Motor modeling method, device, computer equipment, storage medium and program product
By combining systematic identification of the reference motor with measured physical parameters, and using parameter mapping coefficients to establish a target motor model, the problem of low motor modeling efficiency in existing technologies is solved, and a highly efficient motor modeling process is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI AWINIC TECH CO LTD
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, motor modeling methods are inefficient and require cumbersome parameter acquisition and calculation processes, especially for internal parameters that cannot be directly measured, such as magnetic force coefficients, which require iterative fitting.
By systematically identifying the reference motor, determining the reference motor model and physical parameters, and combining the measured physical parameters of the reference and target motors obtained from actual measurements, the parameter mapping coefficients are calculated. The target motor model is then established using the mapping coefficients, avoiding the tedious iterative fitting process.
This method achieves highly efficient motor modeling, avoiding the inefficiency caused by the inability to directly measure internal parameters in traditional methods, and simplifies the modeling process.
Smart Images
Figure CN122113300A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of electronic technology, and in particular to a motor modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology
[0002] In daily life, people are increasingly reliant on electronic devices such as smartphones and tablets, and are also placing higher and higher demands on the user experience of these devices. Among these demands, the touch interaction experience between users and electronic devices is a crucial aspect. Typically, these electronic devices are equipped with motors to provide users with tactile vibrations, giving them a direct touch interaction experience.
[0003] To achieve precise control of a motor, it is necessary to model the motor. In existing technologies, motor modeling can be achieved based on white-box modeling methods. White-box modeling attempts to directly describe the motor's behavior through physical equations, requiring precise knowledge of the motor's internal parameters. Modeling the motor is then based on both the physical equations and these internal parameters.
[0004] Some internal parameters of a motor cannot be directly measured. For example, the magnetic flux density of a motor requires repeated iterations and fitting to obtain. This results in a tedious process of parameter acquisition, processing, and calculation for each motor when modeling it, making the white-box modeling method inefficient. Summary of the Invention
[0005] Therefore, it is necessary to provide a motor modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product to address the aforementioned technical problems, so as to achieve efficient modeling of motors.
[0006] Firstly, this application provides a motor modeling method, including:
[0007] Based on the system identification of the reference motor, the reference motor model is determined.
[0008] Based on the reference motor model, determine the reference physical parameters corresponding to the reference motor;
[0009] Actual measurements are performed on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor.
[0010] The parameter mapping coefficients are determined based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0011] The target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
[0012] In one embodiment, the target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model, including:
[0013] The target numerator coefficients of the target motor model are determined based on the numerator coefficients of the reference motor model and the parameter mapping coefficients.
[0014] The target denominator coefficients of the target motor model are determined based on the reference physical parameters and the parameter mapping coefficients.
[0015] The target motor model is determined based on the target numerator coefficient and the target denominator coefficient.
[0016] In one embodiment, the parameter mapping coefficients include a first parameter mapping coefficient, and determining the parameter mapping coefficients based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor includes:
[0017] Obtain the preset oscillator mass of the reference motor and the preset oscillator mass of the target motor;
[0018] The system gain of the reference motor is calculated based on the oscillator mass of the reference motor and the measured physical parameters of the reference motor.
[0019] Calculate the system gain of the target motor based on the oscillator mass of the target motor and the measured physical parameters of the target motor;
[0020] Calculate a first ratio between the system gain of the target motor and the system gain of the reference motor, and use the first ratio as the first parameter mapping coefficient;
[0021] Determining the target numerator coefficients of the target motor model based on the numerator coefficients of the reference motor model and the parameter mapping coefficients includes:
[0022] Calculate the first product between the numerator coefficients of the reference motor model and the first parameter mapping coefficients, and use the first product as the target numerator coefficients.
[0023] In one embodiment, the parameter mapping coefficients include second parameter mapping coefficients, the measured physical parameters of the reference motor include the measured resonant angular frequency of the reference motor, the measured physical parameters of the target motor include the measured resonant angular frequency of the target motor, and the reference physical parameters include a reference resonant angular frequency. Determining the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor includes:
[0024] Calculate a second ratio between the measured resonant angular frequency of the target motor and the measured resonant angular frequency of the reference motor, and use the second ratio as the mapping coefficient of the second parameter;
[0025] The step of determining the target denominator coefficient of the target motor model based on the reference physical parameters and the parameter mapping coefficients includes:
[0026] Calculate the second product between the second parameter mapping coefficient and the reference resonant angular frequency, and determine the target denominator coefficient based on the second product.
[0027] In one embodiment, the parameter mapping coefficient includes a third parameter mapping coefficient, the measured physical parameters of the reference motor include the measured damping ratio of the reference motor, the measured physical parameters of the target motor include the measured damping ratio of the target motor, and the reference physical parameters include a reference damping ratio. Determining the parameter mapping coefficient based on the measured physical parameters of the reference motor and the target motor includes:
[0028] Calculate the third ratio between the measured damping ratio of the target motor and the measured damping ratio of the reference motor, and use the third ratio as the mapping coefficient of the third parameter;
[0029] The step of determining the target denominator coefficient of the target motor model based on the reference physical parameters and the parameter mapping coefficients includes:
[0030] Calculate the third product between the third parameter mapping coefficient and the reference damping ratio, and determine the target denominator coefficient based on the third product.
