WO2021134323A1 - 马达非线性模型判断方法和系统 - Google Patents

马达非线性模型判断方法和系统 Download PDF

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WO2021134323A1
WO2021134323A1 PCT/CN2019/130153 CN2019130153W WO2021134323A1 WO 2021134323 A1 WO2021134323 A1 WO 2021134323A1 CN 2019130153 W CN2019130153 W CN 2019130153W WO 2021134323 A1 WO2021134323 A1 WO 2021134323A1
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motor
signal
nonlinear model
impulse response
tooling
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PCT/CN2019/130153
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French (fr)
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向征
郭璇
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瑞声声学科技(深圳)有限公司
瑞声科技(新加坡)有限公司
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Priority to PCT/CN2019/130153 priority Critical patent/WO2021134323A1/zh
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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

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  • the present invention relates to the technical field of tactile perception, in particular to a method and system for judging a nonlinear model of a motor.
  • a tactile actuator with a motor as a carrier can obtain a customized tactile experience by designing its specific waveform, which greatly enriches the user's perception.
  • drive forms such as magnetic attraction are usually added, and the biggest feature of this drive form is that the nonlinearity is more obvious; this requires the identification of the nonlinear system of the motor, so that the motor vibrator can be predicted more accurately
  • the movement provides the basis of motor modeling for signal production.
  • the present invention provides a simple motor nonlinear model judgment method and system, which can conveniently judge which model the motor belongs to, optimizes the motor nonlinear model judgment method and system in the prior art, and improves the user experience effect.
  • a method for judging a non-linear motor model includes:
  • Step S10 Generate a logarithmic sweep frequency signal x(n);
  • Step S20 feedback the logarithmic sweep signal x(n) to the motor, and collect the acceleration signal y(n) output by the motor;
  • Step S30 Generate the inverse signal of the logarithmic sweep signal x(n)
  • Step S40 Combine the acceleration signal y(n) and the inverse signal of the logarithmic sweep signal x(n) Convolve to obtain a one-dimensional impulse response sequence k(n), the one-dimensional impulse response sequence k(n) is composed of a series of delayed impulse response sequences;
  • Step S50 Use a window function to intercept each part of the impulse response sequence for the one-dimensional impulse response sequence k(n) to obtain a linear term k 1 (n);
  • Step S60 judge the nonlinear model of the motor according to the linear term k 1 (n);
  • n refers to the time.
  • step S10 the formula for generating the logarithmic sweep signal x(n) in step S10 is:
  • A is the signal amplitude
  • ⁇ 1 and ⁇ 2 respectively represent the starting and ending angular frequencies of the sweep signal
  • T is the signal duration
  • N is the total number of sampling points.
  • n is an integer.
  • step S30 generates the inverse signal of the logarithmic sweep signal x(n)
  • the formula is:
  • step S50 includes: using a window function for the one-dimensional impulse response sequence k(n) to intercept p impulse response sequences k 1 (n) ⁇ k p (n), and the calculation formula is:
  • u is the unit step function
  • r p0 is a fixed constant, which represents the delay of the p-th impulse response
  • its calculation formula is:
  • the present invention provides a motor nonlinear model judgment system.
  • the motor nonlinear model judgment system includes: the motor nonlinear model judgment system includes: a personal computer (PC), a motor, a tool, a sponge, An accelerometer, a capture card, a first amplifier and a second amplifier, wherein the PC is connected to the capture card, the motor is installed on the tooling, the tooling is placed and installed on the sponge, and the The accelerometer is installed on the tooling, the accelerometer is connected to the first amplifier, the first amplifier is connected to the capture card, and the second amplifier is connected to the capture card and the motor; the PC is used to execute The motor nonlinear model judgment method according to any one of claims 1 to 4, the linear judgment is obtained by the motor nonlinear model judgment method, and then a logarithmic sweep signal x(n) is output, the logarithmic sweep signal x(n) is the voltage signal that excites the motor.
  • PC personal computer
  • the tooling is used to carry the motor, and the tooling is adhesively attached to the motor;
  • the sponge is used to carry the tooling, and the tooling is placed on the sponge to avoid the influence of the environment on the measurement result;
  • the accelerometer is installed on the tooling for measuring the output acceleration signal y(n) of the tooling in the vibration direction of the motor.
  • the first amplifier is used to amplify the acceleration signal y(n) collected by the accelerometer; the acquisition card: used to synchronously collect the acceleration signal y(n) and the logarithmic sweep signal x (n).
