the dual-inertia elastic model.

[0049] The transformation module is used to perform a Laplace transform on the dynamic equations to obtain the transfer function of the two-inertia elastic model.

[0050] The block diagram construction module is used to construct the control block diagram of the dual-inertia elastic model based on 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 the transfer function between electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and 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 based on 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 based on the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity.

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

[0055] Thirdly, this application provides an electronic device, the electronic device comprising:

[0056] At least one processor; and a memory communicatively connected to said at least one processor;

[0057] The memory stores computer program instructions that can be executed by the at least one processor, which are then executed by the at least one processor to enable the at least one processor to perform the collaborative robot joint model parameter identification method.

[0058] In summary, compared with the prior art, the effective effects of the technical solution provided in this application include at least the following:

[0059] This application proposes a method for identifying joint model parameters of collaborative robots. Based on the frequency domain characteristics of the transfer function between electromagnetic torque and motor angular velocity, this application divides the transfer function between electromagnetic torque and motor angular velocity into three parts: low-frequency, mid-frequency, and high-frequency bands. Parameter identification is performed based on these three parts, the resonant frequency, and the anti-resonant frequency. The obtained parameters have clear physical meanings and high identification accuracy, providing excellent guidance for controller design and parameter optimization. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating a method for identifying joint model parameters of a collaborative robot, as provided in one embodiment of this application;

[0062] Figure 2 This is a structural diagram of a dual-inertia elastic model provided in one embodiment of this application;

[0063] Figure 3 This is a control block diagram of a dual-inertia elastic model provided in one embodiment of this application;

[0064] Figure 4 Bode plot of actual FRF sweep frequency of a single axis of a collaborative robot joint model provided in one embodiment of this application;

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

[0066] Figure 6 This is a block diagram of a collaborative robot joint model parameter identification system provided in one embodiment of this application. Detailed Implementation

[0067] 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 with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

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

[0069] Example:

[0070] Please see Figure 1One embodiment of this application provides a method for identifying joint model parameters of a collaborative robot, 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: a motor, a harmonic reducer, and a robotic arm. Since the harmonic reducer has the weakest stiffness, the collaborative robot joint can be equivalent to a dual-inertia elastic model. The flexible transmission components in the joint are equivalent to torsion springs, and the motor and load are each equivalent to two inertia. For a structural diagram of the dual-inertia elastic model, please refer to [link / reference needed]. Figure 2 .

[0073] S2. Based on the aforementioned dual-inertia elastic model, establish the dynamic equations.

[0074] Furthermore, the dynamic equation is:

[0075]

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

[0077] S3. Perform a Laplace transform on the dynamic equations to obtain the transfer function of the two-inertia elastic model.

[0078] Furthermore, by performing a Laplace transform on the dynamic equation of equation (2.1) above, the transfer function of the two-inertia elastic model can be obtained, which is:

[0079]

[0080] Where S represents the Laplace operator.

[0081] S4. Based on the transfer function of the dual-inertia elastic model, construct the control block diagram of the dual-inertia elastic model.

[0082] For a detailed control block diagram of the dual-inertia elastic model, please refer to [link / reference]. Figure 3 .

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

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

[0085]

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

[0087]

[0088] Furthermore, by analyzing the control block diagram of the aforementioned 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 coefficient b of the motor, load, and transmission mechanism is ignored. M b L and bs, electromagnetic torque T E With the angular velocity ω of the motor M The transfer function between them can be simplified to:

[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 With load angular velocity ω L The transfer function between them can be simplified to:

[0093]

[0094] Specifically, according to the above equation (2.6), the electromagnetic torque TE With the angular velocity ω of the motor M From the transfer function, we can see that the magnetic torque T E With the angular velocity ω of the motor M The transfer function between them has a set of conjugate poles and zeros s 1,2 and a set of conjugate poles s 3,4 This will produce anti-resonance peaks and resonance peaks in the Bode plot magnitude response (Bode plot refers to the magnitude response plot and phase response plot), and the expressions for its conjugate zeros and poles are as follows:

[0095]

[0096] Where j represents the imaginary unit.

