Methods, devices, equipment and media for identifying moment of inertia

Abstract

This application relates to the field of rotational inertia identification technology, and discloses a method, apparatus, device, and medium for rotational inertia identification. The method includes: sending a step excitation signal to a load control system; determining a first mapping relationship based on a received excitation response signal; the excitation response signal is a signal characterizing the motor speed when the load control system controls the motor to drive the load based on the step excitation signal; the first mapping relationship is a mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total rotational inertia of both the motor and the load; and identifying the total rotational inertia of both the motor and the load based on the first mapping relationship. Embodiments of this application can make the identified rotational inertia closely approximate the rotational inertia in actual control.

Description

Technical Field

[0001] This application relates to the field of rotational inertia identification technology, and in particular to a method, apparatus, equipment and medium for rotational inertia identification. Background Technology

[0002] During the movement of the servo motor, the load's moment of inertia fluctuates with the changes in the robotic arm's posture. The control parameters of the speed loop are positively correlated with the actual load moment of inertia. If the correct load moment of inertia is not obtained, the overall control efficiency of the servo motor will be reduced. Therefore, real-time identification of the load moment of inertia is of great significance for precise motor control.

[0003] In related technologies, methods for identifying moment of inertia are mainly divided into offline identification and online identification. Both methods involve discretizing the motor's motion equations and then calculating the moment of inertia based on real-time torque and speed feedback. However, due to various nonlinear interference factors and noise in actual control, the moment of inertia cannot be directly calculated from its physical characteristics under static conditions, causing the calculated moment of inertia to deviate from the moment of inertia in actual control. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment and medium for identifying rotational inertia, which can make the identified rotational inertia close to the rotational inertia in actual control.

[0005] This application provides a method for identifying rotational inertia, including:

[0006] Send a step excitation signal to the load control system;

[0007] Based on the received excitation response signal, a first mapping relationship is determined; the excitation response signal is a signal representing the rotational speed of the motor when the load control system controls the motor to drive the load based on the step excitation signal; the first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of the motor and the load.

[0008] Based on the first mapping relationship, the total moment of inertia of both the motor and the load is identified.

[0009] In some embodiments, determining the first mapping relationship based on the received excitation response signal includes:

[0010] Based on the second mapping relationship, the quadrature-axis current signal corresponding to the excitation response signal is determined; the second mapping relationship is the mapping relationship between the step excitation signal and the quadrature-axis current of the motor.

[0011] When the speed of the motor is within the target speed range, a time-domain step transformation is performed on the transfer function model of the load control system based on the quadrature axis current signal to obtain a time-domain step response model.

[0012] The first mapping relationship is determined based on the excitation response signal and the time-domain step response model.

[0013] In some embodiments, performing a time-domain step transform on the transfer function model of the load control system based on the quadrature-axis current signal includes:

[0014] Perform an inverse Laplace transform on the transfer function model to obtain the time-domain model;

[0015] The quadrature-axis current signal is converted into a step current signal. Based on the step current signal, the time-domain model is decomposed and inversely transformed to obtain the time-domain step response model.

[0016] In some embodiments, determining the first mapping relationship based on the excitation response signal and the time-domain step response model includes:

[0017] The excitation response signal is input into the time-domain step response model to calculate the ratio between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of the motor and the load, thereby obtaining the first mapping relationship.

[0018] In some embodiments, the expression for the transfer function model of the load control system is:

[0019] ,

[0020] ,

[0021] in, This is the transfer function model for the load control system. The excitation response signal in the frequency domain. This is a quadrature-axis current signal. The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. For complex frequency variables, For load factor, The target rotational speed.

[0022] In some embodiments, the expression for the time-domain step response model is:

[0023] ,

[0024] in, The excitation response signal in the time domain, The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. The amplitude of the step current. For time.

[0025] In some embodiments, identifying the total moment of inertia of both the motor and the load based on the first mapping relationship includes:

[0026] Calculate the motor torque constant and equivalent damping coefficient of the motor;

[0027] Based on the first mapping relationship, the total moment of inertia of the motor and the load corresponding to the motor torque constant and the equivalent damping coefficient is determined.