[0031] In one embodiment, after obtaining the target motor model of the target motor based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model, the method further includes:
[0032] In response to a model update command, the target motor model is updated if preset model update conditions are met; wherein, the model update condition is that the driving voltage of the target motor maintains a constant amplitude within a preset time period.
[0033] Secondly, this application also provides a motor modeling apparatus, comprising:
[0034] The first determining module is used to determine the reference motor model of the reference motor based on the system identification of the reference motor.
[0035] The second determining module is used to determine the reference physical parameters corresponding to the reference motor based on the reference motor model.
[0036] The acquisition module is used to perform actual measurements on the reference motor and the target motor, and acquire the measured physical parameters of the reference motor and the target motor.
[0037] The third determining module is used to determine the parameter mapping coefficients based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0038] The fourth determining module is used to obtain the target motor model of the target motor based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0040] Based on the system identification of the reference motor, the reference motor model is determined.
[0041] Based on the reference motor model, determine the reference physical parameters corresponding to the reference motor;
[0042] Actual measurements are performed on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor.
[0043] The parameter mapping coefficients are determined based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0044] The target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0046] Based on the system identification of the reference motor, the reference motor model is determined.
[0047] Based on the reference motor model, determine the reference physical parameters corresponding to the reference motor;
[0048] Actual measurements are performed on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor.
[0049] The parameter mapping coefficients are determined based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0050] The target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
[0051] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:
[0052] Based on the system identification of the reference motor, the reference motor model is determined.
[0053] Based on the reference motor model, determine the reference physical parameters corresponding to the reference motor;
[0054] Actual measurements are performed on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor.
[0055] The parameter mapping coefficients are determined based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0056] The target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
[0057] The aforementioned motor modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product obtain a reference motor model by systematically identifying a reference motor. Based on the reference motor model, reference physical parameters of the reference motor are determined, and the measured physical parameters of the reference motor and the target motor are obtained through direct measurement. Based on these measured physical parameters, parameter mapping coefficients are determined. Finally, based on the parameter mapping coefficients, reference physical parameters, and the reference motor model, a target motor model is obtained. In this technical solution, the parameter mapping coefficients determined by the directly measurable measured physical parameters of the target motor and the reference motor are used to characterize the differences between different motors, establishing a mapping relationship between the target motor and the reference motor. When establishing the target motor model, the target motor model can be derived by combining the aforementioned parameter mapping coefficients and the reference motor model. As can be seen, this technical solution avoids the tedious traversal fitting process required in traditional white-box modeling because some internal parameters (such as magnetic force coefficient) cannot be directly measured, and also eliminates the need for complex system identification of each target motor. It utilizes easily measurable parameters to achieve rapid model construction, significantly improving the efficiency of motor modeling. Attached Figure Description
[0058] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0059] Figure 1 This is a flowchart illustrating a motor modeling method in one embodiment;
[0060] Figure 2 This is a flowchart illustrating the motor modeling method in another embodiment;
[0061] Figure 3 This is a flowchart illustrating the motor modeling method in yet another embodiment;
[0062] Figure 4 This is a flowchart illustrating the motor modeling method in yet another embodiment;
[0063] Figure 5 This is a flowchart illustrating the motor modeling method in yet another embodiment;
[0064] Figure 6 This is a structural block diagram of a motor modeling device in one embodiment;
[0065] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0067] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.
[0068] In one embodiment, such as Figure 1As shown, a motor modeling method is provided. This embodiment illustrates the application of this method to a terminal, which is equipped with a motor. This motor can be a linear resonant actuator (LRA) or a voice coil motor (VCM), or any motor that can be installed on the terminal to provide touch vibration feedback. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0069] S110: Perform system identification on the reference motor and determine the reference motor model.
[0070] Specifically, the reference motor model characterizes the transmission relationship between the input excitation and output state of the reference motor, and is obtained based on the system identification of the reference motor. System identification is a technique for constructing a mathematical description of a system by analyzing its input excitation and output state.
[0071] As an example, when constructing a reference motor model, various input excitation signals can be used, such as swept frequency signals, pseudo-random binary sequences, Gaussian white noise, step signals, etc. The selected input excitation signal is applied to the reference motor, and the output state signal of the reference motor is detected. As an example, the detected output state signal of the reference motor can be the displacement, velocity, acceleration, etc. of the motor's oscillator. After determining the input excitation signal of the reference motor and acquiring the output state signal generated by the reference motor in response to the input excitation signal, these input excitation signals and output state signals are processed by a system identification algorithm to obtain the reference motor model. As an example, the system identification algorithm can be the least squares method, recursive least squares method, prediction error method, frequency response analysis method, etc.
[0072] In some embodiments, technicians select one or more representative samples from a specific model or batch of motors as reference motors. A motor model of the reference motor is obtained using a high-precision system identification method. After obtaining the reference motor model based on system identification, the reference motor model is stored in a terminal. The terminal can access the reference motor model.
[0073] In some feasible embodiments, in order to construct an accurate reference motor model, the input excitation signal input to the reference motor during system identification satisfies the following conditions: the frequency of the input excitation signal is between the minimum effective frequency corresponding to the bandwidth of the reference motor and twice the maximum effective frequency corresponding to the bandwidth of the reference motor; the amplitude of the input excitation signal is first increased for a preset time and then decreased for a preset time.
[0074] S120: Determine the reference physical parameters corresponding to the reference motor based on the reference motor model.