  • the second amplifier is used to amplify the voltage signal of the excitation motor, and is used to output the amplified voltage signal of the excitation motor to the motor.
  • the present invention provides a simple motor nonlinear model judgment method and system, optimizes the prior art motor nonlinear model judgment method and system, and improves the user experience effect.
  • FIG. 1 is a schematic diagram of a Wiener model provided by Embodiment 1 of the present invention.
  • FIG. 2 is a schematic diagram of a Hammerstein model provided by Embodiment 1 of the present invention.
  • FIG. 3 is a schematic flowchart of a method for judging a non-linear motor model according to Embodiment 1 of the present invention
  • Fig. 4 is a schematic diagram of a motor system provided by the first embodiment of the present invention.
  • the present invention provides a method for judging a motor's nonlinear model.
  • the motor's nonlinear model is judged based on the Wiener model and the Hammerstein model.
  • the linear model includes the Wiener model and the Hammerstein model.
  • the Wiener model is implemented by a linear memory system and a nonlinear memoryless system in turn.
  • the linear memory system may use a finite impulse response digital filter (Finite Impulse Response, FIR) and infinite impulse response digital filter (Infinite Impulse Response, IIR) filters are described, and the non-linear memoryless system can be described by using a polynomial model.
  • FIR Finite Impulse Response
  • IIR infinite impulse response digital filter
  • the Hammerstein model is implemented by a non-linear memoryless system and a linear memory system in sequence, and the Wiener model and the Hammerstein model are implemented in a different order for the linear memory system and the nonlinear memoryless system.
  • x(t) is the result function of the linear memory system
  • * is the convolution operation
  • h(t) is the FIR or IIR filter function value of the linear memory system
  • the non-linear system of the Wiener model is calculated as follows:
  • y 1 (t) and y 2 (t) are the resulting functions of the nonlinear memoryless system process.
  • nonlinear represents the nonlinearity due to the square rate, namely
  • the present invention provides a method for judging a motor nonlinear model. Please refer to Fig. 3.
  • the method for judging a motor nonlinear model includes:
  • Step S10 Generate a logarithmic frequency sweep signal x(n); specifically, the calculation formula of the logarithmic frequency sweep signal x(n) is:
  • A is the signal amplitude
  • ⁇ 1 and ⁇ 2 respectively represent the starting and ending angular frequencies of the sweep signal
  • T is the signal duration
  • N is the total number of sampling points.
  • n is an integer.
  • Step S20 feedback the logarithmic sweep signal x(n) to the motor, and collect the acceleration signal y(n) output by the motor;
  • the logarithmic frequency sweep signal x(n) is a voltage signal for exciting the motor.
  • Step S30 Generate the inverse signal of the logarithmic sweep signal x(n)
  • Step S40 Combine the acceleration signal y(n) and the inverse signal of the logarithmic sweep signal x(n) Convolve to obtain a one-dimensional impulse response sequence k(n), the one-dimensional impulse response sequence k(n) is composed of a series of delayed impulse response sequences;
  • Step S50 Use a window function to intercept p impulse response sequences k 1 (n) ⁇ k p (n) for the one-dimensional impulse response sequence k(n) to obtain a linear term k 1 (n); the calculation formula is:
  • u is the unit step function
  • r p0 is a fixed constant, which represents the delay of the p-th impulse response
  • its calculation formula is:
  • Step S60 Judging the motor nonlinear model according to the linear term k 1 (n); specifically, when the linear term k 1 (n) is consistent, the linear term k 1 (n) is a Hammerstein model, Otherwise, it is the Wiener model.
  • the present invention provides a motor nonlinear model judgment system.
  • the motor nonlinear model judgment system includes: PC10, motor 20, tooling 30, sponge body 40, accelerometer 50, acquisition card 80, An amplifier 60 and a second amplifier 70, wherein the PC 10 is connected to the capture card 80, the motor 20 is installed on the tool 30, and the tool 30 is placed and installed on the sponge body 40.
  • the accelerometer 50 is installed on the tool 30, the accelerometer 50 is connected to the first amplifier 60, the first amplifier 60 is connected to the acquisition card 80, and the second amplifier 70 is connected to the acquisition card 80 and the motor 20; Land:
  • PC10 used to implement the aforementioned motor nonlinear model judgment method. After the linear judgment is obtained by the motor nonlinear model judgment method, the logarithmic sweep signal x(n) is output, and the logarithmic sweep signal x(n ) Is the voltage signal to excite the motor;
  • Motor 20 is a linear resonance driver (Linear resonance driver, LRA).