[0028] This application embodiment also provides a rotational inertia identification device, including:

[0029] The first module is used to send step excitation signals to the load control system;

[0030] The second module is used to determine a first mapping relationship based on the received excitation response signal; the excitation response signal is a signal that characterizes the speed of the motor when the load control system controls the motor to drive the load based on the step excitation signal; the first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of the motor and the load.

[0031] The third module is used to identify the total moment of inertia of the motor and the load based on the first mapping relationship.

[0032] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for identifying the moment of inertia.

[0033] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying moment of inertia.

[0034] The beneficial effects of this application are as follows: By injecting a step excitation into the load control system, the load control system drives the motor to enter a dynamic response state. The motor speed signal, motor torque constant, and damping coefficient are determined, and a mapping relationship between these three is established. By solving for the unknown parameters in this mapping relationship, the total moment of inertia of both the motor and the load can be directly calculated. Because system noise and nonlinear disturbances are incorporated into the model parameters during data processing, the identified moment of inertia closely approximates the moment of inertia in actual control, ensuring the engineering applicability of the identification results. Attached Figure Description

[0035] Figure 1 This diagram illustrates the application environment of the rotational inertia identification method provided in the embodiments of this application.

[0036] Figure 2 This is a flowchart of the rotational inertia identification method provided in the embodiments of this application.

[0037] Figure 3 This is a schematic diagram of the rotational inertia identification device provided in the embodiments of this application.

[0038] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0039] 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.

[0040] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application. Furthermore, the information, data, and signals involved in the embodiments of this application are all authorized by relevant parties or have been fully authorized by all parties, and the collection, use, and processing of related data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0042] The rotational inertia identification method provided in this application embodiment can be executed by a host computer. This host computer includes, but is not limited to, mobile phones, computers, smart home appliances, vehicle terminals, and aircraft.

[0043] Figure 1 This diagram illustrates the application environment of the moment of inertia identification method provided in this embodiment. (See also...) Figure 1 This moment of inertia identification method is applied to a moment of inertia identification system. The system includes a load control system 110 and a host computer 120. The load control system 110 and the host computer 120 are connected via a network. The load control system 110 controls the motor to drive the load based on a received step excitation signal, collects the excitation response signal, and feeds it back to the host computer 120. The host computer 120 sends a step excitation signal to the load control system, determines a first mapping relationship based on the received excitation response signal, and identifies the total moment of inertia of both the motor and the load based on the first mapping relationship. The excitation response signal is a signal characterizing the motor speed when the load control system controls the motor to drive the load based on the step excitation signal. The first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of both the motor and the load.

[0044] Figure 2 This is a flowchart of a rotational inertia identification method provided in an embodiment of this application. (See attached document.) Figure 2 In some embodiments, the method includes, but is not limited to, steps S201 to S203.

[0045] Step S201: Send a step excitation signal to the load control system.

[0046] Step S202: Determine the first mapping relationship based on the received excitation response signal.

[0047] Step S203: Based on the first mapping relationship, identify the total moment of inertia of both the motor and the load.

[0048] A step excitation signal is a control signal with abrupt change characteristics. It can be implemented in the form of a current step or a voltage step, and is used to stimulate the dynamic response of a load system.

[0049] The excitation response signal is a signal that characterizes the motor speed when the load control system drives the motor to drive the load based on a step excitation signal. In other words, the excitation response signal refers to the signal indicating how the motor speed changes over time. It is generated by the load control system based on the step excitation signal, driving the motor to rotate, and is acquired by an encoder or Hall sensor, reflecting the dynamic characteristics of the load control system under step excitation.

[0050] The first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of both the motor and the load. It can be understood that the first mapping relationship refers to the proportional relationship between parameters established through a mathematical model. Specifically, it can be derived using inverse transfer function transformation combined with time-domain analysis, and is used to convert measured response data into moment of inertia parameters.

