Road spectrum time domain signal variable gain inverse model simulation control method and system

Abstract

The invention discloses a road spectrum time domain signal variable gain inverse model simulation control method and system, and relates to the field of automatic control. The problems that in the existing control field, the control process is slow and the labor cost is increased due to the technical defects that an inverse model is not updated, gain is not updated and an algorithm cannot be automatically adjusted according to test types are solved. The method comprises the following steps: respectively carrying out Fourier transform on a road spectrum instruction signal in a road spectrum time domain signal and an acquired automobile response signal, and solving a frequency spectrum difference value; an excitation signal required for generating a gain value and inverse model correction is calculated, the excitation signal is applied to the system, an output response signal of an automobile acceleration sensor is collected, and an updated automobile response signal is obtained through Fourier transform; and carrying out characteristic correction based on the automobile response signal, collecting current state information, calculating a numerical value of an excitation signal, carrying out inverse Fourier transform, and converting the numerical value into a road spectrum time domain signal for driving a test platform. The method is also suitable for the automobile fatigue test field.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a method and system for simulating control of a variable gain inverse model of a spectrum time domain signal. Background Art

[0002] In the field of control, especially in the fields of vehicle dynamics, tire performance evaluation, suspension system design, etc., it is a common requirement to simulate the road spectrum data collected on the actual road on the relevant test equipment in the laboratory. This simulation can help engineers test and optimize the performance of vehicles or components in a controlled environment without having to conduct a large number of tests on the actual road. However, there are some problems and challenges with this simulation:

[0003] First, in terms of data accuracy, actual road conditions are very complex, including different road materials (such as asphalt, concrete, gravel, etc.), temperature, humidity, wear and tear, etc. These factors will affect the collection of road spectrum data. It is very difficult to simulate these complex environmental conditions in the laboratory, so the simulated data may not fully reflect the actual situation.

[0004] Secondly, due to equipment limitations, the test equipment in the laboratory, such as the 4WD Chassis Dynamometer or tire tester, may not be able to fully replicate the dynamic characteristics of the actual road. For example, the unevenness of the road surface and the change of lateral force may be difficult to simulate accurately. 3. Adaptability of the control system: Vehicle control systems (such as electronic stability program ESP, anti-lock braking system ABS, etc.) are usually adjusted according to actual driving conditions. In laboratory simulations, these control systems may not be able to correctly respond to the simulated road spectrum data. Due to the large mass of the car, most of the laboratory test equipment is hydraulically driven test equipment. The electromechanical system, especially the hydraulic servo system, has a relatively small damping, which leads to limited control effects of conventional control methods, and the system response lags behind the given road spectrum signal. At the same time, there are factors such as strong coupling, nonlinearity and parameter uncertainty in the system of the test equipment. It is difficult to make the system accurately output the given road spectrum signal using only the conventional PID algorithm, so a correction compensation loop is added outside the servo control loop. Here, correction compensation is an inverse model control technology based on the characteristics of the servo system. This technology obtains the input-output response of the system and continuously corrects the excitation signal to enable the system to accurately simulate the output spectrum time domain signal.

[0005] And there are the following problems in the correction and compensation process:

[0006] First, the control is offline, not online, which increases labor costs and prolongs the test time.

[0007] Second, in the control algorithm, the gain is a constant value or is divided into several frequency bands to give several different gain values.

[0008] The gain is difficult to adjust, and the parameters need to be adjusted manually repeatedly to find the adaptive value. However, after the gain is adjusted, since the gain is a fixed value, the gain value is small to ensure the stability of the process, which increases the number of corrections and prolongs the test time. Even if the frequency band of the spectrum signal is divided into several high, medium and low frequency bands according to the process characteristics, and a different gain value is given to each frequency band, the problem cannot be fundamentally solved.

[0009] Third, in the control algorithm, the inverse model is not updated.

[0010] The response characteristics of the test equipment will change continuously due to the influence of time-varying factors. If a fixed inverse model is used, there will always be errors with the vehicle response, and as the test proceeds, the errors will continue to accumulate and amplify, the accuracy will not meet the indicators, and even the system will diverge.

[0011] Fourth, it is not suitable for different test types.

[0012] In automobile road spectrum testing, in order to simulate high-frequency signals, the control cycle in the test algorithm is getting smaller and smaller. In addition, the algorithm has a large amount of calculation. It is necessary to automatically adjust the algorithm type according to different test requirements to meet different test types.

