WO2024254963A1 - System optimization method based on motor parameter estimation, and motor haptic feedback system - Google Patents

Abstract

The present application relates to the technical field of motor parameter estimation, and discloses a system optimization method based on motor parameter estimation, and a motor haptic feedback system. The method comprises: on the basis of a detected back electromotive force of a motor in a residual vibration stage, calculating the damping motion frequency of the motor; on the basis of an attenuation curve of the back electromotive force and the damping motion frequency, calculating a damping ratio of the motor in the residual vibration stage; on the basis of the damping ratio and the maximum back electromotive force, calculating a magnetic induction intensity coefficient of the motor; and adjusting an excitation signal and a brake signal of the motor on the basis of the magnetic induction intensity coefficient of the motor and the damping ratio of the motor at the residual vibration stage.

Description

System optimization method based on motor parameter estimation and motor tactile feedback system

This application is based on the Chinese patent application with application number 202310700507.7 and application date June 13, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference.

Technical Field

The present application relates to the technical field of motor parameter estimation, for example, to a system optimization method based on motor parameter estimation and a motor tactile feedback system.

Background Art

At present, during the tactile feedback test of the motor, key motor parameters such as magnetic induction coefficient and damping ratio will directly affect the vibration intensity and braking performance of the motor, thus affecting the effect of tactile feedback. To ensure a good and consistent tactile feedback experience between different motors, good and consistent key motor parameters and matching excitation signals are required.

The related technology provides a method for estimating the mass of a motor vibrator. First, an estimated mass value is obtained by estimating the mass of the motor vibrator. Then, the vibrator acceleration value of the motor is calculated based on the estimated mass value, the tooling acceleration and the tooling mass value. Then, the mass verification value of the motor is calculated based on the vibrator acceleration value and the voltage and current at both ends of the motor. Then, it is determined whether the estimated mass value is equal to the mass verification value. If so, the estimated mass value is the actual mass value of the motor vibrator. If not, the estimated mass value is corrected and the calculation and judgment continue.

In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

Due to factors such as manufacturing costs, processes, and aging of the motor during its life cycle, there are differences in the key parameters of different motors. As a result, under a fixed excitation signal, different motors have different vibration intensities and braking performances, leading to inconsistent tactile feedback experience between different motors.

It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field.

Summary of the invention

In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key/critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

The embodiments of the present disclosure provide a system optimization method, a computing device, a motor tactile feedback system and a storage medium based on motor parameter estimation, so that the vibration intensity and braking performance between different motor individuals are maintained within an appropriate range, thereby improving the consistency of the tactile feedback experience.

In some embodiments, the system optimization method based on motor parameter estimation is applied to a motor tactile feedback system, including:

Calculate the damping motion frequency of the motor according to the detected back electromotive force of the motor in the residual vibration stage;

Calculating the damping ratio of the motor in the residual vibration stage according to the attenuation curve of the back electromotive force and the damping motion frequency;

Calculating the magnetic induction coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force;

The excitation signal and brake signal of the motor are adjusted by using the magnetic induction intensity coefficient of the motor and the damping ratio of the motor in the residual vibration stage.

Optionally, the calculating the damping motion frequency of the motor according to the back electromotive force of the motor in the residual vibration stage includes:

After the current excitation signal is played, the reverse electromotive force signal of the motor is detected and the corresponding zero-crossing point information is generated;

The damping motion frequency of the motor is calculated according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information.

Optionally, the calculating the damping motion frequency of the motor damper according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information includes:

calculate Obtain the damping motion frequency f _d of the motor;

Wherein, Cn is the zero-crossing moment of the nth back electromotive force, Cm is the zero-crossing moment of the mth back electromotive force, m and n are positive integers greater than or equal to 1 and not equal, and Fs is the sampling frequency.

Optionally, the calculating the damping ratio of the motor in the residual vibration stage according to the attenuation curve of the back electromotive force and the damping motion frequency includes:

Perform envelope fitting on the attenuation curve of the back electromotive force to obtain the envelope of the back electromotive force;

According to the envelope of the back electromotive force, the damping ratio of the motor in the residual vibration stage is calculated.

