WO2020140551A1 - 马达系统辨识方法 - Google Patents

马达系统辨识方法 Download PDF

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WO2020140551A1
WO2020140551A1 PCT/CN2019/111096 CN2019111096W WO2020140551A1 WO 2020140551 A1 WO2020140551 A1 WO 2020140551A1 CN 2019111096 W CN2019111096 W CN 2019111096W WO 2020140551 A1 WO2020140551 A1 WO 2020140551A1
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motor system
impulse response
formula
response sequence
signal
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French (fr)
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郭璇
向征
李涛
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AAC Technologies Holdings Shenzhen Co Ltd
AAC Technologies Pte Ltd
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AAC Acoustic Technologies Shenzhen Co Ltd
AAC Technologies Pte Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D19/00—Control of mechanical oscillations, e.g. of amplitude, of frequency, of phase
    • G05D19/02—Control of mechanical oscillations, e.g. of amplitude, of frequency, of phase characterised by the use of electric means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/016—Input arrangements with force or tactile feedback as computer generated output to the user
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B06—GENERATING OR TRANSMITTING MECHANICAL VIBRATIONS IN GENERAL
    • B06B—METHODS OR APPARATUS FOR GENERATING OR TRANSMITTING MECHANICAL VIBRATIONS OF INFRASONIC, SONIC, OR ULTRASONIC FREQUENCY, e.g. FOR PERFORMING MECHANICAL WORK IN GENERAL
    • B06B1/00—Methods or apparatus for generating mechanical vibrations of infrasonic, sonic, or ultrasonic frequency
    • B06B1/02—Methods or apparatus for generating mechanical vibrations of infrasonic, sonic, or ultrasonic frequency making use of electrical energy
    • B06B1/04—Methods or apparatus for generating mechanical vibrations of infrasonic, sonic, or ultrasonic frequency making use of electrical energy operating with electromagnetism
    • B06B1/045—Methods or apparatus for generating mechanical vibrations of infrasonic, sonic, or ultrasonic frequency making use of electrical energy operating with electromagnetism using vibrating magnet, armature or coil system
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions

Definitions

  • the invention relates to the field of signal processing, in particular to a motor system identification method using a one-dimensional Volterra filter.
  • Tactile actuators that use motors as the core and electronic devices (such as mobile phones, tablet computers, and car central controls) as carriers can achieve a customized tactile experience by designing their specific waveforms. For example, combining different scenes in the game can equip different sound sources with different vibrations, so that people can more directly and quickly identify the target in the game, and at the same time get a higher sense of immersion; on electronic devices such as mobile phones, you can use the virtual buttons to configure the height Customize the vibration waveform to get the special feeling of physical buttons. In order to design the customized waveform of the motor, motor system identification is essential.
  • the identification of the motor system can be understood as clarifying the processing process of the input signal by the motor system, that is, given an input voltage signal, after the identification process, the output vibration waveform of the motor can be known.
  • motor system identification mostly relies on physical models, including components such as circuit structure and magnetic drive.
  • the analysis is more tedious, and errors in the recognition of the physical model will lead to errors in system identification; on the other hand, the nonlinear components of the motor are difficult to get an accurate description.
  • the technical problem to be solved by the present invention is to provide a simple and easy-to-recognize motor system identification method.
  • the present invention provides a motor system identification method, which includes the following steps:
  • Step S1 a functional series filter model for describing the motor system is established, the excitation voltage x(n) is used as the input of the functional series filter model, and the vibration acceleration y(n) is used as the functional
  • the output of the series filter model; y(n) satisfies the following identification formula (1):
  • h p is the kernel function of the functional series filter model
  • M p is the filter length of the p-th order kernel function
  • i represents the point coordinate of the discrete domain kernel function
  • i is the value range from 0 to M p-
  • a natural number of 1 n is a positive integer
  • p is a positive integer
  • x p (ni) represents the p-th power of the x sequence of the ni-th point coordinate
  • Step S2 Generate a logarithmic sweep signal and make the excitation voltage x(n) of the motor system, where x(n) satisfies formula (2):
  • A is the amplitude
  • ⁇ 1 and ⁇ 2 are the starting angular frequency and the ending angular frequency of the sweep signal
  • T is the signal duration
  • N is the total number of sampling points
  • n is a positive integer
  • ⁇ ...,-1,0,1,2,...;
  • Step S3. Feed the logarithmic frequency sweep signal generated in step S2 to the motor system, and use an accelerometer to collect the vibration acceleration y(n) output by the motor system;
  • Step S4 Generate an inverse signal of the logarithmic sweep signal Satisfy formula (4):
  • Step S5 the vibration acceleration y(n) collected in step S3 and the inverse signal in step S4 Convolution to get one-dimensional impulse response sequence k(n);
  • Step S6 Use the window function to intercept each part of the one-dimensional impulse response sequence k(n) obtained in step S5 by using a window function k 1 (n)-k p (n):
  • u is the unit step function and ⁇ p0 is a fixed constant, indicating the delay amount of the p-th impulse response, calculated as follows:
  • Step S7 Solve the kernel functions h 1 ⁇ h p according to each partial impulse response sequence:
  • the one-dimensional impulse response sequence k(n) is composed of a series of delayed impulse response sequences.
