CEPS calibration method based on autonomous learning stability compensation parameters

Abstract

The invention discloses a CEPS calibration method based on autonomous learning stability compensation parameters, and the method comprises the following steps: 1, initializing parameters: initializing all test parameters at preset values; 2, recording the torque and the rotation angle, and judging whether the torque and the torque and rotation angle values are abnormal or not; step 3, processing abnormity existing in torque and rotation angle changes; 4, processing that the change of the torque and the change of the rotation angle are not abnormal; and 5, judging whether the calculation of the fluctuation of the torque value in a steering wheel turning period is ended or not. According to the method, the problem that the optimal parameter is missed when the parameter is manually adjusted within a certain range is solved, and all the parameters within the range can be traversed according to the range, the change step length and the hand feeling influence factor of the manually set and autonomously learned adjustment parameter, so that the debugging time is saved; besides, by outputting self-adjusting parameters and corresponding torque fluctuation and phase delay data, a large amount of experimental data is accumulated, and parameter analysis of the system by developers is more convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of CEPS technology, and in particular to a CEPS calibration method based on autonomous learning stability compensation parameters. Background Art

[0002] With the development of automobile steering system technology, EPS (electric power steering system) has basically replaced HPS (hydraulic power steering system) and EHPS (electronically controlled hydraulic power steering system) in civilian cars. Among them, CEPS (column-type electric power steering system) is widely used in small and medium-sized cars. Based on CEPS, it can realize basic power assistance, active return, end protection, side force compensation and other functions, and even switch different feel according to different needs. The driver can control the steering wheel very easily, giving the driver a good driving experience. However, due to the influence of CEPS's own parameters and power gain, the system needs to be compensated for stability in order to be used normally and ensure the safety of the driver. The parameters of stability compensation can not only determine the stability of the system, but also affect the feel of operating the steering wheel, so the parameter calibration of stability compensation plays a very important role in CEPS.

[0003] In the current related technologies, the existing CEPS qualitative compensation parameter calibration method mainly relies on torque sensors to manually calibrate parameters based on experience and sampling data, and then complete the CEPS stability compensation and feel debugging. However, due to time cost, technical maturity and parameter optimization, the CEPS calibration method of self-learning stability compensation parameters can save time and find the optimal parameters that best suit the expected indicators within the specified parameter range.

[0004] Then, to ensure the safety during driving, the calibration of stability compensation parameters needs to ensure the absolute stability of the system; in addition, from the perspective of driver control, the calibration of stability compensation parameters should meet the driver's needs as much as possible, but this requirement cannot be met in the existing technology. Therefore, it is particularly important to design a CEPS calibration method that autonomously learns stability compensation parameters. Summary of the invention

[0005] The object of the present invention is to provide a CEPS calibration method based on autonomous learning of stability compensation parameters to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a CEPS calibration method based on autonomous learning stability compensation parameters, comprising the following steps:

[0007] The first step is parameter initialization. All test parameters are initialized to the preset values ​​to ensure the stability of the steering system when adjusting the parameters. A sweep frequency signal from 0 to 30 Hz is input to the CEPS system. The torque value output by the system is collected and the spectrum is analyzed. Then the obtained parameter a is used to set the parameters b, c, d and the range of each parameter based on experience. This ensures the stability of the system during autonomous learning.

[0008] Step 2: Record the torque and angle and determine whether the torque and angle values ​​are abnormal. The angle and the corresponding torque value of a steering wheel rotation cycle are measured by the torque sensor and the angle sensor. When the steering wheel is turned slowly and at a constant speed, the torque and angle in the entire cycle need to be recorded, and then determine whether the torque and angle are abnormal. If the torque and angle are abnormal, proceed to step 3. If the torque and angle are normal, proceed directly to step 4.