[0051] During the identification of rotational inertia, the host computer injects a step excitation signal into the load control system, causing the load control system to drive the motor and load into a dynamic response state. After acquiring the excitation response signal obtained from the motor's rotational speed through the load control system, the host computer, combined with known parameters such as the motor torque constant and damping coefficient, establishes a proportional relationship model between the motor torque constant, the load's equivalent damping coefficient, and the total rotational inertia of both the motor and the load—that is, determining the first mapping relationship. By solving for the other unknown parameters in the first mapping relationship, the total rotational inertia of both the motor and the load can be directly identified. Because system noise and nonlinear disturbances are incorporated into the model parameters during the identification process, the identified rotational inertia closely approximates the rotational inertia in actual control, ensuring the engineering practicality of the identification results.

[0052] The moment of inertia identification method provided in this application can be applied to identify the total moment of inertia of both the motor and the load in various scenarios, such as identifying the total moment of inertia of the motor and the propeller in a drone. When identifying the total moment of inertia of the motor and the propeller, nonlinear disturbances such as air turbulence and propeller elastic deformation caused by the high-speed flight of the drone are incorporated into the model parameters. Simultaneously, the method avoids situations where battery voltage limitations may prevent the motor's quadrature-axis current from quickly tracking control commands, ensuring that the identified total moment of inertia of the motor and the propeller closely approximates the total moment of inertia of the motor and the propeller in actual control.

[0053] In some embodiments, determining a first mapping relationship based on the received excitation response signal includes: determining the quadrature-axis current signal corresponding to the excitation response signal based on a second mapping relationship; when the motor speed is within the target speed range, performing a time-domain step transformation on the transfer function model of the load control system based on the quadrature-axis current signal to obtain a time-domain step response model; and determining the first mapping relationship based on the excitation response signal and the time-domain step response model.

[0054] The second mapping relationship is the mapping relationship between the step excitation signal and the quadrature-axis current of the motor. The quadrature-axis current signal refers to the current component generated by the motor under the action of the step excitation signal. It can be collected by a current sensor or extracted by a signal processing algorithm, reflecting the dynamic current characteristics of the motor when driving a load. In essence, the second mapping relationship refers to the proportional relationship between parameters established through a mathematical model. Specifically, it can be derived using inverse transfer function transformation combined with time-domain analysis, and is used to convert the step excitation signal into the quadrature-axis current signal of the motor.

[0055] The target speed range refers to the range of speeds that the motor reaches after it has reached a stable state. Specifically, it can be determined by setting a speed threshold or by dynamically monitoring the speed change rate. The speed data within this range can effectively characterize the dynamic response characteristics of the system.

[0056] The time-domain step transform refers to the process of converting the transfer function model from the frequency domain to the time domain. Specifically, it can be achieved using the inverse Laplace transform combined with fractional decomposition. By converting the quadrature-axis current signal into a step current signal, the model complexity can be simplified and the parameter identification efficiency improved. The time-domain step response model refers to the mathematical expression of the speed response of the load control system under a step current input. It can be constructed using time-domain differential equations or discrete difference equations. This model directly relates to key parameters such as the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia.

[0057] During the operation of the motor-driven load, the dynamic response of the load control system is first triggered by a step excitation signal, and the excitation response signal characterizing the motor speed is simultaneously acquired. Based on a pre-established second mapping relationship, the step excitation signal is converted into a corresponding quadrature-axis current signal, which reflects the current dynamic characteristics of the load control system. When the motor speed is detected to enter the target speed range, a time-domain step transformation is performed on the transfer function model of the load control system. Specifically, this involves performing an inverse Laplace transform on the transfer function model to obtain a time-domain differential equation. Subsequently, the quadrature-axis current signal is converted into a step current signal and substituted into the equation. A time-domain step response model is generated through fractional decomposition and inverse transformation operations. Finally, the actual acquired excitation response signal is input into this model, and the proportional relationship between the motor torque constant, equivalent damping coefficient, and total moment of inertia is determined through parameter fitting or algebraic operations, thereby establishing the first mapping relationship.

[0058] Compared to existing technologies, traditional methods for identifying moment of inertia typically rely on static physical parameter calculations or simplified frequency domain analysis, making it difficult to effectively handle nonlinear interference factors such as UAV blade deformation and aerodynamic disturbances. This proposed solution, however, constructs a dynamic response model through a time-domain step transform, directly incorporating actual nonlinear interferences into the model parameter identification process, thus avoiding the dependence on idealized assumptions in traditional methods. Furthermore, by limiting the target rotational speed range for data filtering, it effectively eliminates the interference of invalid data during the stable rotational speed phase on parameter identification, improving the accuracy of moment of inertia measurement.