Optionally, the calculating the damping ratio of the motor in the residual vibration stage according to the envelope of the back electromotive force includes:

calculate Obtain the damping ratio ζ of the motor in the residual vibration stage;

in,

in,

Where ζ is the damping ratio, _fd is the damping motion frequency, _ωd is the damping angular frequency, P and Q are coefficients determined based on the number of samples K of the envelope of the residual vibration stage, and ε(t) is the back electromotive force envelope at time t _i. The back electromotive force value obtained by sampling, _ωn is the angular frequency of the motor's free oscillation, BLv _Max is the motor's The maximum value of the back electromotive force envelope during the aftershock stage.

Optionally, the calculating of the magnetic induction coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force includes:

calculate Obtain the angular frequency ω _n of the motor's free oscillation;

calculate Obtain the magnetic induction coefficient BL of the motor;

in,

Among them, e is the base of the natural logarithm, M and N are coefficients determined based on the number of samples K of the envelope of the aftershock stage, and ε(t) is the value of the envelope of the back electromotive force at time t _i . The sampled back electromotive force value is BLv _Max is the maximum value of the back electromotive force envelope of the motor in the residual vibration stage, m is the mass of the motor vibrator, U is the amplitude of the sine wave input at both ends of the linear motor, _Re is the DC resistance in the coil path, _ωd is the damping angular frequency, and ζ is the damping ratio.

Optionally, the method further comprises:

calculate The maximum value BLv _Max of the back electromotive force envelope of the motor in the residual vibration stage is obtained.

Optionally, the method further comprises:

According to the damping motion frequency of the motor, the damping angular frequency of the motor in the residual vibration stage is calculated.

Optionally, the calculating the damping angular frequency of the motor in the residual vibration stage according to the damping motion frequency of the motor includes:

Calculate ω _d = 2πf _d to obtain the damping angular frequency ω _d of the motor in the residual vibration stage;

Where _fd is the damped motion frequency.

In some embodiments, the computing device includes a processor and a memory storing program instructions, and the processor is configured to execute the system optimization method based on motor parameter estimation as described in the present application when running the program instructions.

In some embodiments, the motor tactile feedback system includes a computing device and a motor signal calibration device as described in the present application, wherein the motor signal calibration device is used to utilize the magnetic induction intensity coefficient of the motor and the damping ratio of the motor in the residual vibration stage to adjust the excitation signal and the braking signal of the motor to adjust the tactile feedback of the motor in real time.

In some embodiments, the storage medium stores program instructions, and when the program instructions are run, they execute the system optimization method based on motor parameter estimation as described in the present application.

The system optimization method based on motor parameter estimation, computing device, motor tactile feedback system and storage medium provided by the embodiments of the present disclosure can achieve the following technical effects:

The present application calculates the damping motion frequency of the motor according to the reverse electromotive force of the motor in the residual vibration stage, and then calculates the damping ratio of the motor in the residual vibration stage according to the reverse electromotive force and the damping motion frequency. The maximum potential is obtained and the magnetic induction coefficient of the motor is calculated. In this way, the key parameters of the motor can be estimated directly by collecting electrical signals, and the estimated damping motion frequency, damping ratio and magnetic induction coefficient of the current motor can be used to match the corresponding excitation signal and brake signal according to the estimated parameters in the subsequent driving process, so that the vibration intensity and braking performance of different motors are kept within a suitable range, improving the consistency of the tactile feedback experience.

The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

FIG1 is a schematic diagram showing different steady-state acceleration amplitudes caused by individual differences in motors;

FIG2 is another schematic diagram showing different steady-state acceleration amplitudes due to individual differences in motors;

FIG3 is a schematic diagram of a large acceleration amplitude and an unsafe motion stroke caused by individual differences in motors;

FIG4 is a schematic diagram of a motor tactile feedback system provided by an embodiment of the present disclosure;

FIG5 is a schematic diagram of a system optimization method based on motor parameter estimation provided by an embodiment of the present disclosure;

FIG6 is a schematic diagram of another system optimization method based on motor parameter estimation provided by an embodiment of the present disclosure;

FIG7 is a schematic diagram of a zero-crossing point of a reverse electromotive force in a motor residual vibration stage provided by an embodiment of the present disclosure;

FIG8 is a schematic diagram of estimating the damping ratio of a motor vibrator in the residual vibration stage using the back electromotive force envelope provided by an embodiment of the present disclosure;

FIG9 is a schematic diagram of a motor according to an embodiment of the present disclosure that matches an excitation signal based on a magnetic induction coefficient;

FIG. 10 is a schematic diagram of a computing device provided by an embodiment of the present disclosure.