  • Tooling 30 used to carry the motor 20, and the tooling 30 is adhesively bonded to the motor 20;
  • Sponge body 40 used to carry the tooling 30, the tooling 30 is placed on the sponge body 40 to avoid the influence of the environment on the measurement result;
  • Accelerometer 50 installed on the tool 30, and used to measure the output acceleration signal y(n) of the tool 30 in the vibration direction of the motor 20.
  • the first amplifier 60 is used to amplify the signal collected by the accelerometer 50; wherein, the first amplifier 60 includes an input port 601 and an output port 602, the input port 601 is connected to the accelerometer 50, and the output port 602 outputs the amplified signal to the collection card 80, and the output port 602 is connected to the collection port 801 (AI0 port) of the collection card 80.
  • the second amplifier 70 used to amplify the voltage signal of the excitation motor and output the amplified voltage signal of the excitation motor to the motor; the second amplifier 70 includes an input port 701 and an output port 702, the input port 701 Connected to the output port 805 (AO0 port) of the acquisition card, the output port 702 feeds back the amplified signal to the motor 50, and at the same time outputs the amplified signal to the acquisition card acquisition port 802 (AI2 port).
  • Acquisition card 80 used to synchronously acquire the acceleration signal y(n) and logarithmic sweep signal x(n); the acquisition card 80 includes acceleration signal y(n) acquisition port 801 (AI0 port), logarithmic sweep Frequency signal x(n) acquisition port 802, output port 805 (AO0 port), and output port 803 and input port 804 connected to PC10; in one embodiment, the acquisition card 80 uses NI-DAQ 4431 acquisition card .
  • the present invention provides a simple method and system for judging the nonlinear model of a motor. By distinguishing the difference between the linear terms of the Wiener model and the Hammerstein model at different degrees of nonlinearity, it is possible to easily determine which motor belongs to. This model optimizes the judgment method and system of the motor nonlinear model in the prior art, and improves the user experience effect.

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Abstract