[0059] In some embodiments, a time-domain step transformation is performed on the transfer function model of the load control system based on the quadrature-axis current signal, including: performing an inverse Laplace transform on the transfer function model to obtain a time-domain model; converting the quadrature-axis current signal into a step current signal; and performing fractional decomposition and inverse transformation on the time-domain model based on the step current signal to obtain a time-domain step response model.

[0060] The inverse Laplace transform is the process of converting a frequency domain transfer function into a time domain differential equation. Specifically, it can be implemented using Laplace transform tables or numerical integration methods to establish a time-domain model.

[0061] A step current signal refers to the abrupt change in signal generated by the quadrature-axis current under step excitation. Specifically, it can be achieved by outputting a constant current value through a current controller to simulate the system input under actual step excitation.

[0062] Fractional decomposition and inverse transformation refer to the process of decomposing a complex time-domain model into simple fractional terms and then performing an inverse transformation. Specifically, it can be achieved by combining partial fractional expansion with the inverse Laplace transform, which simplifies the model solution and separates parameters related to the moment of inertia.

[0063] The transfer function model of the load control system represents the input-output relationship of the load control system in the frequency domain. Through the inverse Laplace transform, it can be converted into a time-domain differential equation, thus directly reflecting the dynamic characteristics of the rotational speed changing with time. The quadrature-axis current signal exhibits a step-like behavior under actual step excitation. By converting it into a step current signal, the time-domain input conditions corresponding to the transfer function model can be constructed. Fractional decomposition of the time-domain model breaks down the higher-order differential equation into a combination of lower-order exponential terms. Then, through inverse transformation, an explicit expression containing the moment of inertia parameter is obtained, forming a time-domain step response model. This model directly correlates the moment of inertia and rotational speed response curves, providing an analytical basis for subsequent parameter identification. Therefore, by modeling the time-domain step response, the moment of inertia parameter is transformed from the frequency domain to the time-domain dynamic equation. Parameter separation is achieved by utilizing the explicit response characteristics under step excitation, avoiding frequency domain fitting errors and improving anti-interference capability.

[0064] In some embodiments, determining a first mapping relationship based on the excitation response signal and the time-domain step response model includes: inputting the excitation response signal into the time-domain step response model, calculating the motor torque constant, the load equivalent damping coefficient, and the ratio between the total moment of inertia of the motor and the load to obtain the first mapping relationship.

[0065] The time-domain step response model is a time-domain mathematical model obtained by transforming the transfer function model of the load control system. Specifically, it can be constructed using the inverse Laplace transform combined with fractional decomposition. This model is used to describe the dynamic relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia.

[0066] In UAV payload systems, when a control signal generated from a step excitation signal is applied to the motor, the excitation response signal is acquired in real time and input into a pre-established time-domain step response model. By matching the actual rotational speed data with the theoretical response in the model, and utilizing the parameter ratios in the model, the motor torque constant, the equivalent load damping coefficient, and the total moment of inertia are solved simultaneously. For example, in the model equations, by separating variables or performing matrix operations, the ratio coefficients among the three are extracted into explicit expressions, thereby directly determining the first mapping relationship. Thus, the moment of inertia can be directly calculated from time-domain data without relying on complex frequency-domain analysis or iterative optimization algorithms. Establishing parameter ratios directly through the time-domain step response model avoids the computational complexity and phase errors caused by frequency-domain conversion, while incorporating noise and nonlinear factors during dynamic operation into the model parameters, improving the robustness of the identification results.

[0067] In a specific embodiment, under FOC control, the motor rotor dynamics equation is:

[0068] ,

[0069] in, For the electromagnetic torque of the motor, For load torque, The total moment of inertia of the motor and the load. This represents the motor speed.

[0070] Perform a Laplace transform on the above motor rotor dynamics equations and substitute them into... and The relevant quantities yield the following equation:

[0071] ,

[0072] in, This is a quadrature-axis current signal. The torque constant of the motor. For load factor, It is a complex frequency variable.