DETAILED DESCRIPTION

In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances to describe the embodiments of the present disclosure herein. In addition, the terms "including" and "having" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. and any variations thereof, are intended to cover a non-exclusive inclusion.

Unless otherwise stated, the term "plurality" means two or more.

In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B indicates: A or B.

The term "and/or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and/or B means: A or B, or, A and B.

The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

In related technologies, tactile feedback is an important means of human-computer interaction and has been widely adopted in various terminals, such as mobile phones, tablet computers, and game controllers. Tactile feedback technology can realistically simulate various natural vibrations, such as mechanical buttons, shooting, motor vibrations, etc.

For the motor control scheme with sensor feedback, it is usually based on the motor test model, and parameters such as the magnetic induction intensity coefficient and the damping ratio are set constant to match the corresponding excitation signal and the brake signal. When the actual key parameters of the motor are not much different from the parameters of the motor test model, a better tactile feedback experience can be provided. However, if the actual key parameters of the motor deviate greatly from the parameters of the motor test model, inconsistent tactile intensity will occur. At the same time, the key parameters of the motor will also change with the changes in the working environment of the motor (temperature, aging, etc.). Furthermore, some motors with more deviations in key motor parameters will be damaged due to excessive movement of the motor vibrator. In order to cope with the situation of inconsistent tactile feedback in the related art, in the process of large-scale use of motors, the voltage threshold of the excitation signal is often reduced to ensure that the motor vibrator has a safe movement range. This also means that in order to "protect" the travel safety of a small number of motor vibrators, the excitation signals of all motors are uniformly reduced.

For motor control solutions with sensor feedback, for example, a Hall sensor is used to detect the motor's motion state in real time. By calculating the motion state, the motor's resonant frequency can be tracked in real time, and the motor can be controlled in the best state in real time. However, the above solution requires the integration of multiple Hall sensors inside the motor, which is relatively expensive. For another example, using voltage-current feedback control to detect voltage-current in real time, it is impossible to obtain parameters related to the motor's mechanical motion in real time.

1 , motor 1 has a normal magnetic flux density coefficient BL, and motor 2 has a larger magnetic flux density coefficient BL than motor 1. When the same excitation signal is input, there is an obvious difference in the acceleration amplitude between motor 1 and motor 2 after steady state.

2, motor 1 has a normal magnetic flux density coefficient BL, and motor 3 has a smaller magnetic flux density coefficient BL than motor 1. When the same excitation signal is input, the acceleration amplitudes of motor 1 and motor 3 after steady state also have obvious differences.

At the same time, as shown in FIG3 , when there are individual differences in the motors, the resulting large acceleration amplitude will cause the motor vibrator to move a larger range of motion, resulting in a shell cracking phenomenon, which deviates from the safe range of motion of the motor vibrator.

The disclosed embodiment provides a motor tactile feedback system, including a computing device and a motor signal calibration device as described in the present application. As shown in FIG4 , when the computing device estimates the motor parameters, the motor is first placed in the tooling, and then the computing device amplifies the matched excitation signal through a class D power amplifier to excite the motor under test. The motor signal calibration device collects voltage, current, acceleration amplitude and reverse electromotive force samples at both ends of the motor after the motor vibrates. The computing device receives the relevant parameters measured by the motor signal calibration device, and estimates the key parameters of the motor to re-match the appropriate excitation signal.

The disclosed embodiment provides a system optimization method based on motor parameter estimation to solve the problem of different or changing key motor parameters caused by individual differences in motors, which in turn causes inconsistent tactile experience and motor stroke safety protection. As shown in FIG5 , the method includes:

Step 501: The computing device calculates the damped motion frequency of the motor according to the detected back electromotive force of the motor in the residual vibration stage.

Step 502: The computing device calculates the damping ratio of the motor in the residual vibration stage according to the attenuation curve of the back electromotive force and the damping motion frequency.

Step 503: The computing device calculates the magnetic flux density coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force.

Step 504: The computing device adjusts the excitation signal and the braking signal of the motor using the magnetic induction intensity coefficient of the motor and the damping ratio of the motor in the residual vibration stage.