  • the excitation voltage x(n) is used as the input of the functional series filter model, Use the vibration acceleration y(n) as the output of the functional series filter model; then generate a logarithmic sweep signal and use it as the excitation voltage of the motor system x(n); then generate the logarithmic sweep frequency
  • the signal is fed to the motor system, and the acceleration acceleration y(n) output by the motor system is collected by an accelerometer; and then the inverse signal of the logarithmic sweep signal is generated
  • the collected vibration acceleration y(n) and inverse signal Convolution to obtain a one-dimensional impulse response sequence k (n); then use the window function to intercept each part of the impulse response sequence k 1 (n) ⁇ k p (n)
  • the kernel functions h 1 to h p are solved according to the impulse response sequence of each part, and the solved kernel functions are substituted into the identification formula
  • FIG. 1 is a schematic structural diagram of a motor system identification device of a motor system identification method of the present invention
  • FIG. 3 is a schematic diagram of a one-dimensional impulse response sequence of the motor system identification method of the present invention.
  • FIG. 4 is a schematic diagram of the first-order kernel function h1 of the motor system identification method of the present invention.
  • FIG. 5 is a schematic diagram of the second-order kernel function h2 of the motor system identification method of the present invention.
  • FIG. 6 is a schematic diagram of the third-order kernel function h3 of the motor system identification method of the present invention.
  • FIG. 7 is a schematic diagram of the fourth-order kernel function h4 of the motor system identification method of the present invention.
  • FIG. 8 is a schematic diagram of the fifth-order kernel function h5 of the motor system identification method of the present invention.
  • FIG. 9 is a comparison diagram of the acceleration obtained by the motor system identification method of the present invention and the measured acceleration in an embodiment.
  • the present invention provides a motor system identification method.
  • the method includes a signal generator 1, a motor system 2 and an accelerometer 3.
  • the signal generator 1 inputs the excitation voltage x(n) to the motor system 2.
  • the motor system 2 is used for identifying the motor system.
  • the motor system 2 is regarded as a black box.
  • the input signal is the excitation voltage x(n), and the output signal is the vibration acceleration y(n).
  • the accelerometer 3 is used to collect the vibration acceleration y(n) output by the motor system 2.
  • the motor system identification method includes the following steps:
  • Step S1 a functional series filter model for describing the motor system is established, the excitation voltage x(n) is used as the input of the functional series filter model, and the vibration acceleration y(n) is used as the functional
  • the output of the series filter model; y(n) satisfies the following identification formula (1):
  • h p is the kernel function of the functional series filter model
  • M p is the filter length of the p-th order kernel function
  • i represents the point coordinate of the discrete domain kernel function
  • i is the value range from 0 to M p-
  • a natural number of 1 n is a positive integer
  • p is a positive integer
  • x p (ni) represents the p-th power of the x sequence of the ni-th point coordinate
  • Step S2 Generate a logarithmic sweep signal and make the excitation voltage x(n) of the motor system, where x(n) satisfies formula (2):
  • A is the amplitude
  • ⁇ 1 and ⁇ 2 are the starting angular frequency and the ending angular frequency of the sweep signal
  • T is the signal duration
  • N is the total number of sampling points
  • n is a positive integer
  • ⁇ ...,-1,0,1,2,...
  • Step S3. Feed the logarithmic sweep signal generated in step S2 to the motor system, and use an accelerometer to collect the vibration acceleration y(n) output by the motor system.