[0013] Creating a test environment that accurately simulates actual road spectra can require expensive investments, and setting up and maintaining such a system also takes a lot of time. In addition, each test requires reconfiguring and calibrating the equipment, which can lead to increased testing costs and reduced efficiency.

[0014] Real-time simulation of actual road data requires high-speed data processing capabilities and precise control systems. If the laboratory equipment does not respond fast enough or the control system is not precise enough, the simulation results may deviate significantly from the actual situation.

[0015] Therefore, as an important part of the performance test of new energy vehicles and other new models, road simulation testing in the laboratory is a key task. How to simulate the road spectrum data collected on the actual road on the relevant test equipment in the laboratory is a technology that must be mastered in the control field. Summary of the invention

[0016] The present invention aims to solve the problems of slow control process, extended test time, increased labor cost, system instability, etc. caused by technical deficiencies in the prior art such as non-online control, inverse model not updated, gain not updated, and algorithm not automatically adjusted according to test type. To solve the above technical problems, the present invention is implemented by the following technical solutions:

[0017] Solution 1: The present invention proposes a method for simulating and controlling a time-domain signal of a road spectrum with a variable gain inverse model, the method comprising the following steps:

[0018] S1, respectively performing Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference;

[0019] S2, update the value of the gain according to the current system state, calculate the value of the gain correction and the excitation signal required for the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transform;

[0020] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects current state information, updates the state information, calculates the value of the excitation signal, and performs inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0021] Further, a preferred implementation manner is provided, in which the method of performing Fourier transform on the road spectrum command signal and the collected vehicle response signal in the time domain signal in S1 is:

[0022] S1.1. Determine the response characteristics of the system;

[0023] S1.2, based on the response characteristics determined in S1, obtain the initial estimate G0(f) of the inverse model;

[0024] S1.3. Perform FFT transformation on the road spectrum time domain command signal to obtain the frequency spectrum of the road spectrum command. The initial excitation signal is J1(f)=G0(f)R(f). The excitation signal J1(f) is converted from a frequency domain signal to a time domain signal through IFFT to excite the system.

[0025] Further, a preferred implementation is provided, in which the method of performing numerical update according to the gain of the current system state in S2 to generate the gain value and the excitation signal required for inverse model correction is:

[0026]

[0027] Where D n (f) is the driving spectrum generated in the previous iteration process.

[0028] Further, a preferred implementation is provided, in which the inverse model correction method described in S2 is:

[0029]

[0030] Where R(f) is the spectrum of the road spectrum command signal.

[0031] Further, a preferred embodiment is provided, in which the method for correcting the characteristics of the vehicle response signal obtained based on the updated Fourier transform in S2 in S3 is:

[0032]

[0033] In the formula, N n (f) is the response spectrum deviation, M n (f) is the driving spectrum deviation.

[0034] Further, a preferred implementation is provided, in which the method for calculating the excitation signal required for inverse model correction in S2 is:

[0035]

[0036] Further, a preferred embodiment is provided, wherein the state information update in S3 includes performing gain correction and inverse model correction simultaneously in the same cycle; the gain correction and inverse model correction are performed in their respective correction cycles, that is, the gain correction is performed first; the excitation signal after the gain correction is used to excite the system to generate an output response, update the information The corrected excitation signal Prediction generation, i.e. steps.

[0037] Solution 2: A road spectrum time domain signal variable gain inverse model simulation control system, the system comprising:

[0038] The state information updating module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference;

[0039] The excitation signal update module is used to perform numerical updates according to the gain of the current system state, calculate the excitation signal required for the gain correction and the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transformation;

[0040] The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect the current state information, update the state information, calculate the value of the excitation signal, and perform inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0041] Solution 3: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution 1.

[0042] Solution 4: A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of Solution 1 are implemented.

[0043] The present invention is beneficial in that:

[0044] The road spectrum time domain signal variable gain inverse model simulation control method described in the present invention solves the problems of slow control process, extended test time, increased labor cost, system instability, etc. caused by technical deficiencies in the existing methods such as non-online, no inverse model update, no gain update, and the algorithm cannot be automatically adjusted according to the test type. That is, safety is improved, and simulating actual road conditions in a laboratory environment can avoid safety risks that may occur when testing on real roads, especially when performing extreme driving or fault simulation.