In the embodiment of the present application, unlike the real-time control scheme based on feedback, the present application directly uses the collected electrical signals to estimate the motor parameters. That is, the damped motion frequency, magnetic induction intensity coefficient and damping ratio of the motor are estimated through the back electromotive force (BEMF) signal after each excitation signal is played. Therefore, in the subsequent driving process, the estimated parameters are applied to match the corresponding excitation signal and brake signal to obtain a consistent tactile experience and motion travel safety.

By using the system optimization method based on motor parameter estimation provided by the embodiment of the present disclosure, the damped motion frequency of the motor is calculated according to the back electromotive force of the motor in the after-vibration stage, and then the damping ratio of the motor in the after-vibration stage is calculated according to the attenuation curve of the back electromotive force and the damped motion frequency. Then, the magnetic induction intensity coefficient of the motor is calculated based on the damping ratio and the maximum value of the back electromotive force. In this way, it is possible to directly estimate the key parameters of the motor by collecting electrical signals, using the estimated damped motion frequency, damping ratio and magnetic induction intensity coefficient of the current motor. Therefore, in the subsequent driving process, the corresponding excitation signal and braking signal can be matched according to the estimated parameters, so that the vibration intensity and braking performance between different motor individuals are kept within an appropriate range, thereby improving the consistency of the tactile feedback experience.

As shown in FIG6 , the embodiment of the present disclosure provides another system optimization method based on motor parameter estimation, including:

Step 601: After the current excitation signal is played, the computing device detects the back electromotive force signal of the motor and generates corresponding zero-crossing point information.

Step 602: The computing device calculates the damped motion frequency of the motor according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information.

Optionally, in combination with FIG. 7 , the damping motion frequency of the motor damper is calculated according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information, including:

By calculation Obtain the damping motion frequency f _d of the motor;

Among them, Cn is the zero-crossing moment of the nth back electromotive force, Cm is the zero-crossing moment of the mth back electromotive force, m and n are positive integers greater than or equal to 1 and unequal, for example, 1, 2, 3, 4 or 5, and Fs is the sampling frequency.

At the same time, the damping angular frequency ω _d of the motor in the residual vibration stage is obtained by calculating ω _d =2πf _d , where f _d is the damping motion frequency.

Step 603: The computing device performs envelope fitting on the attenuation curve of the back electromotive force to obtain an envelope line of the back electromotive force.

Specifically, as shown in FIG8 , after the excitation signal is played, the back electromotive force envelope of the motor oscillating freely in the residual vibration stage is:

Among them, BLv _Max is the maximum amplitude at the beginning of the aftershock stage, that is, the maximum value of the back electromotive force envelope, ζ is the damping ratio of the vibrator aftershock motion, t is a certain moment in the aftershock stage, ε(t) is the back electromotive force value sampled at time t, e is the base of the natural logarithm, and _ωn is the angular frequency of the motor's free oscillation.

Step 604: The computing device calculates the damping ratio of the motor in the residual vibration stage according to the envelope of the back electromotive force.

Specifically, taking the natural logarithm of the motor back electromotive force envelope formula in step 603, we obtain:
Ln(ε(t))=Ln(BLv(t))=Ln(BLv _Max )-ω _n ζt

Further, the parameter x ₁ is assigned to Ln(BLv _Max ), that is, x ₁ =Ln(BLv _Max ), and the parameter x ₂ is assigned to ω _n ζ, that is, x ₂ =ω _n ζ;

Furthermore, the cost (loss) function E is constructed using the least squares method:

in,

Furthermore, partial derivatives of parameters x ₁ and x ₂ are calculated and the results are set to 0, and the parameter estimation equation is obtained:

Furthermore, the parameter estimation equation is converted into a general matrix form:
y＝Fx+w

Among them, the parameter vector to be estimated Observation vector Observation time vector Measurement error vector

Furthermore, according to the optimal solution formula of the least squares method x = (F ^T F) ^-1 F ^T y, the optimal solution vector is calculated The parameter expressions of parameters x1 and x2 are obtained as follows:

in,

Among them, M and N are coefficients determined based on the sampling number K of the envelope line in the aftershock stage, P and Q are coefficients determined based on the sampling number K of the envelope line in the aftershock stage, _fd is the damping motion frequency, and ε(t) is the back electromotive force value sampled at t= _ti .

Finally, by calculating Obtain the damping ratio ζ of the motor in the aftershock stage.

At the same time, since the back electromotive force envelope in the residual vibration stage obeys exponential decay, the starting point of decay is the maximum value of the back electromotive force envelope BLv _max, and then by calculating The maximum value BLv _Max of the back electromotive force envelope of the motor in the residual vibration stage can be obtained.