[0097] Based on the expressions for the conjugate zeros and poles in equation (2.8), the anti-resonant frequency ω can be determined. AR and resonant frequency ω R The formula is as follows:

[0098]

[0099] And the anti-resonance frequency ω AR and resonant frequency ω R The following relationship exists:

[0100]

[0101] The electromagnetic torque T can be plotted according to the above equation (2.6). E With the angular velocity ω of the motor M The Bode plot (also known as the Bode diagram, which includes the magnitude response plot and the phase response plot) shows the electromagnetic torque T. E to the motor angular velocity ω M The Bode plot will have two asymptotes, and the expressions for the transfer functions of the two asymptotes are as follows:

[0102]

[0103] S6. Based on the transfer function between the electromagnetic torque and the load angular velocity, obtain the parameters to be identified for the joint model of the collaborative robot.

[0104] Furthermore, after obtaining the transfer functions between electromagnetic torque and motor angular velocity, and between electromagnetic torque and load angular velocity, the following are also included:

[0105] To obtain the amplitude and phase response curves (Bode plots) of the electromagnetic torque to the motor angular velocity by sweeping the frequency of the collaborative robot joints, please refer to [link / reference needed]. Figure 4 ; Figure 4(a) is the amplitude response diagram of the actual FRF sweep frequency of a single axis of the collaborative robot joint model. Figure 4 (b) is the phase response diagram of the actual FRF sweep frequency of the joint model of the collaborative robot on a single axis; the anti-resonant frequency and the resonant frequency are obtained from the amplitude response curve and the phase response curve.

[0106] Furthermore, frequency sweeping, also known as frequency scanning or frequency sweep test, involves gradually changing the frequency of a signal over a certain time interval and observing the system's response to different frequency signals. The amplitude-frequency response of the system is obtained through the relationship between the given signal and the system response.

[0107] Figure 4 Given the sweep frequency curve of a robot joint under a certain load, the anti-resonant frequency f can be easily obtained from the sweep frequency curve. AR =13Hz, resonant frequency is f R =19Hz; where the anti-resonant 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 of the amplitude response curve can be obtained by differentiating it, and the minimum and maximum values ​​can be obtained from the changes in the derivative; therefore, the reduction ratio N = 101 of the robot joint and the load damping coefficient b can also be obtained. L =0.

[0108] Specifically, based on the transfer function between electromagnetic torque and load angular velocity in equation (2.4) above, the parameters to be identified for the joint model of the collaborative robot are obtained. These parameters include: motor moment of inertia J. M (unit: kg·m2), load rotational 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 is N·m·s / rad).

[0109] S7. Based on 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.

[0110] Furthermore, the transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts: the transfer function between the electromagnetic torque and the motor angular velocity in the low-frequency range, the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency range, and the transfer function between the electromagnetic torque and the motor angular velocity in the high-frequency range. The expressions for the three parts are as follows:

[0111]

[0112] Among them, f ARf is the anti-resonant frequency. R The resonant frequency is given by offset1 and offset2, which are two constants used to correct the defined interval and can be modified according to the actual frequency sweep curve. This is the transfer function between electromagnetic torque and motor angular velocity in the low-frequency range; This is the transfer function between electromagnetic torque and motor angular velocity in the mid-frequency range; This is the transfer function between electromagnetic torque and motor angular velocity in the high-frequency range.

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

[0114] Furthermore, according to equation (3.1) above, the amplitude of this transfer function in the high-frequency range is only related to the moment of inertia of the motor. Therefore, the moment of inertia J of the motor can be calculated. M Therefore, based on the transfer function between the electromagnetic torque and the motor angular velocity in the high-frequency band, the moment of inertia J of the motor is calculated. M The calculation formula includes:

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

[0116]

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

[0118] Then J M The formula for calculation is:

[0119]

[0120] In this embodiment, to further improve J M The accuracy of identification can be improved by taking multiple sets of f0 sweep amplitude values ​​and J. M The mean.

[0121] The load moment of inertia J can be calculated using equations (2.9) and (2.10). L and stiffness coefficient K s The calculation formula is as follows:

[0122]

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

[0124]

[0125] Using the transfer function between the low-frequency electromagnetic torque and the motor angular velocity in equation (3.5), the damping coefficient b of the transmission mechanism is... s For identification, the damping coefficient of a typical motor is very small, so a rough range can be given, and the optimization function is:

[0126]

[0127] Where Mag(2πω) is the sweep amplitude corresponding to angular velocity ω, when f(b M When b reaches its minimum value, M The motor damping coefficient is used for identification.