  • Step S4 Generate an inverse signal of the logarithmic sweep signal, Satisfy formula (4)
  • Step S5 the vibration acceleration y(n) collected in step S3 and the inverse signal in step S4 Convolution to obtain a one-dimensional impulse response sequence k(n); in this step, the one-dimensional impulse response sequence k(n) consists of a series of delayed impulse response sequences.
  • Step S6 Use the window function to intercept each part of the one-dimensional impulse response sequence k(n) obtained in step S5 by using a window function k 1 (n)-k p (n):
  • u is the unit step function and ⁇ p0 is a fixed constant, indicating the delay amount of the p-th impulse response, calculated as follows:
  • Step S7 Solve the kernel functions h 1 ⁇ h p according to each partial impulse response sequence:
  • the motor system is identified by the above-mentioned motor system identification method, does not depend on the physical model, and contains the nonlinear component of the motor, which can effectively estimate the motor output vibration waveform, thereby making the motor system identification method simple and reliable sexuality is high.
  • the identification obtains the kernel function.
  • the identification is performed by the fifth-order kernel function.
  • the input signal x(n) is identified by the motor system, and the estimated output vibration acceleration y'(n) is compared with the measured vibration acceleration y(n).
  • curve A is the measured vibration acceleration value
  • curve B is the estimated vibration acceleration value
  • curve C is the error value.
  • the error between the estimated acceleration and the measured acceleration is 2.69%, and the error is small, indicating that the method of the present invention is effective and accurate.
  • the motor system identification method identifies the motor system and can effectively estimate the motor output vibration waveform.
  • the excitation voltage x(n) is used as the input of the functional series filter model, Use the vibration acceleration y(n) as the output of the functional series filter model; then generate a logarithmic sweep signal and use it as the excitation voltage of the motor system x(n); then generate the logarithmic sweep frequency