[0045] The method of the present invention reduces the need for expensive field testing through laboratory simulation, saving fuel, manpower and time costs. In addition, it can also reduce wear and tear on vehicles and test equipment.

[0046] The method described in the present invention provides highly repeatable test conditions through laboratory simulation, which means that the test can be run multiple times under the same conditions to verify the consistency and reliability of the results.

[0047] The method described in the present invention reduces the environmental impact through laboratory simulations as it does not require testing on real roads, thereby reducing emissions and noise pollution.

[0048] The present invention is also applicable to the field of automobile fatigue testing, as well as fatigue loading and performance testing of equipment in other fields such as transportation, aerospace, and civil engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a diagram of the automobile road spectrum test equipment described in Implementation Example 11.

[0050] Figure 2 This is a schematic diagram of the simulation control principle of the spectrum signal on the test platform as described in Implementation Example 11.

[0051] Figure 3 This is a control flow chart of a method for simulating control of a road spectrum time domain signal variable gain inverse model as described in Implementation Method 1.

[0052] Figure 4 This is a schematic diagram of the time domain signal flow of the road spectrum described in the eleventh implementation mode.

[0053] Figure 1 In the figure, car 1, actuator 2, test platform 3. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0055] Implementation method 1: This implementation method provides a method for simulating and controlling a time-domain signal of a road spectrum with a variable gain inverse model, the method comprising the following steps:

[0056] S1, respectively performing Fourier transform on the road spectrum command signal and the collected vehicle response signal in the road spectrum time domain signal to obtain the spectrum difference;

[0057] S2, perform numerical update according to the gain of the current system state, calculate and generate the gain value and the excitation signal required for inverse model correction, apply the excitation signal to the system, collect the output response signal of the vehicle acceleration sensor, and obtain the updated vehicle response signal through Fourier transform;

[0058] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects current state information, calculates the value of the excitation signal, and performs inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0059] Implementation method 2: This implementation method is a further limitation of the road spectrum time domain signal variable gain inverse model simulation control method described in implementation method 1. S1: Fourier transform the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor respectively to obtain the spectrum difference;

[0060] S2, update the value of the gain according to the current system state, calculate the value of the gain correction and the excitation signal required for the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transform;

[0061] S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects current state information, updates the state information, calculates the value of the excitation signal, and performs inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0062] Implementation method 3: This implementation method is a further limitation of the road spectrum time domain signal variable gain inverse model simulation control method described in implementation method 1. In S2, the gain of the current system state is numerically updated to generate the gain value and the excitation signal required for inverse model correction as follows:

[0063]

[0064] Where D n (f) is the driving spectrum generated in the previous iteration process.

[0065] Implementation method 4: This implementation method further limits the road spectrum time domain signal variable gain inverse model simulation control method described in implementation method 1. The inverse model correction method described in S2 is:

[0066]

[0067] Where R(f) is the spectrum of the road spectrum command signal.

[0068] Implementation mode 5: This implementation mode further limits the road spectrum time domain signal variable gain inverse model simulation control method described in implementation mode 1. The method for correcting the characteristics of the vehicle response signal obtained based on the leaf transformation updated in S2 in S3 is:

[0069]

[0070] In the formula, N n (f) is the response spectrum deviation, M n (f) is the driving spectrum deviation.

[0071] Implementation method 6: This implementation method further limits the road spectrum time domain signal variable gain inverse model simulation control method described in implementation method 1. The method for calculating the excitation signal required for inverse model correction in S2 is:

[0072]

[0073] Implementation method 7. This implementation method is a further limitation of the road spectrum time domain signal variable gain inverse model simulation control method described in implementation method 1. The state information update described in S3 includes gain correction and inverse model correction being performed simultaneously in the same cycle; gain correction and inverse model correction are performed in their respective correction cycles, that is, gain correction is performed first; the excitation signal after gain correction is used to excite the system to generate an output response and update information. The corrected excitation signal Prediction generation, i.e. steps.