[0009] The third step is to deal with abnormal changes in torque and angle. If there are abnormalities in the torque and angle at a certain moment in the recorded data, the abnormal angle and torque value are processed; after processing, the fourth step is carried out to analyze the torque and calculate the torque fluctuation value corresponding to the moment;

[0010] Step 4: If there is no abnormality in the changes of torque and rotation angle, the torque is analyzed and calculated to obtain the torque fluctuation value corresponding to the moment;

[0011] Step 5: determine whether the calculation of torque fluctuations within a steering wheel turning cycle is completed. If it is completed, proceed to step 6. If it is not completed, repeat step 2 to continue to determine whether there is an abnormality in the torque and angle values ​​at a certain moment in the recorded data.

[0012] Step 6: Calculate the torque fluctuation factor. According to the torque fluctuation value calculated in step 4, use a table lookup (such as Figure 3 The torque fluctuation factor corresponding to the parameter is obtained as shown; then, the parameter calculation is performed according to the torque fluctuation factor, and the relationship between the phase angle and the frequency under the parameter is calculated; the corresponding phase-frequency factor is obtained by looking up the table according to the relationship between the phase angle and the frequency obtained by calculation;

[0013] Step 7: Set the weights of torque fluctuation and feel stickiness; then calculate the comprehensive value corresponding to the parameter based on the torque fluctuation factor and the phase-frequency factor;

[0014] Step 8: Determine whether to end learning; if learning continues, the parameters will be automatically changed according to the fixed step size, the parameters will be reinitialized, and the above steps will be repeated; if learning is ended, the optimal parameters within a certain range will be obtained based on the calculated comprehensive values, and the parameters, torque fluctuations, minimum phase angle, and comprehensive values ​​obtained in this learning will be output. The present invention solves the problem of missing the optimal parameters when manually adjusting parameters within a certain range, and can autonomously learn and adjust the range of parameters, change step size, and hand feel influencing factors according to artificial settings, and can traverse all parameters within the range to save debugging time; in addition, by outputting autonomous adjustment parameters and corresponding torque fluctuations and phase delay data, a large amount of experimental data is accumulated, which makes it more convenient for developers to analyze system parameters.

[0015] Preferably, the standard for judging whether there is an abnormality in the change of torque and angle values ​​in the second step is that when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase, if the torque and angle values ​​corresponding to two adjacent time points do not increase, it is considered that there is an abnormality in the angle and torque values ​​being processed; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease, if the torque and angle values ​​corresponding to two adjacent time points do not decrease, it is considered that there is an abnormality in the angle and torque values ​​being processed.

[0016] Preferably, the method of processing the corresponding torque values ​​when the torque and angle values ​​are abnormal in the third step is that when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are greater than the previous torque and angle values; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are less than the previous torque and angle values.

[0017] Preferably, the fluctuation mode of analyzing and calculating the torque in the fourth step is to subtract the torque value corresponding to the previous torque and angle value from the torque corresponding to the next torque and angle value, and the difference obtained is the torque fluctuation value within the two torque and angle values. The maximum and minimum values ​​of the torque fluctuation and the corresponding torque and angle values ​​are obtained by comparing them one by one.

[0018] Preferably, the fifth step determines whether the calculation method of the torque fluctuation within a steering wheel turning cycle is: determine whether two terminal torque and angle values ​​are passed according to the currently processed torque and angle values; if two terminal angle values ​​are passed and the angle is processed to 0, the calculation of the torque fluctuation is terminated and the sixth step is performed; otherwise, the second step is repeated.

[0019] Preferably, the sixth step obtains the torque fluctuation factor corresponding to the parameter by looking up the table according to the calculated torque fluctuation value: the range of the torque fluctuation value is 0.16-0.26Nm, and the corresponding torque fluctuation factor value is 1-0; when the torque fluctuation value is less than 0.16, the corresponding factor is 1, and when the torque fluctuation value is greater than 0.26, the corresponding factor is 0; the factor value can represent the human feel, and when the corresponding factor value is greater than 0.6, it indicates that the feel under the parameter is qualified, otherwise the feel is unqualified, and the factor value corresponding to the torque fluctuation range can be changed according to requirements; the relationship between the phase angle and the frequency under the parameter is calculated according to the parameter, wherein the phase-frequency characteristic expression is calculated according to the transfer function, and then the current parameters a, b, c, d are substituted into the expression and the frequency values ​​​​of 0-100 are traversed to obtain the minimum phase angle and frequency corresponding to the parameter.