  • the signal is fed to the motor system, and the acceleration acceleration y(n) output by the motor system is collected by an accelerometer; and then the inverse signal of the logarithmic sweep signal is generated
  • the collected vibration acceleration y(n) and inverse signal Convolution to obtain a one-dimensional impulse response sequence k (n); then use the window function to intercept each part of the impulse response sequence k 1 (n) ⁇ k p (n)
  • the kernel functions h 1 to h p are solved according to the impulse response sequence of each part, and the solved kernel functions are substituted into the identification formula

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

一种马达系统（2）辨识方法，包括步骤：建立用于描述马达系统（2）的泛函级数滤波器模型（S1）；生成对数扫频信号（S2）；将生成的对数扫频信号馈给马达系统（2），并用加速度计（3）采集马达系统（2）输出的振动加速度（S3）；生成对数扫频信号的逆信号（S4）；将采集到的振动加速度与逆信号卷积，得到一维脉冲响应序列（S5）；对一维脉冲响应序列使用窗函数截取各部分脉冲响应序列（S6）；根据各部分脉冲响应序列求解核函数，将求解出的各部分核函数代入马达系统（2）的辨识公式，并通过辨识公式描述和辨识马达系统（2）（S7）。方法简单易行且可靠性高。

Description

马达系统辨识方法 技术领域
本发明涉及信号处理领域，尤其涉及一种运用一维沃尔特拉滤波器的马达系统辨识方法。
背景技术
科技日益发展的今天，视听等感官已难以满足人们的需求，触觉反馈作为一种直接感受进入大众视野。以马达为核心，以电子设备(如手机、平板电脑、汽车中控)为载体的触觉致动器，通过设计其特定波形，可以获得定制化的触觉体验。例如，游戏中结合场景可以为不同的发声源配备不同的振动，使人们在游戏中目标辨识更加直接快捷，同时获得更高的沉浸感；又如手机等电子设备上，可以通过虚拟按键配备高度定制化振动波形以获得实体按键的特殊感受。而要想进行马达定制化波形的设计，马达系统辨识必不可少。
该马达系统辨识，可以理解为明确马达系统对输入信号的处理过程，即给定一种输入电压信号，经过辨识所得处理过程，则可得知马达的输出振动波形。
相关技术中马达系统辨识大多依赖物理模型，包括电路结构和磁力驱动等组成部分。一方面分析较为繁琐，对物理模型认知的误差会导致系统辨识的误差；另一方面马达的非线性成分难以得到准确描述。
因此，有必要提供一种新的马达系统辨识方法解决上述问题。
发明内容
本发明需要解决的技术问题是提供一种简单易行且可靠性高的马达系统辨识方法。为解决上述技术问题，本发明提供一种马达系统 辨识方法，该方法包括如下步骤:
步骤S1、建立用于描述马达系统的泛函级数滤波器模型，以激励电压x(n)作为所述泛函级数滤波器模型的输入，以振动加速度y(n)作为所述泛函级数滤波器模型的输出；y(n)满足如下辨识公式(1)：
Figure PCTCN2019111096-appb-000001
其中，h p是泛函级数滤波器模型的核函数，M p是第p阶核函数的滤波器长度，i表示离散域核函数的点坐标，i为取值范围为0～M p-1的自然数，n为正整数，p为正整数，x p(n-i)表示第n-i点坐标的x序列的p次方；
步骤S2、生成对数扫频信号并作所述马达系统的激励电压x(n)，x(n)满足公式(2)：
Figure PCTCN2019111096-appb-000002
其中，A为幅度，ω 1和ω 2分别为该扫频信号的起始角频率和终止角频率，T为信号时长，N为总采样点数，n为正整数，
同时，参数满足公式(3)：
Figure PCTCN2019111096-appb-000003
其中，η＝…,-1,0,1,2,…；
步骤S3、将步骤S2生成的对数扫频信号馈给所述马达系统，并用加速度计采集所述马达系统输出的振动加速度y(n)；
步骤S4、生成所述对数扫频信号的逆信号
Figure PCTCN2019111096-appb-000004
满足公式(4)：
Figure PCTCN2019111096-appb-000005
步骤S5、将步骤S3中采集到的所述振动加速度y(n)与步骤S4中的逆信号
Figure PCTCN2019111096-appb-000006
卷积，得到一维脉冲响应序列k(n)；
步骤S6、对步骤S5中所得到的所述一维脉冲响应序列k(n)使用窗函数截取各部分脉冲响应序列k 1(n)～k p(n)：
Figure PCTCN2019111096-appb-000007
其中，u为单位阶跃函数，γ p0为固定常数，表示第p个脉冲响应的延时量，计算如下：
Figure PCTCN2019111096-appb-000008
步骤S7、根据各部分脉冲响应序列求解核函数h 1～h p：