本发明涉及触觉感知技术领域，提供一种马达非线性模型判断方法和系统。所述马达非线性模型判断方法包括：生成对数扫频信号x(n)；将所述对数扫频信号x(n)反馈给马达，并采集所述马达输出的加速度信号y(n)；生成对数扫频信号x(n)的逆信号(I)；将所述加速度信号y(n)与所述对数扫频信号x(n)的逆信号(I) 卷积，得到一维脉冲响应序列k(n)，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；对所述一维脉冲响应序列k(n)使用窗函数截取p个脉冲响应序列，得到线性项k1(n)；对所述线性项k1(n)进行判断。此外，本发明还提供一种马达非线性模型判断系统。与现有技术相比，本发明优化了现有技术中马达非线性模型的判断方法和系统，提升了用户体验效果。

Description

马达非线性模型判断方法和系统 【技术领域】
本发明涉及触觉感知技术领域，尤其涉及一种马达非线性模型判断方法和系统。
【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逐渐进入大众视野。以马达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，极大程度地丰富了用户感知。为了获得更大的马达驱动能力，通常会加入磁吸力等驱动形式，而这种驱动形式最大的特点是非线性比较明显；这就需要对马达进行非线性系统辨识，从而能够更加准确地预测马达振子的运动，为信号制作提供马达建模依据。
非线性系统的辨识在近几年已经成为了一个广泛讨论和研究的话题，其中黑盒模型由于其灵活的建模方式而备受关注。在黑盒模型中，Wiener模型和Hammerstein模型为最典型的两种模型方式，但现有技术中，如何对这两种模型进行判断区分，方便地得到马达在所述黑盒模型中符合哪种模型，以及后续的建模实现对马达的驱动等，缺少理想的判断方法，这样影响了后续马达系统的设计，用户体验效果差。
【发明内容】
本发明提供一种简便的马达非线性模型判断方法及系统，能够方便地判断出马达属于哪种模型，优化了现有技术中马达非线性模型的判断方法和系统，提升了用户体验效果。
一种马达非线性模型判断方法，所述马达非线性模型判断方法包括：
步骤S10：生成对数扫频信号x(n)；
步骤S20：将所述对数扫频信号x(n)反馈给马达，并采集所述马达输出的加速度信号y(n)；
步骤S30：生成对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000001
步骤S40：将所述加速度信号y(n)与所述对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000002
卷积，得到一维脉冲响应序列k(n)，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S50：对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，得到线性项k 1(n)；
步骤S60：根据所述线性项k 1(n)对马达非线性模型进行判断；
其中，n是指时刻。
进一步地，所述步骤S10生成对数扫频信号x(n)的公式为：
Figure PCTCN2019130153-appb-000003
其中，A为信号幅度，ω 1和ω 2分别表示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，同时，参数满足：
Figure PCTCN2019130153-appb-000004
其中，η为整数。
进一步地，所述步骤S30生成对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000005
的公式为：
Figure PCTCN2019130153-appb-000006
进一步地，所述步骤S50包括：对所述一维脉冲响应序列k(n)使用窗函数截取p个脉冲响应序列k 1(n)～k p(n)，其计算公式为：
Figure PCTCN2019130153-appb-000007
其中，u为单位阶跃函数，r p0为固定常数，表示第p个脉冲响应的延时量，其计算公式为：
Figure PCTCN2019130153-appb-000008
此外，本发明提供一种马达非线性模型判断系统，所述马达非线性模型判断系统包括：所述马达非线性模型判断系统包括：个人计算机(Personal Computer，PC)、马达、工装、海绵体、加速度计、采集卡、第一放大器和第二放大器，其中，所述PC与所述采集卡连接，所述马达安装在所述工装上，所述工装放置安装在所述海绵体上，所述加速度计安装在所述工装上，所述加速度计连接所述第一放大器，所述第一放大器连接所述采集卡，第二放大器连接所述采集卡和马达；所述PC用于执行如权利要求1至4任一项所述的马达非线性模型判断方法，经所述马达非线性模型判断方法得到所述线性判断后输出对数扫频信号x(n)，所述对数扫频信号x(n)为激励马达的电压信号。
优选地，所述工装用于承载所述马达，所述工装与所述马达粘性贴合；
优选地，所述海绵体用于承载所述工装，所述工装放置在海绵体上以避免环境对测量结果的影响；
优选地，所述加速度计安装在所述工装上，用于测量所述工装在所述马达振动方向上的输出的加速度信号y(n)。
优选地，所述第一放大器用于将所述加速度计采集的加速度信号y(n)进行放大；所述采集卡：用于同步采集所述加速度信号y(n)和对数扫频信号x(n)。
优选地，所述第二放大器用于将所述激励马达的电压信号放大，并用于将放大的激励马达的电压信号输出至马达。
与现有技术相比，本发明提供一种简便的马达非线性模型判断方法及系统，优化了现有技术中马达非线性模型的判断方法和系统，提升了用户体验效果。
【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为本发明实施例一提供的Wiener模型示意图；
图2为本发明实施例一提供的Hammerstein模型示意图；
图3为本发明实施例一提供的马达非线性模型判断方法的流程示意图；