[0020] Preferably, the seventh step calculates the comprehensive value corresponding to the parameter according to the torque fluctuation factor and the phase-frequency factor: the range of the minimum phase angle under different parameters is -20 to -70 degrees, and the corresponding phase-frequency factor range is 1-0; if the minimum phase angle is greater than -20 degrees, the phase-frequency factor is 1, and if the minimum phase angle is less than -70 degrees, the phase-frequency factor is 0; this factor value can represent the sticky feel of the steering wheel, and when the corresponding factor value is greater than 0.6, it means that the sticky feel under this parameter is qualified, otherwise it is unqualified, and the phase-frequency factor value can be changed according to requirements. The weights of torque fluctuation and sticky feel are set, and two weights are set according to people's subjective feelings. If more attention is paid to torque fluctuation, w1 is larger; if more attention is paid to sticky paper feel, w2 is larger, and w1+w2=1 is satisfied when setting the weights.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention solves the problem of missing the optimal parameters when manually adjusting parameters within a certain range, and can autonomously learn the range, change step size, and feel influencing factor of the adjustment parameters according to the artificial settings, and can traverse all parameters within the range to save debugging time; in addition, by outputting the autonomously adjusted parameters and the corresponding torque fluctuation and phase delay data, a large amount of experimental data is accumulated, which makes it more convenient for developers to analyze the system parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of a CEPS calibration method based on autonomous learning of stability compensation parameters provided by the present invention;

[0024] Figure 2 A schematic diagram of the acquisition process of the torque fluctuation factor and the phase-frequency factor of the steering wheel in a CEPS calibration method based on autonomous learning stability compensation parameters provided by the present invention;

[0025] Figure 3A schematic diagram of a torque fluctuation factor, phase-frequency factor, comprehensive value, and minimum phase angle calculation process provided by the present invention;

[0026] Figure 4 A schematic diagram of a rotation angle and torque signal provided by the present invention;

[0027] Figure 5 This is a schematic diagram of the angle and torque signal after processing provided by the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1-5 The present invention provides a technical solution: a CEPS calibration method based on autonomous learning stability compensation parameters, comprising the following steps:

[0030] The first step is parameter initialization. All test parameters are initialized to the preset values ​​to ensure the stability of the steering system when adjusting the parameters. A sweep frequency signal from 0 to 30 Hz is input to the CEPS system. The torque value output by the system is collected and the spectrum is analyzed. Then the obtained parameter a is used to set the parameters b, c, d and the range of each parameter based on experience. This ensures the stability of the system during autonomous learning.

[0031] The second step is to record the torque and angle and determine whether the torque and angle values ​​are abnormal. The angle and the corresponding torque value of a steering wheel rotation cycle are measured by the torque sensor and the angle sensor (TSA sensor). When the steering wheel is turned slowly and at a constant speed, the torque and angle in the entire cycle need to be recorded, and then determine whether the torque and angle are abnormal. If the torque and angle are abnormal, proceed to the third step. If the torque and angle are normal, proceed directly to the fourth step; (where Figure 4 The torque and angle are in abnormal state. Figure 5 is the normal state after the torque and angle values ​​are processed);

[0032] The third step is to deal with abnormal changes in torque and angle. If there are abnormalities in the torque and angle at a certain moment in the recorded data, the abnormal angle and torque value are processed; after processing, the fourth step is carried out to analyze the torque and calculate the torque fluctuation value corresponding to the moment;

[0033] Step 4: If there is no abnormality in the changes of torque and rotation angle, the torque is analyzed and calculated to obtain the torque fluctuation value corresponding to the moment;

[0034] Step 5: determine whether the calculation of torque fluctuations within a steering wheel turning cycle is completed. If it is completed, proceed to step 6. If it is not completed, repeat step 2 to continue to determine whether there is an abnormality in the torque and angle values ​​at a certain moment in the recorded data.