[0075] Specifically, after acquiring the reference motor model, the terminal determines the reference physical parameters of the reference motor based on the values in the reference motor model. Reference physical parameters refer to parameter values that characterize the inherent physical properties of the reference motor, such as the reference resonant angular frequency, reference damping ratio, reference impedance, and reference inductance. The reference motor model is obtained based on the system identification of the reference motor; therefore, it can be understood that the reference physical parameters can be directly extracted through the aforementioned system identification process.
[0076] A reference motor model is typically represented as a rational fraction with numerator and denominator polynomials, for example, as a second-order or higher-order transfer function in the Laplace domain (S-domain), or as difference equation coefficients in the discrete Z-domain. This reference motor model can reflect the frequency response, gain, and phase characteristics of the reference motor under ideal conditions, and includes details such as system parasitic resonances and structural damping that are difficult to describe with simple physical formulas.
[0077] In some feasible embodiments, a reference motor model is obtained based on a voltage-acceleration system identification method. Specifically, in the voltage-acceleration system identification method, the input excitation signal of the reference motor is the driving voltage of the reference motor, and the output excitation signal of the reference motor is the output state signal, which is the acceleration of the reference motor. When the reference motor model is obtained using the voltage-acceleration system identification method, it is necessary to first construct the transfer function under the voltage-acceleration system identification method, which is expressed as follows (1):
[0078] (1);
[0079] in, The transfer function under the voltage-acceleration system identification method. For the reference motor acceleration, For the reference motor drive voltage, For reference motor magnetic force coefficient, For reference motor damping ratio, For the reference motor's resonant angular frequency, For reference, the oscillator mass of the motor, The impedance is used as a reference motor.
[0080] From the above equation (1), the system gain of the reference motor is expressed as follows (2):
[0081] (2);
[0082] Where G is the system gain of the transfer function corresponding to the reference motor model. It can be seen that the reference motor model shown in Equation (1) has two poles and two zeros, but both zeros are fixed at 0. Equation (1) above does not consider the influence of noise and structural response in the reference motor.
[0083] Therefore, equation (1) can be rewritten into a more general form. Specifically, the transfer function under the voltage-acceleration system identification method can be expressed as equation (3) as follows:
[0084] (3);
[0085] Where a, b, and c are the function coefficients of the transfer function under the voltage-acceleration system identification method.
[0086] Combining the above equation (3), the specific values of each parameter in equation (3) can be determined using the voltage-acceleration system identification method. These specific parameter values are the reference physical parameters. In some feasible embodiments, the reference physical parameters specifically include the reference damping ratio. With reference resonant angular frequency In this embodiment, the reference motor model is used for the reference motor. It can be expressed as follows (4):
[0087] (4);
[0088] in, , as well as These are the numerator coefficients of the reference motor model identified through system identification.
[0089] S130: Perform actual measurements on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor.
[0090] Specifically, after determining the reference physical parameters, the terminal acquires the measured physical parameters of the reference motor and the target motor. The measured physical parameters of the reference motor are obtained directly from real-time measurements of the reference motor at the current moment, and the measured physical parameters of the target motor are obtained directly from real-time measurements of the target motor at the current moment. The target motor is the motor currently being modeled.
[0091] In some feasible embodiments, both the reference motor and the target motor are located within the terminal. The terminal determines the measured physical parameters of the reference motor and the target motor through a detection circuit. In this embodiment, both the reference motor and the target motor are located within the terminal. The terminal synchronously sends detection signals to the reference motor and the target motor through a drive chip in the detection circuit, causing the reference motor and the target motor to operate. The measured physical parameters of the reference motor and the target motor are then obtained based on measurements taken by the detection chip in the detection circuit.
[0092] In some feasible embodiments, the measured physical parameters of the reference motor and the target motor are both directly measurable physical parameters. The measured physical parameters of the reference motor include at least the measured resonant angular frequency, the measured damping ratio, the measured impedance, and the measured peak value of the back electromotive force of the reference motor. The measured physical parameters of the target motor include at least the measured resonant angular frequency, the measured damping ratio, the measured impedance, and the measured peak value of the back electromotive force of the target motor.
[0093] S140: Determine the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor.
[0094] Specifically, after obtaining the measured physical parameters of the reference motor and the target motor, the terminal determines the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor.
[0095] Parameter mapping coefficients are a set of scaling factors used to describe the offset or scaling of the physical characteristics of a target motor relative to a reference motor. Since motors of the same model are identical in mechanical structure and electromagnetic design, their individual differences mainly stem from manufacturing tolerances or environmental influences. Therefore, there is a strong physical mapping relationship between the model parameters of the target motor and the model parameters of the reference motor. By determining the measured physical parameters of the reference motor and the target motor, parameter mapping coefficients are established. These coefficients describe the physical mapping relationship between the model parameters of the target and reference motors, enabling the transfer of the reference motor model to the target motor, thus achieving the modeling of the target motor.
[0096] S150: Based on the parameter mapping coefficients, reference physical parameters, and reference motor model, obtain the target motor model of the target motor.
[0097] Specifically, after the terminal calculates the parameter mapping coefficients, it determines the target motor model based on the parameter mapping coefficients, reference physical parameters, and reference motor model. In other words, the calculated parameter mapping coefficients are applied to the reference motor model to adjust it, thereby obtaining a mathematical model suitable for the current target motor, thus determining the target motor model.