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

[0129]

[0130] Where Mag(2πω) is the sweep amplitude corresponding to angular velocity ω, when f(b s When b reaches its minimum value, s The damping coefficient of the transmission mechanism is to be identified.

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

[0132] Substituting the identified parameters into equation (2.6) above, the model identification curve obtained using the parameter identification method of this application can be plotted; please refer to Figure 5 This is a comparison chart 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 Bode plot of the joint model obtained by the identification method of this application and the Bode plot of the actual FRF sweep of the joint model. Figure 5 (b) A comparison of the phase response between the identified Bode plot of the joint model obtained by the identification method of this application and the Bode plot of the actual FRF sweep of the joint model; it can be seen that the amplitude response obtained by the identification method used in this application can well reflect the resonant frequency and anti-resonant frequency of the system, and the amplitude characteristics of 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 identified model remains unchanged at -90°. This is because the presence of the low-pass filter of the velocity feedback loop in the actual system causes the phase angle of the actual system to continuously decay in the high frequency band, but this has little impact on the system modeling and controller optimization design of the robot joint; therefore, the identification method used in this application has high accuracy in identifying parameters.

[0133] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some 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 embodiments, this application also provides a collaborative robot joint model parameter identification system, which can be used to execute the above-described collaborative robot joint model parameter identification method. For ease of explanation, the structural diagram of an embodiment of the collaborative robot joint model parameter identification system only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the system, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0135] Please see Figure 6 In another embodiment of this application, a collaborative robot joint model parameter identification system is provided. The system includes an equivalent module 101, an equation establishment module 102, a transformation module 103, a block diagram construction module 104, an analysis module 105, a parameter acquisition module 106, a partitioning module 107, and a parameter identification module 108.

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

[0137] The equation-establishing module 102 is used to establish dynamic equations based on the dual-inertia elastic model.

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

[0139] The block diagram construction module 104 is used to construct a control block diagram of the dual-inertia elastic model based on 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 the transfer function between electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and load angular velocity.

[0141] The module 106 for obtaining parameters to be identified is used to obtain the parameters to be identified of the joint model of the collaborative robot based on 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 based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity.

[0144] It should be noted that the collaborative robot joint model parameter identification system and the collaborative robot joint model parameter identification method of this application correspond one-to-one. The technical features and beneficial effects described in the above-mentioned embodiments of the collaborative robot joint model parameter identification method are all applicable to the embodiments of the collaborative robot joint model parameter identification system. For details, please refer to the description in the embodiments of the method of this application, which will not be repeated here.

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

[0146] In another embodiment, an electronic device is provided for implementing a method for identifying joint model parameters of a collaborative robot, including 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, it implements a method for identifying joint model parameters of a collaborative robot according to any embodiment of this application.

[0147] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.

[0148] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and memory.

[0149] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0150] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

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

[0152] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments of this application, but the implementation of this application is not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of this application shall be considered equivalent substitutions and shall be included within the protection scope of this application.

Claims

1. A method for identifying joint model parameters of a collaborative robot, characterized in that, Includes the following steps: The components of a collaborative robot joint are equivalent to a dual-inertia elastic model. Based on the aforementioned dual-inertia elastic model, the dynamic equations are established; The Laplace transform of the dynamic equations yields the transfer function of the two-inertia elastic model. Based on the transfer function of the dual-inertia elastic model, construct the control block diagram of the dual-inertia elastic model; The control block diagram of the dual-inertia elastic model is analyzed to obtain the transfer function between electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and load angular velocity. Based on the transfer function between the electromagnetic torque and the load angular velocity, the parameters to be identified for the joint model of the collaborative robot are obtained. Based on 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; 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.