[0035] Step 6: Calculate the torque fluctuation factor. According to the torque fluctuation value calculated in step 4, use a table lookup (such as Figure 3 The torque fluctuation factor corresponding to the parameter is obtained as shown; then, the parameter calculation is performed according to the torque fluctuation factor, and the relationship between the phase angle and the frequency under the parameter is calculated; the corresponding phase-frequency factor is obtained by looking up the table according to the relationship between the phase angle and the frequency obtained by calculation;

[0036] Step 7: Set the weights of torque fluctuation and feel stickiness; then calculate the comprehensive value corresponding to the parameter based on the torque fluctuation factor and the phase-frequency factor;

[0037] Step 8: Determine whether to end learning; if learning continues, the parameters will be automatically changed according to the fixed step size, the parameters will be reinitialized, and the above steps will be repeated; if learning is ended, the optimal parameters within a certain range will be obtained based on the calculated comprehensive values, and the parameters, torque fluctuations, minimum phase angle, and comprehensive values ​​obtained in this learning will be output. The present invention solves the problem of missing the optimal parameters when manually adjusting parameters within a certain range, and can autonomously learn and adjust the range of parameters, change step size, and hand feel influencing factors according to artificial settings, and can traverse all parameters within the range to save debugging time; in addition, by outputting autonomous adjustment parameters and corresponding torque fluctuations and phase delay data, a large amount of experimental data is accumulated, which makes it more convenient for developers to analyze system parameters.

[0038] In the second step, the standard for judging whether there is an abnormality in the change of torque and angle values ​​is that when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase. If the torque and angle values ​​corresponding to two adjacent time points do not increase, it is considered that the angle and torque values ​​being processed are abnormal; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease. If the torque and angle values ​​corresponding to two adjacent time points do not decrease, it is considered that the angle and torque values ​​being processed are abnormal;

[0039] In the third step, the torque and angle values ​​corresponding to abnormal torque and angle values ​​are processed as follows: when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are greater than the previous torque and angle values; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are less than the previous torque and angle values;

[0040] In the fourth step, the torque fluctuation method for analyzing and calculating the torque is to subtract the torque value corresponding to the previous torque and angle value from the torque corresponding to the next torque and angle value. The difference is the torque fluctuation value within the two torque and angle values. The maximum and minimum values ​​of the torque fluctuation and the corresponding torque and angle values ​​are obtained by comparison one by one.

[0041] The fifth step is to determine whether the calculation method of the torque fluctuation within a steering wheel turning cycle is as follows: determine whether two terminal torque and angle values ​​have been passed according to the currently processed torque and angle values. If two terminal angle values ​​have been passed and the angle is processed to be 0, the calculation of the torque fluctuation is terminated and the sixth step is performed. Otherwise, the second step is repeated.

[0042] The sixth step is to obtain the torque fluctuation factor corresponding to the parameter by looking up the table according to the calculated torque fluctuation value: the range of the torque fluctuation value is 0.16-0.26Nm, and the corresponding torque fluctuation factor value is 1-0; when the torque fluctuation value is less than 0.16, the corresponding factor is 1, and when the torque fluctuation value is greater than 0.26, the corresponding factor is 0; the factor value can represent the human feel, and when the corresponding factor value is greater than 0.6, it means that the feel under the parameter is qualified, otherwise the feel is unqualified, and the factor value corresponding to the torque fluctuation range can be changed according to requirements; the relationship between the phase angle and the frequency under the parameter is calculated according to the parameter, wherein the phase-frequency characteristic expression is calculated according to the transfer function, and then the current parameters a, b, c, d are brought into the expression and the frequency values ​​​​of 0-100 are traversed to obtain the minimum phase angle and frequency corresponding to the parameter;