[0098] In some feasible embodiments, the model structure of the reference motor model is consistent with that of the target motor model. For example, if the reference motor model is as shown in equation (4) above, then the target motor model will also have the same structure as shown in equation (4), which is a second-order transfer function.
[0099] In this embodiment, parameter mapping coefficients are determined using the directly measurable measured physical parameters of a reference motor and the target motor. These coefficients characterize the differences between different motors, establishing a mapping relationship between the target motor and the reference motor. When building the target motor model, the model can be derived by combining the aforementioned parameter mapping coefficients with the reference motor model. This technical solution avoids the tedious iterative fitting process required in traditional white-box modeling due to the inability to directly measure some internal parameters (such as magnetic force coefficients), and eliminates the need for complex system identification for each target motor. It utilizes easily measurable parameters to achieve rapid model construction, significantly improving the efficiency of motor modeling.
[0100] In one exemplary embodiment, such as Figure 2 As shown, S150 includes S210 to S230. Wherein:
[0101] S210: Determine the target numerator coefficients of the target motor model based on the numerator coefficients and parameter mapping coefficients of the reference motor model.
[0102] S220: Determine the target denominator coefficients of the target motor model based on the reference physical parameters and parameter mapping coefficients.
[0103] S230: Determine the target motor model based on the coefficients of the target numerator and the target denominator.
[0104] Specifically, the terminal processes the numerator coefficients of the reference motor model using parameter mapping coefficients to determine the target numerator coefficients of the target motor model. Simultaneously, the terminal processes the reference physical parameters using parameter mapping coefficients to determine the target denominator coefficients of the target motor model. The target motor model is then constructed based on the target numerator and target denominator coefficients.
[0105] The target motor model has the same structure as the reference motor model; that is, the construction method and order of the target motor model are identical to those of the reference motor model. For example, if the reference motor model is a second-order system, the target motor model will also remain second-order. If the reference motor model is a higher-order system (e.g., fourth-order or sixth-order), the target motor model will also maintain the corresponding order, only the function coefficients will change. In some feasible embodiments, if the transfer function corresponding to the reference motor model is a second-order transfer function, then the transfer function corresponding to the target motor model will also be a second-order transfer function, making both the reference motor model and the target motor model second-order systems.
[0106] Combining equations (1) and (3) above, it can be understood that in the motor model in the form of a second-order transfer function, the numerator coefficient is related to the system gain and determines the amplitude of the motor vibration, while the numerator coefficient is determined by the damping ratio and the resonant angular frequency, which determine the dynamic response characteristics of the motor vibration. In this embodiment, processing the numerator and denominator coefficients of the reference motor model separately enables more accurate independent correction for the different physical differences between the target motor and the reference motor, avoiding mutual interference between parameters.
[0107] In one exemplary embodiment, the parameter mapping coefficients include first parameter mapping coefficients, such as... Figure 3 As shown, S140 includes S310 to S340. Wherein:
[0108] S310: Obtain the preset oscillator mass of the reference motor and the preset oscillator mass of the target motor.
[0109] S320: Calculate the system gain of the reference motor based on the oscillator mass of the reference motor and the measured physical parameters of the reference motor.
[0110] S330: Calculate the system gain of the target motor based on the oscillator mass and the measured physical parameters of the target motor.
[0111] S340: Calculate the first ratio between the system gain of the target motor and the system gain of the reference motor, and use the first ratio as the first parameter mapping coefficient.
[0112] Specifically, the parameter mapping coefficients include a first parameter mapping coefficient, which is used to adjust the numerator coefficients of the reference motor model. The terminal acquires the preset oscillator mass of the reference motor and the preset oscillator mass of the target motor. Based on the oscillator mass of the reference motor and the measured physical parameters of the reference motor, the first system gain of the reference motor is calculated. Based on the oscillator mass of the target motor and the measured physical parameters of the target motor, the second system gain of the target motor is calculated. The first ratio between the second system gain and the first system gain is calculated, thereby obtaining the first parameter mapping coefficient.
[0113] The oscillator masses of both the reference motor and the target motor are pre-stored in the terminal, for example, in the terminal's storage space. The oscillator mass of the reference motor is pre-measured by those skilled in the art, while the oscillator mass of the target motor is usually indicated in the target motor's product specification sheet at the time of manufacture and can be obtained based on the target motor's product specification sheet.
[0114] In some feasible embodiments, referring to the above equation (2), the first system gain of the reference motor is expressed as the following equation (5):
[0115] (5);
[0116] in, For the reference motor's system gain, To reference the measured magnetic flux coefficient of the motor, For reference, the oscillator mass of the motor, The reference motor's measured impedance can be obtained directly from the terminal, and the reference motor's oscillator mass can be obtained directly from the terminal's actual measurement of the reference motor.
[0117] However, the measured magnetic flux coefficient of the reference motor cannot actually be obtained directly through actual measurement of the reference motor. Therefore, the measured magnetic flux coefficient of the reference motor is determined by the measured physical parameters of the reference motor. In some feasible embodiments, the measured magnetic flux coefficient of the reference motor is determined based on the measured peak value of the back electromotive force, the measured damping ratio, the measured impedance, and the measured resonant angular frequency of the reference motor. In this embodiment, the measured magnetic flux coefficient of the reference motor is calculated using the measured magnetic flux coefficient calculation formula of the reference motor, which is expressed as follows (6):
[0118] (6);
[0119] in, To reference the measured resonant angular frequency of the motor, For reference, the measured peak value of the back electromotive force of the motor, For reference, the measured damping ratio of the motor, This refers to the driving voltage of the reference motor during actual measurements. The measured peak back electromotive force, measured damping ratio, and measured resonant angular frequency of the reference motor can all be directly obtained from the actual measurements of the reference motor by the terminal.