图4为本发明实施例一提供的马达系统示意图。
【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。
本发明提供一种马达非线性模型判断方法，具体在实施例一中，所述马达非线性模型基于Wiener模型和Hammerstein模型进行判断，具体地，请结合参阅图1和图2，所述马达非线性模型包括Wiener模型和Hammerstein模型，其中，所述Wiener模型依次通过线性有记忆系统和非线性无记忆系统实现，其中，所述线性有记忆系统可以采用有限脉冲响应数字滤波器(Finite Impulse Response,FIR)和无限脉冲响应数字滤波器(Infinite Impulse Response,IIR)滤波器进行描述，所述非线性无记忆系统可以采用多项式模型进行描述。
所述Hammerstein模型依次通过非线性无记忆系统和线性有记忆系统实现，所述Wiener模型和Hammerstein模型在实现的线性有记忆系统和非线性无记忆系统的先后顺序不一样。
具体地，设定输入为u(t)，其中，t表示时刻且t>0，所述Wiener模型的线性系统计算如下：
x(t)＝u(t)*h(t)，
其中，x(t)为经过线性有记忆系统过程的结果函数，*为卷积操作，h(t)为线性有记忆系统FIR或IIR滤波器函数值；
所述Wiener模型的非线性系统计算如下：
当非线性信号弱，即小信号时：
y 1(t)＝x(t)＝u(t)*h(t)
当非线性信号强，即大信号时：
y 2(t)＝x(t)+x 2(t)＝u(t)*h(t)+(u(t)*h(t)) 2≠y 1(t)+nonlinear
其中，y 1(t)和y 2(t)为经过非线性无记忆系统过程的结果函数。
其中，nonlinear表示由于平方率带来的非线性，即
nonlinear＝u 2(t)*h(t)。
所述Hammerstein模型的计算如下：
非线性系统的计算：
当非线性信号弱，即小信号时：
x 1(t)＝u(t)；
当非线性信号强，即大信号时：
x 2(t)＝u(t)+u 2(t)
线性系统的计算：
y 1(t)＝x 1(t)*h(t)＝u(t)*h(t)，
y 2(t)＝x 2(t)*h(t)＝(u(t)+u 2(t))*h(t)＝y 1(t)+nonlinear。
根据Wiener模型和Hammerstein模型在计算过程和结果的差异，本发明提供一种马达非线性模型判断方法，请参阅图3，所述马达非线性模型 判断方法包括：
步骤S10：生成对数扫频信号x(n)；具体地，对数扫频信号x(n)的计算公式为：
Figure PCTCN2019130153-appb-000009
其中，A为信号幅度，ω 1和ω 2分别表示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，同时，参数满足：
Figure PCTCN2019130153-appb-000010
其中，η为整数。
步骤S20：将所述对数扫频信号x(n)反馈给马达，并采集所述马达输出的加速度信号y(n)；
具体在本实施例中，所述对数扫频信号x(n)为激励马达的电压信号。
步骤S30：生成对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000011
具体地，生成对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000012
的公式为：
Figure PCTCN2019130153-appb-000013
步骤S40：将所述加速度信号y(n)与所述对数扫频信号x(n)的逆信号
Figure PCTCN2019130153-appb-000014
卷积，得到一维脉冲响应序列k(n)，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S50：对所述一维脉冲响应序列k(n)使用窗函数截取p个脉冲响应序列k 1(n)～k p(n)，得到线性项k 1(n)；其计算公式为：
Figure PCTCN2019130153-appb-000015
其中，u为单位阶跃函数，r p0为固定常数，表示第p个脉冲响应的延时量，其计算公式为：
Figure PCTCN2019130153-appb-000016
步骤S60：根据所述线性项k 1(n)对马达非线性模型进行判断；具体地，当所述线性项k 1(n)一致时，所述线性项k 1(n)为Hammerstein模型，否则为Wiener模型。
此外，本发明提供一种马达非线性模型判断系统，请参阅图4，所述马达非线性模型判断系统包括：PC10、马达20、工装30、海绵体40、加速度计50、采集卡80、第一放大器60和第二放大器70，其中，所述PC10与所述采集卡80连接，所述马达20安装在所述工装30上，所述工装30放置安装在所述海绵体40上，所述加速度计50安装在所述工装30上，所述加速度计50连接所述第一放大器60，所述第一放大器60连接所述采集卡80，第二放大器70连接采集卡80和马达20；具体地：
PC10：用于执行上述的马达非线性模型判断方法，经所述马达非线性模型判断方法得到所述线性判断后输出对数扫频信号x(n)，所述对数扫频信号x(n)为激励马达的电压信号；
马达20：为线性谐振传动器(Linear resonance driver，LRA)。
工装30：用于承载所述马达20，所述工装30与所述马达20粘性贴合；
海绵体40：用于承载所述工装30，所述工装30放置在海绵体40上以避免环境对测量结果的影响；
加速度计50：安装在所述工装30上，用于测量所述工装30在所述马达20振动方向上的输出的加速度信号y(n)。