[0073] Linearizing the motor speed by applying signal perturbation within the target speed range yields:

[0074] ,

[0075] ,

[0076] in, For the target speed, The disturbance speed.

[0077] Ignoring higher-order terms, we obtain the following perturbation equation:

[0078] ,

[0079] Based on this, the expression for the transfer function model of the load control system can be obtained as follows:

[0080] ,

[0081] ,

[0082] in, This is the transfer function model for the load control system. The excitation response signal in the frequency domain. It is the quadrature-axis current signal in the frequency domain.

[0083] In a specific embodiment, the inverse Laplace transform of the transfer function model of the above load control system yields the expression for the time-domain model as follows:

[0084] ,

[0085] in, This is the quadrature-axis current signal in the time domain.

[0086] When the motor drives the load, the corresponding control signal is generated based on the step excitation signal. During this operation, the quadrature-axis current signal on the motor is a step current signal in the form of a step current. The expression for the time-domain step response model is obtained by performing fractional decomposition and inverse transformation on the time-domain model:

[0087] ,

[0088] in, The excitation response signal in the time domain, The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. The amplitude of the step current. For time.

[0089] In some embodiments, identifying the total moment of inertia of the motor and the load based on the first mapping relationship includes: calculating the motor torque constant and the equivalent damping coefficient of the motor; and determining the total moment of inertia of the motor and the load corresponding to the motor torque constant and the equivalent damping coefficient based on the first mapping relationship.

[0090] The time-domain step response model is derived by performing an inverse Laplace transform on the frequency-domain transfer function and combining it with the step excitation characteristics of the quadrature-axis current. After the motor speed enters the stable range, the time-domain step response model describes the speed change over time in the form of an exponential function, where the decay rate of the exponential term is determined by the ratio of the total moment of inertia to the equivalent damping coefficient of the load. By fitting the actual acquired excitation response signal with the model prediction curve, the total moment of inertia of both the motor and the load can be separated. The model construction process considers the coupling effect between the motor drive characteristics and the load mechanical characteristics, transforming the complex dynamic response of the electromechanical system into an analytically calculable mathematical expression. Therefore, by establishing a time-domain step response model, the moment of inertia identification process is directly linked to the real-time speed response, which can automatically compensate for nonlinear interference caused by mechanical deformation. Existing technologies using frequency-domain analysis methods require complex data conversion processing; this scheme directly completes parameter identification in the time domain, avoiding problems such as spectral leakage.

[0091] The formula for calculating the motor torque constant is as follows:

[0092] ,

[0093] in, This represents the number of pole pairs of the motor. For motor flux linkage.

[0094] Please see Figure 3 This application also provides a moment of inertia identification device, which can implement the above-described moment of inertia identification method. The device includes:

[0095] The first module 301 is used to send a step excitation signal to the load control system;

[0096] The second module 302 is used to determine a first mapping relationship based on the received excitation response signal; the excitation response signal is a signal that characterizes the speed of the motor when the load control system controls the motor to drive the load based on the step excitation signal; the first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient and the total moment of inertia of the motor and the load.

[0097] The third module 303 is used to identify the total moment of inertia of both the motor and the load based on the first mapping relationship.

[0098] The specific implementation of this moment of inertia identification device is basically the same as the specific implementation of the above-described moment of inertia identification method, and will not be repeated here.

[0099] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0100] The following reference Figure 4To describe an electronic device 400 according to such an embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0101] like Figure 4 As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), a display unit 440, etc.

[0102] The storage unit stores program code, which can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the above section of the method for identifying moment of inertia according to various exemplary embodiments of this disclosure.

[0103] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 4201 and / or cache memory 4202, and may further include a read-only memory (ROM) 4203.

[0104] Storage unit 420 may also include a program / utility 4204 having a set (at least one) program module 4205, such program module 4205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0105] Bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0106] Electronic device 400 can also communicate with one or more external devices 400' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. Network adapter 460 can communicate with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0107] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying moment of inertia.