[0120] Specifically, equation (6) above can be derived based on the transfer function (i.e., equation (1) above) under the voltage-acceleration system identification method. In equation (1) above, let The resonant angular frequency of the motor is at the resonant frequency point. At this point, the parameter expression for the maximum speed of the motor oscillator is as follows (7):
[0121] (7);
[0122] in, This represents the amplitude of the motor's drive voltage. This represents the maximum speed of the motor oscillator.
[0123] Furthermore, the formula for the free oscillation velocity of the motor oscillator is as follows (8):
[0124] (8):
[0125] By combining equation (8) and equation (7) above, we can obtain equation (6) above.
[0126] In some feasible embodiments, referring to the above equation (2), the system gain of the target motor is expressed as the following equation (9):
[0127] (9);
[0128] in, For the system gain of the target motor, The measured magnetic flux density of the target motor is given. For the oscillator mass of the target motor, The measured impedance of the target motor is given by the terminal. The oscillator mass of the target motor can be directly obtained by the terminal, and the measured impedance of the target motor can be directly obtained by the terminal through actual measurement of the target motor.
[0129] In some feasible embodiments, the measured magnetic flux coefficient of the target motor is determined based on the measured peak value of the target motor's back electromotive force, the measured damping ratio of the target motor, the measured impedance of the target motor, and the measured resonant angular frequency of the target motor. In this embodiment, the measured magnetic flux coefficient of the target motor is calculated using the measured magnetic flux coefficient calculation formula of the target motor, which is expressed as follows (10):
[0130] (10);
[0131] in, The measured resonant angular frequency of the target motor. The measured peak value of the back electromotive force of the target motor is given. The measured damping ratio of the target motor. This refers to the driving voltage of the target motor during actual measurement. The measured peak back electromotive force, measured damping ratio, and measured resonant angular frequency of the target motor can all be directly obtained from the actual measurements of the target motor by the terminal.
[0132] The first ratio between the system gain of the target motor and the system gain of the reference motor is calculated to obtain the first parameter mapping coefficient. Specifically, the first parameter mapping coefficient is expressed as follows (11):
[0133] (11);
[0134] Where gain1 is the first parameter mapping coefficient.
[0135] In conjunction with the above embodiments, S210 further includes the following steps:
[0136] Calculate the first product between the numerator coefficients of the reference motor model and the first parameter mapping coefficients, and use the first product as the target numerator coefficients.
[0137] Specifically, for each numerator coefficient of the reference motor model, it is multiplied by the first parameter mapping coefficient to obtain multiple first products. The determined first products are used as the target numerator coefficients of the target motor model. The conversion relationship between the numerator coefficients of the reference motor model and the target numerator coefficients can be specifically expressed as follows (12):
[0138] (12);
[0139] in, , as well as The coefficients of the target numerator term in the target motor model.
[0140] In some feasible embodiments, the target denominator coefficients of the target motor model are determined based on the measured physical parameters of the target motor, and the target motor model is constructed based on the target numerator and denominator coefficients. Specifically, the target denominator coefficients of the target motor model are determined based on the measured resonant angular frequency and the measured damping ratio of the target motor, and the target motor model is then determined based on the target numerator and denominator coefficients.
[0141] In this embodiment, by comparing the system gain, the problem of inconsistent vibration amplitude caused by individual differences in the target motor is accurately corrected, ensuring the accuracy of the amplitude prediction for the target motor model.
[0142] In an exemplary embodiment, the parameter mapping coefficients include second parameter mapping coefficients, the measured physical parameters of the reference motor include the measured resonant angular frequency of the reference motor, the measured physical parameters of the target motor include the measured resonant angular frequency of the target motor, and the reference physical parameters include the reference resonant angular frequency, such as... Figure 4 As shown, S140 includes S410. Wherein:
[0143] S410: Calculate the second ratio between the measured resonant angular frequency of the target motor and the measured resonant angular frequency of the reference motor, and use the second ratio as the second parameter mapping coefficient.
[0144] Specifically, the second ratio between the measured resonant angular frequency of the target motor and the measured resonant angular frequency of the reference motor is calculated to obtain the second parameter mapping coefficient. Specifically, this second parameter mapping coefficient is expressed as follows (13):
[0145] (13);
[0146] Where gain2 is the second parameter, the mapping coefficient.
[0147] In conjunction with the above embodiments, S220 further includes the following steps:
[0148] Calculate the second product between the second parameter mapping coefficient and the reference resonant angular frequency, and determine the target denominator coefficient based on the second product.
[0149] Specifically, since the reference resonant angular frequency is based on the globally fitted optimal solution obtained after system identification of the reference motor, there is a certain difference between it and the measured resonant angular frequency of the reference motor obtained by direct measurement. Based on the second parameter mapping coefficient, assuming that the measurement error or deviation is linear among similar motors, the superior frequency characteristics identified in the reference motor model can be transferred to the target motor by the ratio of the measured resonant angular frequencies. This allows the modeling of the target motor to be based on a better target resonant angular frequency, which can be considered as the superior frequency characteristics obtained after system identification of the target motor.