2. The method for identifying joint model parameters of a collaborative robot according to claim 1, characterized in that, The dynamic equation is: Among them, J M J represents the inertia of the motor. L Load inertia; K s T is the stiffness coefficient of the transmission mechanism; N is the reduction ratio of the reducer; T E T L and T W These are the motor input electromagnetic torque, load torque, and rotating shaft torsional torque, respectively; b M b L θ and bs are the damping coefficients of the motor, load, and transmission mechanism, respectively; θ M ω M θ L and ω L These are the motor angle, motor angular velocity, load angle, and load angular velocity, respectively. The first derivative of the motor angle; The second derivative of the motor angle; The first derivative of the load angle; It is the second derivative of the load angle.

3. The method for identifying joint model parameters of a collaborative robot according to claim 1, characterized in that, The transfer function of the dual-inertia elastic model is: Where S represents the Laplace operator.

4. The method for identifying joint model parameters of a collaborative robot 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 method for identifying joint model parameters of a collaborative robot according to claim 1, characterized in that, After obtaining the transfer functions between electromagnetic torque and motor angular velocity, and between electromagnetic torque and load angular velocity, the method further includes: Frequency sweeping was performed on the joints of the collaborative robot to obtain the amplitude response curve and phase response curve of electromagnetic torque to motor angular velocity; The anti-resonance frequency and resonance frequency are obtained from the amplitude response curve and phase response curve.

6. The method for identifying joint model parameters of a collaborative robot according to claim 1, characterized in that, The transfer function between the electromagnetic torque and the motor angular velocity is divided into three parts: the transfer function between the electromagnetic torque and the motor angular velocity in the low-frequency range, the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency range, and the transfer function between the electromagnetic torque and the motor angular velocity in the high-frequency range; the expressions for the three parts are as follows: Among them, f AR f is the anti-resonant frequency. R Here, is the resonant frequency, and offset1 and offset2 are two constants used to correct the defined interval.

7. The method for identifying joint model parameters of a collaborative robot 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: Based on the transfer function between electromagnetic torque and motor angular velocity in the high-frequency band, the moment of inertia J of the motor is calculated. M The calculation formula includes: Let the sweep amplitude at f0 be Mag(f0), where f0 > 2π(f R +offset2), then we have the equation: Where f0 is the frequency corresponding to the sweep frequency; J M The formula for calculation is: The load moment of inertia J is calculated based on the anti-resonance frequency and the resonance frequency. L and stiffness coefficient K s , the calculation formula is:

8. The method for identifying joint model parameters of a collaborative robot according to claim 6, characterized in that, The step of identifying the parameters of the collaborative robot joint model based on the three parts of the transfer function between the electromagnetic torque and the motor angular velocity also includes: Based on the transfer function between the electromagnetic torque and the motor angular velocity in the mid-frequency band, the damping coefficient b of the transmission mechanism is calculated. s The calculation formula is: Where Mag(2πω) is the sweep frequency amplitude corresponding to angular velocity ω, when f(b s When b reaches its minimum value, s The damping coefficient of the transmission mechanism for identification; Based on the transfer function between the electromagnetic torque and the motor angular velocity in the low-frequency band, the motor damping coefficient b is calculated. M The calculation formula is: Where Mag(2πω) is the sweep frequency amplitude corresponding to angular velocity ω, when f(b M When b reaches its minimum value, M The motor damping coefficient is used for identification.

9. A collaborative robot joint model parameter identification system, characterized in that, A method for identifying joint model parameters of a collaborative robot, applicable to any one of claims 1-8, includes an equivalent module, an equation establishment module, a transformation module, a block diagram construction module, an analysis module, a parameter acquisition module, a partitioning module, and a parameter identification module. The equivalent module is used to convert the components of the collaborative robot joint into a dual-inertia elastic model. The equation-establishing module is used to establish dynamic equations based on the dual-inertia elastic model. The transformation module is used to perform a Laplace transform on the dynamic equations to obtain the transfer function of the two-inertia elastic model. The block diagram construction module is used to construct the control block diagram of the dual-inertia elastic model based on 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 the transfer function between electromagnetic torque and motor angular velocity, and the transfer function between electromagnetic torque and 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 based on 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 based on the frequency domain characteristics of the transfer function between the electromagnetic torque and the motor angular velocity. The parameter identification module is used to identify the parameters of the collaborative robot joint model based on 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 includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores computer program instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a collaborative robot joint model parameter identification method as described in any one of claims 1-8.

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