Wherein, BL is the magnetic induction coefficient, v _Max is the maximum speed of the motor vibrator in the driving stage, and e is the base of the natural logarithm.

Step 605: The computing device calculates the magnetic flux density coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force envelope.

Step 606: The computing device adjusts the excitation signal and the braking signal of the motor using the magnetic flux density coefficient of the motor and the damping ratio of the motor in the residual vibration stage.

Optionally, in the driving stage of the motor, the motor vibrator motion speed and voltage transfer function is:

Let s = jω, and the parameter expression of the maximum speed of the motor oscillator is obtained from the speed frequency response formula:

Where BL is the magnetic induction coefficient, m is the mass of the motor vibrator, U is the amplitude of the sine wave input at both ends of the linear motor, _Re is the DC resistance in the coil path, _ωn is the angular frequency of the motor's free oscillation, and ζ is the damping ratio.

Further, in combination with the parameter expression of the maximum value of the back electromotive force envelope in step 604, It can be deduced that:

After sorting out the above formula, we can get the parameter expression of the magnetic induction intensity coefficient:

Among them, by calculating Obtain the angular frequency ω _n of the motor's free oscillation;

Among them, BLv _Max is the maximum value of the back electromotive force envelope of the motor in the residual vibration stage, m is the mass of the motor vibrator, U is the amplitude of the sinusoidal wave input at both ends of the linear motor, _Re is the DC resistance in the coil path, _ωd is the damping angular frequency, and ζ is the damping ratio.

In this way, the present application can perform data collection directly on the motor signal calibration equipment. There are no special requirements for the basic conditions of the external mechanism and no additional physical configuration is required. While obtaining the key parameters of the motor, the testing cost is reduced.

At the same time, the present application can timely adjust and match the excitation signal and brake signal of the current motor according to the parameters such as the magnetic induction intensity coefficient and damping ratio of the motor obtained each time, so as to obtain a consistent tactile experience. As shown in FIG9 , after matching the appropriate excitation signal (using a smaller excitation signal voltage), the motor 2 can obtain a relatively consistent tactile feedback vibration intensity with the motor 1 under the condition of normal magnetic induction intensity coefficient.

As shown in FIG10 , an embodiment of the present disclosure provides a computing device, including a processor 100 and a memory 101. Optionally, the computing device may also include a communication interface 102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other through the bus 103. The communication interface 102 may be used for information transmission. The processor 100 may call the logic instructions in the memory 101 to execute the system optimization method based on motor parameter estimation of the above embodiment.

In addition, the logic instructions in the memory 101 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

The memory 101 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions/modules corresponding to the method in the embodiment of the present disclosure. The processor 100 executes functional applications and data processing by running the program instructions/modules stored in the memory 101, that is, implementing the system optimization method based on motor parameter estimation in the above embodiment.

The memory 101 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 101 may include a high-speed random access memory and may also include a non-volatile memory.

An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned system optimization method based on motor parameter estimation.

An embodiment of the present disclosure provides a computer program, which, when executed by a computer, enables the computer to implement the above-mentioned system optimization method based on motor parameter estimation.

An embodiment of the present disclosure provides a computer program product, which includes computer instructions stored on a computer-readable storage medium. When the program instructions are executed by a computer, the computer implements the above-mentioned system optimization method based on motor parameter estimation.

The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solution of the embodiment of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

The above description and the accompanying drawings sufficiently illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless expressly required, individual components and functions are optional, and the order of operations may vary. Portions and features of some embodiments may be included in or replace portions and features of other embodiments. Moreover, the terms used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application means including one or one Any and all possible combinations listed above are associated. In addition, when used in the present application, the term "comprise" and its variants "comprises" and/or comprising refer to the existence of stated features, wholes, steps, operations, elements, and/or components, but do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and/or groups of these. In the absence of further restrictions, the elements defined by the statement "including one..." do not exclude the existence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts may refer to the description of the method part.

Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, which They may sometimes be performed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, and they may sometimes be performed in reverse order, depending on the functions involved. Each block in the block diagram and/or flowchart, and the combination of blocks in the block diagram and/or flowchart, may be implemented with a dedicated hardware-based system that performs the specified functions or actions, or may be implemented with a combination of dedicated hardware and computer instructions.