[0150] Calculate the second product between the second parameter mapping coefficient and the reference resonant angular frequency. This second product is the target resonant angular frequency of the target motor, which can be expressed as follows (14):
[0151] (14);
[0152] in, The target resonant angular frequency.
[0153] Combining equation (3) above, the coefficient of the denominator term of the transfer function under the voltage-acceleration system identification method is: and Therefore, in some feasible embodiments, the coefficient of the target denominator term in the target motor model is: and .
[0154] Using the measured resonant angular frequency of the target motor directly as the model parameter of the target motor model may introduce measurement noise. In this embodiment, the second parameter mapping coefficient is calculated accordingly, and measurement deviation is taken into account to determine a more ideal target resonant angular frequency for the target motor during modeling. This ensures that the target motor model reflects its own frequency characteristics while retaining the noise resistance and structural response details obtained from the reference motor model through system identification, making the established target motor model more accurate.
[0155] In an exemplary embodiment, the parameter mapping coefficients include third parameter mapping coefficients, the measured physical parameters of the reference motor include the measured damping ratio of the reference motor, the measured physical parameters of the target motor include the measured damping ratio of the target motor, and the reference physical parameters include the reference damping ratio, such as... Figure 5 As shown, S140 includes S510. Wherein:
[0156] S510: Calculate the third ratio between the measured damping ratio of the target motor and the measured damping ratio of the reference motor, and use the third ratio as the third parameter mapping coefficient.
[0157] Specifically, the third ratio between the measured damping ratio of the target motor and the measured damping ratio of the reference motor is calculated to obtain the third parameter mapping coefficient. Specifically, this third parameter mapping coefficient is expressed as follows (15):
[0158] (15);
[0159] Wherein, gain3 is the third parameter mapping coefficient.
[0160] In conjunction with the above embodiments, S220 further includes the following steps:
[0161] Calculate the third product between the third parameter mapping coefficient and the reference damping ratio, and determine the target denominator coefficient based on the third product.
[0162] Specifically, since the reference damping ratio is based on the globally fitted optimal solution obtained after system identification of the reference motor, there is a certain difference between it and the measured damping ratio of the reference motor obtained by direct measurement. Based on the second parameter mapping coefficient, it is assumed that the measurement error or deviation is linear among similar motors. Therefore, the superior damping ratio characteristics identified in the reference motor model can be transferred to the target motor by using the ratio of the measured damping ratios. This allows the modeling of the target motor to be based on a better target damping ratio, which can be considered as the superior damping ratio characteristics obtained after system identification of the target motor.
[0163] Calculate the third product between the third parameter mapping coefficient and the reference damping ratio. This third product is the target damping ratio of the target motor, which can be expressed as follows (16):
[0164] (16);
[0165] in, The target damping ratio.
[0166] Combining equation (3) above, the coefficient of the denominator term of the transfer function under the voltage-acceleration system identification method is: and Therefore, in some feasible embodiments, the coefficient of the target denominator term in the target motor model is... and In other feasible embodiments, in conjunction with the above-described embodiments for calculating the target resonant angular frequency, the target denominator coefficient in the target motor model is: and .
[0167] Similarly, directly using the measured damping ratio of the target motor as the model parameter of the target motor model may introduce measurement noise. In this embodiment, the mapping coefficient of the third parameter is obtained through corresponding calculation, and the measurement deviation is taken into account to determine a more ideal target damping ratio for the target motor during modeling. This ensures that the target motor model reflects its own damping ratio characteristics while retaining the noise resistance and structural response details obtained from the reference motor model through system identification, making the established target motor model more accurate.
[0168] In an exemplary embodiment, after S140, the method further includes the following steps:
[0169] In response to the model update command, the target motor model is updated if the preset model update conditions are met.
[0170] The model update condition is that the driving voltage of the target motor remains constant within a preset time period.
[0171] Specifically, after receiving a model update command, the terminal checks whether the preset model update conditions are met. If the conditions are met, the target motor model is updated; otherwise, the target motor model is not updated. The model update condition is that the driving voltage of the target motor remains constant for a preset time.
[0172] The model update command instructs the target motor model to be updated. Updating the target motor model refers to updating the coefficients of the target numerator and denominator terms in the target motor model. As an example, the model update command can be an instruction input to the terminal by the user, or it can be an instruction generated by the terminal itself, such as an instruction automatically generated by the terminal at a preset update time after the previous update of the target motor model.
[0173] In one embodiment, the terminal drives the target motor to perform actions by driving voltage. When the terminal detects that the amplitude of the driving voltage remains constant within a preset time, it enters the above-mentioned S110-S150, determines the target motor model again, and updates the target motor model.
[0174] In this embodiment, when the target motor is actually working in the terminal, an update mechanism for the target motor model is set up. Since the target motor is inevitably affected by hardware aging and environmental changes during operation, its corresponding target motor model may not match the current actual application scenario. This embodiment sets up an update mechanism for the target motor model, which allows the target motor model to track the thermal drift and aging characteristics of the target motor in real time, while avoiding control instability caused by sudden parameter changes.
[0175] In conjunction with the above embodiments, in some feasible embodiments, the motor modeling method provided in this application can be performed according to the following steps.