[0108] The rotational inertia identification method, apparatus, device, and medium provided in this application inject a step excitation into the load control system, causing the load control system to control the motor to drive the load into a dynamic response state. The method determines the motor's speed signal, torque constant, and damping coefficient, and establishes a mapping relationship between these three. By solving for the unknown parameters in this mapping relationship, the total rotational inertia of both the motor and the load can be directly calculated. Because system noise and nonlinear interference are incorporated into the model parameters during data processing, the identified rotational inertia closely approximates the actual rotational inertia under control, ensuring the engineering applicability of the identification results.

[0109] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.

[0110] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0111] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0112] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0113] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for identifying rotational moment of inertia, characterized in that, include: Send a step excitation signal to the load control system; Based on the received excitation response signal, determine the first mapping relationship; The excitation response signal is a signal representing the rotational speed of the motor when the load control system controls the motor to drive the load based on the step excitation signal. The first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of the motor and the load. Based on the first mapping relationship, the total moment of inertia of the motor and the load is identified; Determining the first mapping relationship based on the received excitation response signal includes: Based on the second mapping relationship, the quadrature-axis current signal corresponding to the excitation response signal is determined; the second mapping relationship is the mapping relationship between the step excitation signal and the quadrature-axis current of the motor. When the motor speed is within the target speed range, a time-domain step transformation is performed on the transfer function model of the load control system based on the quadrature axis current signal to obtain a time-domain step response model. Based on the excitation response signal and the time-domain step response model, the first mapping relationship is determined; The expression for the time-domain step response model is: ， in, The excitation response signal in the time domain, The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. The amplitude of the step current. For time.

2. The method for identifying the moment of inertia according to claim 1, characterized in that, The step transformation of the transfer function model of the load control system based on the quadrature-axis current signal in the time domain includes: Perform an inverse Laplace transform on the transfer function model to obtain the time-domain model; The quadrature-axis current signal is converted into a step current signal. Based on the step current signal, the time-domain model is decomposed and inversely transformed to obtain the time-domain step response model.

3. The method for identifying the moment of inertia according to claim 1, characterized in that, Determining the first mapping relationship based on the excitation response signal and the time-domain step response model includes: The excitation response signal is input into the time-domain step response model to calculate the ratio between the total moment of inertia of the motor and the load, the torque constant of the motor and the equivalent damping coefficient of the load, and thus obtain the first mapping relationship.

4. The method for identifying the moment of inertia according to any one of claims 1 to 3, characterized in that, The expression for the transfer function model of the load control system is: ， ， in, This is the transfer function model for the load control system. The excitation response signal in the frequency domain. This is a quadrature-axis current signal. The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. For complex frequency variables, For load factor, The target rotational speed.

5. The method for identifying the moment of inertia according to claim 1, characterized in that, The step of identifying the total moment of inertia of the motor and the load based on the first mapping relationship includes: Calculate the motor torque constant and equivalent damping coefficient of the motor; Based on the first mapping relationship, the total moment of inertia of the motor and the load corresponding to the motor torque constant and the equivalent damping coefficient is determined.

6. A device for identifying the moment of inertia, characterized in that, include: The first module is used to send step excitation signals to the load control system; The second module is used to determine the first mapping relationship based on the received excitation response signal; The excitation response signal is a signal representing the rotational speed of the motor when the load control system controls the motor to drive the load based on the step excitation signal. The first mapping relationship is the mapping relationship between the motor torque constant, the load equivalent damping coefficient, and the total moment of inertia of the motor and the load. The third module is used to identify the total moment of inertia of the motor and the load based on the first mapping relationship; Determining the first mapping relationship based on the received excitation response signal includes: Based on the second mapping relationship, the quadrature-axis current signal corresponding to the excitation response signal is determined; the second mapping relationship is the mapping relationship between the step excitation signal and the quadrature-axis current of the motor. When the motor speed is within the target speed range, a time-domain step transformation is performed on the transfer function model of the load control system based on the quadrature axis current signal to obtain a time-domain step response model. Based on the excitation response signal and the time-domain step response model, the first mapping relationship is determined; The expression for the time-domain step response model is: ， in, The excitation response signal in the time domain, The torque constant of the motor. The total moment of inertia of the motor and the load. This is the equivalent damping coefficient of the load. The amplitude of the step current. For time.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the rotational inertia identification method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the rotational inertia identification method according to any one of claims 1 to 5.

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