[0074] Embodiment 8: This embodiment proposes a road spectrum time domain signal variable gain inverse model simulation control system, the system comprising:

[0075] The state information updating module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference;

[0076] The excitation signal update module is used to perform numerical updates according to the gain of the current system state, calculate the excitation signal required for the gain correction and the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transformation;

[0077] The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect the current state information, update the state information, calculate the value of the excitation signal, and perform inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

[0078] Embodiment 9. This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the method described in any one of embodiments 1 to 7.

[0079] Embodiment 10: This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any one of embodiments 1 to 7 are implemented.

[0080] Implementation eleven: This implementation provides an example, which is used to explain the above implementations one to eight. The specific example is as follows:

[0081] See also Figures 1 to 4 To explain this embodiment, Figure 1 The car is fixed on the test platform, which is a mechanical device driven by multiple hydraulic actuators. During the test, several acceleration sensors are placed at appropriate positions on the car to test the car's response under road spectrum signals.

[0082] See also Figure 2 As shown in the figure, the complete road spectrum time domain signal online control process includes two parts. The first part is the state information update, which mainly includes the road spectrum error signal calculation, gain correction and inverse model correction. The road spectrum error signal is obtained by Fourier transforming (FFT) the road spectrum command signal in the time domain signal and the collected vehicle response signal respectively to obtain the spectrum difference; the gain correction is mainly to numerically update the gain in the control process according to the current system state; the inverse model correction is mainly to correct the characteristics of the current system state. The second part is the excitation signal update, which calculates the latest value of the excitation signal through the collection of the current state information, and then performs an inverse Fourier transform (IFFT) to convert it into a time domain signal to drive the test platform.

[0083] Figure 3 Flow chart of the control shown in; Figure 4In the control process shown in , the specific trends and relationships of various signals are shown, where the two rectangular dotted boxes represent the same controlled object.

[0084] First, the initial conditions are:

[0085] First, the response characteristics of the system are identified by the H1 method through white powder noise, and then the initial estimate G of the inverse model is obtained based on the response characteristics. 0 (f) According to the specific situation, the initial value of the gain α0 is given. The initial value can be selected in the range of 0.1 to 0.2 (considering stability) or 0.4 to 0.5 (considering rapidity).

[0086] Perform FFT transformation on the road spectrum time domain command signal to obtain the spectrum R(f) of the road spectrum command. Then the initial excitation signal is J1(f)=G0(f)R(f). The excitation signal J1(f) is converted from a frequency domain signal to a time domain signal through IFFT to excite the system.

[0087] Secondly, status information update:

[0088] Here, the control algorithm is divided into three working conditions according to the different test types. Figure 3 As shown in . Now take working condition 1 as an example to explain.

[0089] For the nth iteration process, the nth frequency domain signal J n (f) After IFFT transformation, the time domain signal is converted into the test platform to excite the corresponding vehicle acceleration output response signal Z n (t), transformed into Z by FFT n (f) is compared with the frequency domain signal of the road spectrum to form a frequency domain error signal E n (f), that is, E n (f) = R(f) - Z n (f).

[0090] Generates the gain values ​​and excitation signals required for inverse model correction.

[0091]

[0092] Apply the excitation signal In the system. That is, through IFFT Convert into a time domain signal to excite the system.

[0093] Collect the response signal output by the vehicle acceleration sensor at this time, and obtain the updated response signal through FFT transformation

[0094] Gain value correction. By the car response signal Z n(f) and the Hermitian transpose of the path spectrum instruction R(f) are used to modify the gain value.

[0095]

[0096] Inverse model correction. Based on the previous step inverse model G n (f), the previous step incentive J n (f) Update the excitation signal Collecting vehicle response signals Last response signal Z n (f) Jointly modify the inverse model.

[0097]

[0098] G n+1 (f) In the expression,

[0099] In terms of stimulus signal update:

[0100] Calculate the excitation signal J n+1 times n+1 (f)

[0101]

[0102] Apply the excitation signal J n+1 (f) in the system. That is, J n+1 (f) Convert to time domain signal excitation system

[0103] Collect the response signal output by the vehicle acceleration sensor at this time, and obtain the updated response signal Z through FFT transformation n+1 (f).

[0104] In summary, each correction is completed in two stages, namely, formulas (1) to (4). In the first stage, the test platform is stimulated by the current excitation signal to generate the vehicle acceleration response signal, formula (1), based on which the gain and inverse model are corrected, namely, formulas (2) to (3). The second stage is to update the excitation signal and generate the next corrected excitation signal, namely, formula (4). In actual tests, some frequency bands or frequency points may meet the technical indicators, then these frequency bands or points are windowed, the last excitation value is maintained, and only other frequency bands or points are corrected.