[0176] S601: Perform system identification on the reference motor to obtain the reference motor model.
[0177] S602: Determine the reference resonant angular frequency based on the denominator coefficients of the reference motor model. Compared with reference damping ratio And determine the coefficients of the numerator term of the reference motor model. , as well as .
[0178] S603: Real-time measurement of the measured physical parameters of the reference motor, including the measured resonant angular frequency of the reference motor. Measured damping ratio of the reference motor Measured impedance of the reference motor And the measured peak value of the back electromotive force of the reference motor And call the oscillator mass of the reference motor .
[0179] S604: Based on the oscillator mass of the reference motor The measured magnetic flux coefficient of the reference motor is calculated corresponding to the measured physical parameters of the reference motor. Based on the measured magnetic force coefficient of the reference motor Measured impedance of the reference motor Oscillator mass of the reference motor Calculate the system gain of the reference motor .
[0180] S605: Real-time measurement of the target motor's measured physical parameters, including the target motor's measured resonant angular frequency. Measured damping ratio of the target motor Measured impedance of the target motor and the measured peak value of the back electromotive force of the target motor And call the oscillator mass of the target motor .
[0181] S606: Based on the oscillator mass of the target motor The measured magnetic flux coefficient of the target motor is calculated based on the measured physical parameters of the target motor. Based on the measured magnetic flux coefficient of the target motor Measured impedance of the target motor With the target oscillator mass Calculate the system gain of the target motor .
[0182] S607: Based on the system gain of the target motor System gain compared to reference motor The first ratio determines the mapping coefficient of the first parameter. .
[0183] S608: Based on the measured resonant angular frequency of the target motor Measured resonant angular frequency of the reference motor The second ratio between them determines the second parameter mapping coefficient. .
[0184] S609: Based on the measured damping ratio of the target motor Measured damping ratio of the reference motor The third ratio between them determines the third parameter mapping coefficient. .
[0185] S610: The numerator coefficients of the reference motor model , as well as Mapping coefficients with the first parameter respectively Multiplying them together yields the coefficient of the target numerator. , as well as .
[0186] S611: Reference resonant angular frequency Mapping coefficients with the second parameter Multiply to obtain the target resonant angular frequency. .
[0187] S612: Reference damping ratio Mapping coefficients with the third parameter Multiply to obtain the target damping ratio .
[0188] S613: Based on the target resonant angular frequency Damping ratio with target Determine the coefficient of the target denominator. and .
[0189] S614: Based on the coefficients of the target denominator and the target numerator, the target motor model is obtained.
[0190] In the above embodiments, the target motor model was obtained. As shown in equation (17):
[0191] (17).
[0192] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.
[0193] Based on the same inventive concept, this application also provides a motor modeling apparatus for implementing the motor modeling method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more motor modeling apparatus embodiments provided below can be found in the limitations of the motor modeling method described above, and will not be repeated here.
[0194] In one exemplary embodiment, such as Figure 6 As shown, a motor modeling device is provided, including: a first determining module 601, a second determining module 602, an acquisition module 603, a third determining module 604, and a fourth determining module 605, wherein:
[0195] The first determining module 601 is used to determine the reference motor model of the reference motor based on the system identification of the reference motor.
[0196] The second determining module 602 is used to determine the reference physical parameters corresponding to the reference motor based on the reference motor model;
[0197] The acquisition module 603 is used to perform actual measurements on the reference motor and the target motor, and to acquire the measured physical parameters of the reference motor and the target motor.
[0198] The third determining module 604 is used to determine the parameter mapping coefficients based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor.
[0199] The fourth determining module 605 is used to obtain the target motor model of the target motor based on the parameter mapping coefficients, reference physical parameters, and reference motor model.
[0200] In some embodiments, the fourth determining module 605 is further configured to:
[0201] Based on the numerator coefficients and parameter mapping coefficients of the reference motor model, determine the target numerator coefficients of the target motor model; based on the reference physical parameters and parameter mapping coefficients, determine the target denominator coefficients of the target motor model; based on the target numerator and target denominator coefficients, determine the target motor model.
[0202] In some embodiments, the parameter mapping coefficients include first parameter mapping coefficients, and the third determining module 604 is further configured to:
[0203] Obtain the preset oscillator mass of the reference motor and the preset oscillator mass of the target motor; calculate the system gain of the reference motor based on the oscillator mass of the reference motor and the measured physical parameters of the reference motor; calculate the system gain of the target motor based on the oscillator mass of the target motor and the measured physical parameters of the target motor; calculate the first ratio between the system gain of the target motor and the system gain of the reference motor, and use the first ratio as the first parameter mapping coefficient.
[0204] The fourth determining module 605 is also used for:
[0205] Calculate the first product between the numerator coefficients of the reference motor model and the first parameter mapping coefficients, and use the first product as the target numerator coefficients.
[0206] In some embodiments, the parameter mapping coefficients include second parameter mapping coefficients, the measured physical parameters of the reference motor include the measured resonant angular frequency of the reference motor, the measured physical parameters of the target motor include the measured resonant angular frequency of the target motor, the reference physical parameters include the reference resonant angular frequency, and the third determining module 604 is further configured to:
[0207] Calculate the second ratio between the measured resonant angular frequency of the target motor and the measured resonant angular frequency of the reference motor, and use the second ratio as the second parameter mapping coefficient;
[0208] The fourth determining module 605 is also used for:
[0209] Calculate the second product between the second parameter mapping coefficient and the reference resonant angular frequency, and determine the target denominator coefficient based on the second product.