第一放大器60：用于将所述加速度计50采集的信号进行放大；其中，第一放大器60包括输入口601和输出口602，所述输入口601连接所述加速度计50，所述输出口602将放大的信号输出至采集卡80，所述输出口602连接所述采集卡80的采集口801(AI0端口)。
第二放大器70：用于将所述激励马达的电压信号放大，并将放大的激 励马达的电压信号输出至马达；所述第二放大器70包括输入口701和输出口702，所述输入口701与所述采集卡的输出口805(AO0端口)连接，所述输出口702将放大的信号反馈给马达50，同时将放大的信号输出给采集卡采集端口802(AI2端口)。
采集卡80：用于同步采集所述加速度信号y(n)和对数扫频信号x(n)；所述采集卡80包括加速度信号y(n)采集口801(AI0端口)、对数扫频信号x(n)采集口802、输出口805(AO0端口)，以及与PC10连接的输出口803和输入口804；具体在一实施例中，所述采集卡80使用NI-DAQ 4431采集卡。
与现有技术相比，本发明提供一种简便的马达非线性模型判断方法及系统，通过区分Wiener模型和Hammerstein模型在不同非线性程度下的线性项的差别，能够方便地判断出马达属于哪种模型，优化了现有技术中马达非线性模型的判断方法和系统，提升了用户体验效果。
以上所述的仅是本发明的实施方式，在此应当指出，对于本领域的普通技术人员来说，在不脱离本发明创造构思的前提下，还可以做出改进，但这些均属于本发明的保护范围。

Claims (10)

  1. 一种马达非线性模型判断方法，其特征在于，所述马达非线性模型判断方法包括：
    步骤S10：生成对数扫频信号x(n)；
    步骤S20：将所述对数扫频信号x(n)反馈给马达，并采集所述马达输出的加速度信号y(n)；
    步骤S30：生成对数扫频信号x(n)的逆信号
    Figure PCTCN2019130153-appb-100001
    步骤S40：将所述加速度信号y(n)与所述对数扫频信号x(n)的逆信号
    Figure PCTCN2019130153-appb-100002
    卷积，得到一维脉冲响应序列k(n)，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S50：对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，得到线性项k 1(n)；
    步骤S60：根据所述线性项k 1(n)对马达非线性模型进行判断；
    其中，n是指时刻。
  2. 根据权利要求1所述的马达非线性模型判断方法，其特征在于，所述步骤S10生成对数扫频信号x(n)的公式为：
    Figure PCTCN2019130153-appb-100003
    其中，A为信号幅度，ω 1和ω 2分别表示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，同时，参数满足：
    Figure PCTCN2019130153-appb-100004
    其中，η为整数。
  3. 根据权利要求1所述的马达非线性模型判断方法，其特征在于，所述步骤S30生成对数扫频信号x(n)的逆信号
    Figure PCTCN2019130153-appb-100005
    的公式为：
    Figure PCTCN2019130153-appb-100006
  4. 根据权利要求1所述的马达非线性模型判断方法，其特征在于，所述步骤S50包括：对所述一维脉冲响应序列k(n)使用窗函数截取p个脉冲响应序列k 1(n)～k p(n)，其计算公式为：
    Figure PCTCN2019130153-appb-100007
    其中，u为单位阶跃函数，r p0为固定常数，表示第p个脉冲响应的延时量，其计算公式为：
    Figure PCTCN2019130153-appb-100008
  5. 一种马达非线性模型判断系统，其特征在于，所述马达非线性模型判断系统包括：个人计算机(Personal Computer，PC)、马达、工装、海绵体、加速度计、采集卡、第一放大器和第二放大器，其中，所述PC与所述采集卡连接，所述马达安装在所述工装上，所述工装放置安装在所述海绵体上，所述加速度计安装在所述工装上，所述加速度计连接所述第一放大器，所述第一放大器连接所述采集卡，第二放大器连接所述采集卡和马达；所述PC用于执行如权利要求1至4任一项所述的马达非线性模型判断方法，经所述马达非线性模型判断方法得到所述线性判断后输出对数扫频信号x(n)，所述对数扫频信号x(n)为激励马达的电压信号。
  6. 根据权利要求5所述的马达非线性模型判断系统，其特征在于，所述工装用于承载所述马达，所述工装与所述马达粘性贴合。
  7. 根据权利要求5所述的马达非线性模型判断系统，其特征在于，所述海绵体用于承载所述工装，所述工装放置在海绵体上以避免环境对测量结果的影响。
  8. 根据权利要求5所述的马达非线性模型判断系统，其特征在于，所述加速度计安装在所述工装上，用于测量所述工装在所述马达振动方向上的输出的加速度信号y(n)。
  9. 根据权利要求5所述的马达非线性模型判断系统，其特征在于，所述第一放大器用于将所述加速度计采集的加速度信号y(n)进行放大；所述采集卡：用于同步采集所述加速度信号y(n)和对数扫频信号x(n)。
  10. 根据权利要求5所述的马达非线性模型判断系统，其特征在于，所述第二放大器用于将所述激励马达的电压信号放大，并用于将放大的激励马达的电压信号输出至马达。
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