Figure PCTCN2019111096-appb-000009
将求解出的各部分核函数代入所述马达系统的辨识公式，并通过 所述辨识公式描述和辨识所述马达系统。
优选的，在所述步骤S5中，所述一维脉冲响应序列k(n)由一系列延时的脉冲响应序列组成。
与相关技术相比，本发明的马达系统辨识方法中通过建立用于描述马达系统的泛函级数滤波器模型，以激励电压x(n)作为所述泛函级数滤波器模型的输入，以振动加速度y(n)作为所述泛函级数滤波器模型的输出；再通过生成对数扫频信号并作所述马达系统的激励电压x(n)；再将生成的对数扫频信号馈给所述马达系统，并用加速度计采集所述马达系统输出的振动加速度y(n)；再通过生成所述对数扫频信号的逆信号
Figure PCTCN2019111096-appb-000010
再将采集到的所述振动加速度y(n)与逆信号
Figure PCTCN2019111096-appb-000011
卷积，得到一维脉冲响应序列k(n)；再通过所得到的所述一维脉冲响应序列k(n)使用窗函数截取各部分脉冲响应序列k 1(n)～k p(n)；再根据各部分脉冲响应序列求解核函数h 1～h p，将求解出的各部分核函数代入所述马达系统的辨识公式，并通过所述辨识公式描述和辨识所述马达系统。通过上述马达系统辨识方法对所述马达系统进行辨识，不依赖物理模型，并包含马达的非线性成分，能够对马达输出振动波形进行有效估计，从而使所述马达系统辨识方法简单易行且可靠性高。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图，其中：
图1为本发明马达系统辨识方法的马达系统辨识装置结构示意图；
图2为本发明马达系统辨识方法的流程图；
图3为本发明马达系统辨识方法的一维脉冲响应序列的示意图；
图4为本发明马达系统辨识方法的一阶核函数h1的示意图；
图5为本发明马达系统辨识方法的二阶核函数h2的示意图；
图6为本发明马达系统辨识方法的三阶核函数h3的示意图；
图7为本发明马达系统辨识方法的四阶核函数h4的示意图；
图8为本发明马达系统辨识方法的五阶核函数h5的示意图；
图9为实施例通过本发明马达系统辨识方法得出的加速度与实测得出的加速度对比图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
请参阅图1，本发明提供一种马达系统辨识方法，该方法包括信号发生器1、马达系统2以及加速度计3。
所述信号发生器1向所述马达系统2输入激励电压x(n)。
所述马达系统2用于辨识的马达系统。在本实施方式中，所述马达系统2视为黑盒。输入信号为所述激励电压x(n)，输出信号为振动加速度y(n)。
所述加速度计3用于采集所述马达系统2输出的所述振动加速度y(n)。
请参阅图2，所述马达系统辨识方法包括如下步骤:
步骤S1、建立用于描述马达系统的泛函级数滤波器模型，以激励电压x(n)作为所述泛函级数滤波器模型的输入，以振动加速度y(n)作为所述泛函级数滤波器模型的输出；y(n)满足如下辨识公式(1)：
Figure PCTCN2019111096-appb-000012
其中，h p是泛函级数滤波器模型的核函数，M p是第p阶核函数的滤波器长度，i表示离散域核函数的点坐标，i为取值范围为0～M p-1的自然数，n为正整数，p为正整数，x p(n-i)表示第n-i点坐标的x序列的p次方；
步骤S2、生成对数扫频信号并作所述马达系统的激励电压x(n)，x(n)满足公式(2)：
Figure PCTCN2019111096-appb-000013
其中，A为幅度，ω 1和ω 2分别为该扫频信号的起始角频率和终止角频率，T为信号时长，N为总采样点数，n为正整数，
同时，参数满足公式(3)：
Figure PCTCN2019111096-appb-000014
其中，η＝…,-1,0,1,2,…。
步骤S3、将步骤S2生成的对数扫频信号馈给所述马达系统，并用加速度计采集所述马达系统输出的振动加速度y(n)。
步骤S4、生成所述对数扫频信号的逆信号，
Figure PCTCN2019111096-appb-000015
满足公式(4)
Figure PCTCN2019111096-appb-000016
Figure PCTCN2019111096-appb-000017
步骤S5、将步骤S3中采集到的所述振动加速度y(n)与步骤S4中的逆信号
Figure PCTCN2019111096-appb-000018
卷积，得到一维脉冲响应序列k(n)；在本步骤中，所述一维脉冲响应序列k(n)由一系列延时的脉冲响应序列组成。
步骤S6、对步骤S5中所得到的所述一维脉冲响应序列k(n)使用窗函数截取各部分脉冲响应序列k 1(n)～k p(n)：
Figure PCTCN2019111096-appb-000019
其中，u为单位阶跃函数，γ p0为固定常数，表示第p个脉冲响应的延时量，计算如下：
Figure PCTCN2019111096-appb-000020
步骤S7、根据各部分脉冲响应序列求解核函数h 1～h p：
Figure PCTCN2019111096-appb-000021
将求解出的各部分核函数代入所述马达系统的辨识公式，并通过所述辨识公式描述和辨识所述马达系统。
通过上述马达系统辨识方法对所述马达系统进行辨识，不依赖物理模型，并包含马达的非线性成分，能够对马达输出振动波形进行有效估计，从而使所述马达系统辨识方法简单易行且可靠性高。