[0043] The seventh step is to calculate the comprehensive value corresponding to the parameter based on the torque fluctuation factor and the phase-frequency factor: the range of the minimum phase angle under different parameters is -20 to -70 degrees, and the corresponding phase-frequency factor range is 1-0; if the minimum phase angle is greater than -20 degrees, the phase-frequency factor is 1, and if the minimum phase angle is less than -70 degrees, the phase-frequency factor is 0; this factor value can represent the sticky feel of the steering wheel. When the corresponding factor value is greater than 0.6, it means that the sticky feel under this parameter is qualified, otherwise it is unqualified, and the phase-frequency factor value can be changed according to requirements. Set the weights of torque fluctuation and sticky feel. Set two weights according to people's subjective feelings. If you pay more attention to torque fluctuation, w1 is larger; if you pay more attention to the sticky feel, w2 is larger, and when setting the weights, w1+w2=1 is satisfied;

[0044] In the eighth step, the output comprehensive value = torque fluctuation factor * w1 + phase frequency factor * w2; and the calculated result and the corresponding parameters a, b, c, d are saved. When the comprehensive numerical calculation is completed, the learning mark position will be set to 1, prompting that the learning is completed; if the learning mark position is manually set to 0, the system will continue to learn the parameters; if the learning mark position is manually set to 2, the system will stop learning the parameters; continuing to learn will automatically change the parameters according to the fixed step size, including: if the system continues to learn the parameters, the system will change the parameters a, b, c, and d according to the fixed step size set manually; since the frequency response of the system is known, parameter a basically remains unchanged, and the system only automatically adjusts b, c, and d. When a parameter changes from the initial value to the set upper limit, the parameter will change from the initial value to the lower limit with a fixed step size; after the learning is completed, the optimal parameters within a certain range will be obtained based on the calculated comprehensive values, and the parameters, torque fluctuations, minimum phase angles, and comprehensive numerical outputs obtained in this learning will include: based on the calculation of the comprehensive values ​​of each steering wheel turning cycle, the parameters corresponding to the maximum comprehensive value are written into the system; each comprehensive value and the corresponding parameters, torque fluctuation values, and minimum phase angles are output to facilitate the debugging personnel to analyze the system.

[0045] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0046] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A CEPS calibration method based on autonomous learning stability compensation parameters, comprising the following steps, characterized in that: The first step is parameter initialization, which initializes all test parameters to predetermined values ​​to ensure the stability of the steering system when adjusting the parameters; Step 2: record the torque and the angle and determine whether the torque and the angle are abnormal. If the torque and the angle are abnormal, proceed to step 3. If the torque and the angle are normal, proceed directly to step 4. The third step is to deal with abnormal changes in torque and angle. If there are abnormalities in the torque and angle at a certain moment in the recorded data, the abnormal angle and torque value are processed; after processing, the fourth step is carried out to analyze the torque and calculate the torque fluctuation value corresponding to the moment; Step 4: If there is no abnormality in the changes of torque and rotation angle, the torque is analyzed and calculated to obtain the torque fluctuation value corresponding to the moment; Step 5: determine whether the calculation of torque fluctuations within a steering wheel turning cycle is completed. If it is completed, proceed to step 6. If it is not completed, repeat step 2 to continue to determine whether there is an abnormality in the torque and angle values ​​at a certain moment in the recorded data. Step 6: Calculate the torque fluctuation factor. According to the torque fluctuation value calculated in step 4, obtain the torque fluctuation factor corresponding to the parameter by looking up the table. Then, perform parameter calculation according to the torque fluctuation factor to calculate the relationship between the phase angle and the frequency under the parameter. According to the calculated relationship between the phase angle and the frequency, obtain the corresponding phase-frequency factor by looking up the table. Step 7. Set the weights of torque fluctuation and feel viscosity; Then, the comprehensive value corresponding to the parameter is calculated according to the torque fluctuation factor and the phase-frequency factor; Step 8: Determine whether to end the study; If learning continues, the parameters will be automatically changed according to the fixed step size, the parameters will be reinitialized, and the above steps will be repeated; if learning is terminated, the data will be output.

2. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The standard for judging whether there is an abnormality in the change of torque and angle values ​​in the second step is that when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase. If the torque and angle values ​​corresponding to two adjacent time points do not increase, it is considered that there is an abnormality in the angle and torque values ​​being processed; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease. If the torque and angle values ​​corresponding to two adjacent time points do not decrease, it is considered that there is an abnormality in the angle and torque values ​​being processed.

3. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The method of processing the corresponding torque values ​​when the torque and angle values ​​are abnormal in the third step is that when the steering wheel is turned from the neutral position, the torque and angle values ​​gradually increase, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are greater than the previous torque and angle values; when the steering wheel is turned back from the end, the torque and angle values ​​gradually decrease, and the abnormal angle and torque values ​​are eliminated until the next torque and angle values ​​are less than the previous torque and angle values.

4. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The fluctuation method of analyzing and calculating the torque in the fourth step is to subtract the torque value corresponding to the previous torque and angle value from the torque corresponding to the next torque and angle value. The difference obtained is the torque fluctuation value within the two torque and angle values. The maximum and minimum values ​​of the torque fluctuation and the corresponding torque and angle values ​​are obtained by comparing them one by one.

5. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The fifth step determines whether the calculation method of the torque fluctuation within a steering wheel turning cycle is: determine whether the two terminal torque and angle values ​​are passed according to the currently processed torque and angle values; if the two terminal angle values ​​are passed and the angle is processed to 0, the calculation of the torque fluctuation is terminated and the sixth step is performed; otherwise, the second step is repeated.

6. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The sixth step obtains the torque fluctuation factor corresponding to the parameter by looking up the table according to the calculated torque fluctuation value: the range of the torque fluctuation value is 0.16-0.26Nm, and the corresponding torque fluctuation factor value is 1-0; when the torque fluctuation value is less than 0.16, the corresponding factor is 1, and when the torque fluctuation value is greater than 0.26, the corresponding factor is 0; the factor value can represent the human feel, and when the corresponding factor value is greater than 0.6, it indicates that the feel under the parameter is qualified, otherwise the feel is unqualified, and the factor value corresponding to the torque fluctuation range can be changed according to requirements; the relationship between the phase angle and the frequency under the parameter is calculated according to the parameter, wherein the phase-frequency characteristic expression is calculated according to the transfer function, and then the current parameters a, b, c, d are substituted into the expression and the frequency values ​​​​of 0-100 are traversed to obtain the minimum phase angle and frequency corresponding to the parameter.

7. The CEPS calibration method based on autonomous learning stability compensation parameters according to claim 1, characterized in that: The seventh step calculates the comprehensive value corresponding to the parameter according to the torque fluctuation factor and the phase-frequency factor: the range of the minimum phase angle under different parameters is -20 to -70 degrees, and the corresponding phase-frequency factor range is 1-0; if the minimum phase angle is greater than -20 degrees, the phase-frequency factor is 1, and if the minimum phase angle is less than -70 degrees, the phase-frequency factor is 0; the factor value can represent the sticky feel of the steering wheel, and when the corresponding factor value is greater than 0.6, it indicates that the sticky feel under the parameter is qualified, otherwise the sticky feel is unqualified, and the phase-frequency factor value can be changed according to requirements, and the weights of torque fluctuation and sticky feel are set. Two weights are set according to people's subjective feelings. If more attention is paid to torque fluctuation, w1 is larger; if more attention is paid to the sticky feel, w2 is larger, and w1+w2=1 is satisfied when setting the weights.

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