[0105] In the status information update, there are three working condition algorithms: Working condition one, gain correction and inverse model correction are performed simultaneously in the same cycle, which is suitable for tests with short test control cycle requirements and good controller hardware, and can give accurate vehicle fatigue test results; Working condition two, gain correction and inverse model correction are performed in their respective correction cycles, that is, gain correction is performed first, and the excitation signal after gain correction is used to stimulate the system to produce an output response. Then use this response to perform inverse model correction. In this way, gain correction and impedance correction are performed sequentially, and the two are independent to avoid mutual influence, but the number of times the system is excited increases. It is suitable for test equipment that has no requirements for the total test time and has general controller hardware performance; Working condition three, update information Not by the corrected stimulus signal The stimulus test platform is collected from the vehicle acceleration, but the stimulus signal is corrected Prediction generation, i.e. It is suitable for tests that have requirements on the total test time but not on the control accuracy, and can quickly give approximate test results.

[0106] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0107] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for simulating and controlling a variable gain inverse model of a road spectrum time domain signal, characterized in that: The method comprises the following steps: S1, respectively performing Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference; S2, update the value of the gain according to the current system state, calculate the value of the gain correction and the excitation signal required for the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transform; S3, based on the vehicle response signal obtained by the updated Fourier transform in S2, performs characteristic correction, collects current state information, updates the state information, calculates the value of the excitation signal, and performs inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

2. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1 is characterized in that: The method of performing Fourier transform on the road spectrum command signal and the collected vehicle response signal in the time domain signal in S1 is: S1.

1. Determine the response characteristics of the system; S1.2, based on the response characteristics determined in S1, obtain the initial estimate G0(f) of the inverse model; S1.

3. Perform FFT transformation on the road spectrum time domain command signal to obtain the frequency spectrum of the road spectrum command. The initial excitation signal is J1(f)=G0(f)R(f). The excitation signal J1(f) is converted from a frequency domain signal to a time domain signal through IFFT to excite the system.

3. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1 is characterized in that: In S2, the gain of the current system state is numerically updated to generate the gain value and the excitation signal required for inverse model correction as follows: Where D n (f) is the driving spectrum generated in the previous iteration process.

4. The method for simulating and controlling the variable gain inverse model of the time-domain signal of the road spectrum according to claim 1 is characterized in that: The inverse model correction method described in S2 is: Where R(f) is the spectrum of the road spectrum command signal.

5. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1 is characterized in that: The method for correcting the characteristics of the vehicle response signal obtained based on the updated Fourier transform of S2 in S3 is: In the formula, N n (f) is the response spectrum deviation, M n (f) is the driving spectrum deviation.

6. The method for simulating and controlling the time-domain signal gain inverse model of the road spectrum according to claim 1 is characterized in that: The method for calculating the excitation signal required for inverse model correction in S2 is:

7. The method for simulating and controlling the time-domain signal variable gain inverse model of the road spectrum according to claim 1 is characterized in that: The state information update described in S3 includes performing gain correction and inverse model correction simultaneously in the same cycle; the gain correction and inverse model correction are performed in their respective correction cycles, that is, the gain correction is performed first; the excitation signal after the gain correction is used to excite the system to generate an output response and update the information The corrected excitation signal Prediction generation, i.e. steps.

8. A road spectrum time domain signal variable gain inverse model simulation control system, characterized in that: The system comprises: The state information updating module is used to perform Fourier transform on the road spectrum command signal in the road spectrum time domain signal and the vehicle response signal collected by the sensor to obtain the spectrum difference; The excitation signal update module is used to perform numerical updates according to the gain of the current system state, calculate the excitation signal required for the gain correction and the inverse model correction, apply the excitation signal to the system, and output the response signal, and obtain the updated vehicle response signal through Fourier transformation; The driving module is used to perform characteristic correction on the vehicle response signal obtained based on the updated Fourier transform of the excitation signal update module, collect the current state information, update the state information, calculate the value of the excitation signal, and perform inverse Fourier transform to convert it into a road spectrum time domain signal for driving the test platform.

9. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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