[0210] In some embodiments, the parameter mapping coefficients include third parameter mapping coefficients, the measured physical parameters of the reference motor include the measured damping ratio of the reference motor, the measured physical parameters of the target motor include the measured damping ratio of the target motor, the reference physical parameters include the reference damping ratio, and the third determining module 604 is further configured to:
[0211] Calculate the third ratio between the measured damping ratio of the target motor and the measured damping ratio of the reference motor, and use the third ratio as the third parameter mapping coefficient.
[0212] The fourth determining module 605 is also used for:
[0213] Calculate the third product between the third parameter mapping coefficient and the reference damping ratio, and determine the target denominator coefficient based on the third product.
[0214] In some embodiments, the device further includes:
[0215] The update module is used to update the target motor model in response to the model update command, provided that the preset model update conditions are met; wherein the model update condition is that the driving voltage of the target motor maintains a constant amplitude within a preset time.
[0216] Each module in the aforementioned motor modeling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.
[0217] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a motor modeling method.
[0218] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0219] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0220] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.
[0221] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.
[0222] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0223] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0224] 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 application.
[0225] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A motor modeling method, characterized in that, The method includes: Based on the system identification of the reference motor, the reference motor model is determined. Based on the reference motor model, determine the reference physical parameters corresponding to the reference motor; Actual measurements are performed on the reference motor and the target motor to obtain the measured physical parameters of the reference motor and the target motor. The parameter mapping coefficients are determined based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor. The target motor model of the target motor is obtained based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
2. The method according to claim 1, characterized in that, Based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model, the target motor model of the target motor is obtained, including: The target numerator coefficients of the target motor model are determined based on the numerator coefficients of the reference motor model and the parameter mapping coefficients. The target denominator coefficients of the target motor model are determined based on the reference physical parameters and the parameter mapping coefficients. The target motor model is determined based on the target numerator coefficient and the target denominator coefficient.
3. The method according to claim 2, characterized in that, The parameter mapping coefficients include a first parameter mapping coefficient. Determining the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor includes: Obtain the preset oscillator mass of the reference motor and the preset oscillator mass of the target motor; The system gain of the reference motor is calculated based on the oscillator mass of the reference motor and the measured physical parameters of the reference motor. Calculate the system gain of the target motor based on the oscillator mass of the target motor and the measured physical parameters of the target motor; Calculate a first ratio between the system gain of the target motor and the system gain of the reference motor, and use the first ratio as the first parameter mapping coefficient; Determining the target numerator coefficients of the target motor model based on the numerator coefficients of the reference motor model and the parameter mapping coefficients includes: Calculate the first product between the numerator coefficients of the reference motor model and the first parameter mapping coefficients, and use the first product as the target numerator coefficients.
4. The method according to claim 2, characterized in that, The parameter mapping coefficients include a second parameter mapping coefficient. The measured physical parameters of the reference motor include the measured resonant angular frequency of the reference motor. The measured physical parameters of the target motor include the measured resonant angular frequency of the target motor. The reference physical parameters include a reference resonant angular frequency. Determining the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor includes: Calculate a second ratio between the measured resonant angular frequency of the target motor and the measured resonant angular frequency of the reference motor, and use the second ratio as the mapping coefficient of the second parameter; The step of determining the target denominator coefficient of the target motor model based on the reference physical parameters and the parameter mapping coefficients includes: Calculate the second product between the second parameter mapping coefficient and the reference resonant angular frequency, and determine the target denominator coefficient based on the second product.
5. The method according to claim 2, characterized in that, The parameter mapping coefficients include a third parameter mapping coefficient; the measured physical parameters of the reference motor include the measured damping ratio of the reference motor; the measured physical parameters of the target motor include the measured damping ratio of the target motor; the reference physical parameters include a reference damping ratio; and determining the parameter mapping coefficients based on the measured physical parameters of the reference motor and the target motor includes: Calculate the third ratio between the measured damping ratio of the target motor and the measured damping ratio of the reference motor, and use the third ratio as the mapping coefficient of the third parameter; The step of determining the target denominator coefficient of the target motor model based on the reference physical parameters and the parameter mapping coefficients includes: Calculate the third product between the third parameter mapping coefficient and the reference damping ratio, and determine the target denominator coefficient based on the third product.
6. The method according to any one of claims 1 to 5, characterized in that, After obtaining the target motor model of the target motor based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model, the method further includes: In response to a model update command, the target motor model is updated if preset model update conditions are met; wherein, the model update condition is that the driving voltage of the target motor maintains a constant amplitude within a preset time period.
7. A motor modeling device, characterized in that, The device includes: The first determining module is used to determine the reference motor model of the reference motor based on the system identification of the reference motor. The second determining module is used to determine the reference physical parameters corresponding to the reference motor based on the reference motor model. The acquisition module is used to perform actual measurements on the reference motor and the target motor, and acquire the measured physical parameters of the reference motor and the target motor. The third determining module is used to determine the parameter mapping coefficients based on the measured physical parameters of the reference motor and the measured physical parameters of the target motor. The fourth determining module is used to obtain the target motor model of the target motor based on the parameter mapping coefficients, the reference physical parameters, and the reference motor model.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.