Claims (12)

A system optimization method based on motor parameter estimation, characterized in that it is applied to a motor tactile feedback system, comprising: Calculate the damping motion frequency of the motor according to the detected back electromotive force of the motor in the residual vibration stage; Calculating the damping ratio of the motor in the residual vibration stage according to the attenuation curve of the back electromotive force and the damping motion frequency; Calculating the magnetic induction coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force; The excitation signal and brake signal of the motor are adjusted by using the magnetic induction intensity coefficient of the motor and the damping ratio of the motor in the residual vibration stage. The system optimization method according to claim 1 is characterized in that the step of calculating the damping motion frequency of the motor according to the back electromotive force of the motor in the residual vibration stage comprises: After the current excitation signal is played, the reverse electromotive force signal of the motor is detected and the corresponding zero-crossing point information is generated; The damping motion frequency of the motor is calculated according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information. The system optimization method according to claim 1 or 2, characterized in that the calculating the damping motion frequency of the motor damper according to the zero-crossing point index and the zero-crossing point time of the back electromotive force in the zero-crossing point information comprises: calculate Obtain the damping motion frequency f _d of the motor; Wherein, Cn is the zero-crossing moment of the nth back electromotive force, Cm is the zero-crossing moment of the mth back electromotive force, m and n are positive integers greater than or equal to 1 and not equal, and Fs is the sampling frequency. The system optimization method according to any one of claims 1 to 3, characterized in that the step of calculating the damping ratio of the motor in the residual vibration stage according to the attenuation curve of the back electromotive force and the damping motion frequency comprises: Perform envelope fitting on the attenuation curve of the back electromotive force to obtain the envelope of the back electromotive force; According to the envelope of the back electromotive force, the damping ratio of the motor in the residual vibration stage is calculated. The system optimization method according to claim 4 is characterized in that the step of calculating the damping ratio of the motor in the residual vibration stage according to the envelope of the back electromotive force comprises: calculate Obtain the damping ratio ζ of the motor in the residual vibration stage; in, in, Where ζ is the damping ratio, _fd is the damping motion frequency, _ωd is the damping angular frequency, P and Q are coefficients determined based on the number of samples K of the envelope of the residual vibration stage, and ε(t) is the back electromotive force envelope at time t _i. The sampled back electromotive force value, _ωn is the angular frequency of the motor's free oscillation, and BLv _Max is the maximum value of the motor's back electromotive force envelope in the residual vibration stage. The system optimization method according to any one of claims 1 to 5, characterized in that the calculating the magnetic induction coefficient of the motor according to the damping ratio and the maximum value of the back electromotive force comprises: calculate Obtain the angular frequency ω _n of the motor's free oscillation; calculate Obtain the magnetic induction coefficient BL of the motor; in, Among them, e is the base of the natural logarithm, M and N are coefficients determined based on the number of samples K of the envelope of the aftershock stage, and ε(t) is the value of the envelope of the back electromotive force at time t _i . The sampled back electromotive force value is BLv _Max is the maximum value of the back electromotive force envelope of the motor in the residual vibration stage, m is the mass of the motor vibrator, U is the amplitude of the sine wave input at both ends of the linear motor, _Re is the DC resistance in the coil path, _ωd is the damping angular frequency, and ζ is the damping ratio. The system optimization method according to claim 6, characterized in that it also includes: calculate The maximum value BLv _Max of the back electromotive force envelope of the motor in the residual vibration stage is obtained. A computing device comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the system optimization method based on motor parameter estimation as described in any one of claims 1 to 7 when running the program instructions. A motor tactile feedback system, characterized in that it includes a computing device as described in claim 8 and a motor signal calibration device, wherein the motor signal calibration device is used to use the magnetic induction intensity coefficient of the motor and the damping ratio of the motor in the residual vibration stage to adjust the excitation signal and braking signal of the motor to adjust the tactile feedback of the motor in real time. A storage medium storing program instructions, characterized in that when the program instructions are run, the system optimization method based on motor parameter estimation as described in any one of claims 1 to 7 is executed. A computer program, when executed by a computer, enables the computer to implement the system optimization method based on motor parameter estimation as described in any one of claims 1 to 7. A computer program product, the computer program product comprising computer instructions stored on a computer readable storage medium, when the program instructions are executed by a computer, the computer implements the A system optimization method based on motor parameter estimation as described in any one of items 1 to 7.