为了验证所述马达系统辨识方法的实际效果，通过对一种马达实测振动加速度，并基于所述泛函级数滤波器模型进行系统辨识，该马达的参数如下表1所示：
符号 物理意义及单位 数值
f1 扫频信号起始频率，Hz 20
f2 扫频信号结束频率，Hz 4×10 3
T 扫频信号持续时间，s 9.3383
A 扫频信号幅度，V 2
fs 采样率，Hz 8×10 3
M 核函数长度 2×10 3
zero_pos_offset 零时刻偏移量 300
表1马达的参数表
请同时参阅图4-8，通过对该马达进行所述马达系统辨识方法进行辨识，辨识得到所述核函数，本验证的辨识中，通过五阶所述核函数进行辨识。
请参阅图9，将输入信号x(n)通过辨识所得马达系统，其估计输出振动加速度y’(n)与实测所得振动加速度y(n)进行对比。其中，曲线A为实测振动加速度值，曲线B为估计振动加速度值，曲线C为误差值，所得估计加速度与实测加速度误差为2.69％，误差较小，说明本发明方法系统辨识有效且准确。
通过上述验证试验可以得出结论：所述马达系统辨识方法对所述马达系统进行辨识，能够对马达输出振动波形进行有效估计。
与相关技术相比，本发明的马达系统辨识方法中通过建立用于描述马达系统的泛函级数滤波器模型，以激励电压x(n)作为所述泛函级数滤波器模型的输入，以振动加速度y(n)作为所述泛函级数滤波器模型的输出；再通过生成对数扫频信号并作所述马达系统的激励电压x(n)；再将生成的对数扫频信号馈给所述马达系统，并用加速度计采集所述马达系统输出的振动加速度y(n)；再通过生成所述对数扫频信号的逆信号
Figure PCTCN2019111096-appb-000022
再将采集到的所述振动加速度y(n)与逆信号
Figure PCTCN2019111096-appb-000023
卷积，得到一维脉冲响应序列k(n)；再通过所得到的所述一维脉冲响应序列k(n)使用窗函数截取各部分脉冲响应序列k 1(n)～k p(n)；再根据各部分脉冲响应序列求解核函数h 1～h p，将求解出的各部分核函数代入所述马达系统的辨识公式，并通过所述辨识公式描述和辨识所述马达系统。通过上述马达系统辨识方法对所述马达系统进行辨识，不依赖物理模型，并包含马达的非线性成分，能够对马 达输出振动波形进行有效估计，从而使所述马达系统辨识方法简单易行且可靠性高。
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其它相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (2)

  1. 一种马达系统辨识方法，其特征在于，该方法包括如下步骤:
    步骤S1、建立用于描述马达系统的泛函级数滤波器模型，以激励电压x(n)作为所述泛函级数滤波器模型的输入，以振动加速度y(n)作为所述泛函级数滤波器模型的输出；y(n)满足如下辨识公式(1)：
    Figure PCTCN2019111096-appb-100001
    其中，h p是泛函级数滤波器模型的核函数，M p是第p阶核函数的滤波器长度，i表示离散域核函数的点坐标，i为取值范围为0～M p-1的自然数，x p(n-i)
    n表示核函数的采样点，n为正整数，p为正整数，表示第n-i点坐标的x序列的p次方；
    步骤S2、生成对数扫频信号并作所述马达系统的激励电压x(n)，x(n)满足公式(2)：
    Figure PCTCN2019111096-appb-100002
    其中，A为幅度，ω 1和ω 2分别为该扫频信号的起始角频率和终止角频率，T为信号时长，N为总采样点数，n为正整数，
    同时，参数满足公式(3)：
    Figure PCTCN2019111096-appb-100003
    其中，η＝…,-1,0,1,2,…；
    步骤S3、将步骤S2生成的对数扫频信号馈给所述马达系统，并用加速度计采集所述马达系统输出的振动加速度y(n)；
    步骤S4、生成所述对数扫频信号的逆信号
    Figure PCTCN2019111096-appb-100004
    满足公式(4)：
    Figure PCTCN2019111096-appb-100005
    步骤S5、将步骤S3中采集到的所述振动加速度y(n)与步骤S4中的逆信号
    Figure PCTCN2019111096-appb-100006
    卷积，得到一维脉冲响应序列k(n)；
    步骤S6、对步骤S5中所得到的所述一维脉冲响应序列k(n)使用窗函数截取各部分脉冲响应序列k 1(n)～k p(n)：
    Figure PCTCN2019111096-appb-100007
    其中，u为单位阶跃函数，γ p0为固定常数，表示第p个脉冲响应的延时量，计算如下：
    Figure PCTCN2019111096-appb-100008
    步骤S7、根据各部分脉冲响应序列求解核函数h 1～h p：
    Figure PCTCN2019111096-appb-100009
    将求解出的各部分核函数代入所述马达系统的辨识公式，并通过所述辨识公式描述和辨识所述马达系统。
  2. 根据权利要求1所述的马达系统辨识方法，其特征在于，在所述步骤S5中，所述一维脉冲响应序列k(n)由一系列延时的脉冲响应序列组成。
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