Speed control method, device and equipment based on electric tail gate and medium

Abstract

The invention discloses a speed control method, device and equipment based on an electric tail gate and a medium, and relates to the technical field of automobile tail gate control. Acquiring a system disturbance parameter and a corresponding disturbance derivative; inputting the system disturbance parameter and the disturbance derivative as state variables into an extended state observer of the stay bar motor to predict system disturbance; and adjusting the input torque of the stay bar motor according to the system disturbance so as to control the rotating speed output of the stay bar motor. According to the method, the system disturbance parameters and the corresponding disturbance derivatives serve as the state variables of the extended state observer, then the disturbance change trend behind the disturbance derivatives is subjected to state quantity input, while the system disturbance is counteracted in advance, the change rate of the disturbance is concerned, and the rotating speed control accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile tailgate control, and in particular to a speed control method, device, equipment and medium based on an electric tailgate. Background Art

[0002] Conventional electric tailgate opening and closing uses a second-order extended state observer, combined with relevant feedback control laws, to achieve closed-loop feedback control of the system. Compared with traditional proportional-integral-derivative (PID) feedback control, this method avoids large-scale vibrations and responds quickly during speed regulation. However, due to the use of the total system disturbance parameter, the accuracy of speed control is reduced to a certain extent.

[0003] Therefore, how to improve the accuracy of speed control is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] The object of the present invention is to provide a speed control method, device, equipment and medium based on an electric tailgate to solve the problem of reduced accuracy of speed control.

[0005] To solve the above technical problems, the present invention provides a speed control method based on an electric tailgate, comprising:

[0006] Obtain system disturbance parameters and corresponding disturbance derivatives;

[0007] The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance;

[0008] The input torque of the strut motor is adjusted according to the system disturbance to control the speed output of the strut motor.

[0009] On the one hand, the system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including:

[0010] Derivative the system disturbance parameter once to obtain a disturbance derivative;

[0011] The system disturbance parameter and the disturbance derivative are respectively used as the first state variable and the second state variable, and input into the extended state observer of the strut motor to predict the system disturbance; wherein the system disturbance parameter is the total load torque.

[0012] On the other hand, the system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including:

[0013] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0014] Derivatives are taken of the first disturbance parameter, the second disturbance parameter, the third disturbance parameter, and the fourth disturbance parameter to obtain a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative;

[0015] A first disturbance parameter, a second disturbance parameter, a third disturbance parameter, a fourth disturbance parameter, a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative are respectively used as state variables and input into an extended state observer of the strut motor to predict system disturbances; wherein the system disturbance parameter is the total load torque.

[0016] On the other hand, the system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including:

[0017] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0018] Set the corresponding disturbance impact factor for each disturbance parameter;

[0019] Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters;

[0020] Derivative the target disturbance parameter multiple times to obtain a target first disturbance derivative;

[0021] Performing a derivation on the disturbance parameters other than the target disturbance parameter to obtain a second disturbance derivative;

[0022] The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter, the target first disturbance derivative and the second disturbance derivative are respectively used as state variables and input into the extended state observer of the strut motor to predict the system disturbance.

[0023] On the other hand, the system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including:

[0024] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0025] Set the corresponding disturbance impact factor for each disturbance parameter;

[0026] Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters;

[0027] Derivative the target disturbance parameter once to obtain a target second disturbance derivative;

[0028] The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter and the target second disturbance derivative are respectively used as state variables and input into the extended state observer of the strut motor to predict the system disturbance.

[0029] On the other hand, after adjusting the input torque of the strut motor according to the system disturbance, the method further includes:

[0030] Get the operating parameters of the strut motor;

[0031] Calling artificial intelligence models;

[0032] inputting the operating parameters into an artificial intelligence model to output a first input torque;

[0033] comparing the first input torque with the adjusted input torque;

[0034] If the difference between the first input torque and the adjusted input torque is within a preset range, the adjusted input torque is used to control the output speed of the strut motor.

[0035] On the other hand, if the difference between the first input torque and the adjusted input torque is not within a preset range, the method further includes:

[0036] Performing average processing on the first input torque and the adjusted input torque to obtain a second input torque;

[0037] The strut motor speed output is controlled according to the second input torque.

[0038] In order to solve the above technical problems, the present invention further provides a speed control device based on an electric tailgate, comprising:

[0039] An acquisition module is used to obtain system disturbance parameters and corresponding disturbance derivatives;

[0040] A prediction module is used for inputting system disturbance parameters and disturbance derivatives as state variables into an extended state observer of the strut motor to predict system disturbances;

[0041] The control module is used to adjust the input torque of the strut motor according to the system disturbance to control the speed output of the strut motor.

[0042] In order to solve the above technical problems, the present invention further provides a speed control device based on an electric tailgate, comprising:

[0043] memory for storing computer programs;

[0044] A processor is configured to implement the steps of the speed control method based on an electric tailgate when executing the computer program.

[0045] To solve the above technical problems, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the speed control method based on the electric tailgate are implemented.

[0046] The present invention provides a speed control method for an electric tailgate. The method obtains system disturbance parameters and their corresponding disturbance derivatives; uses these as state variables and inputs them into an extended state observer (ESO) for a strut motor to predict system disturbances; and adjusts the input torque of the strut motor based on the system disturbances to control the strut motor's output speed. By using the system disturbance parameters and their corresponding disturbance derivatives as state variables for the ESO and then incorporating the disturbance change trend underlying the disturbance derivatives as state variables, the method achieves early cancellation of system disturbances while focusing on the rate of change of the disturbance, thereby improving the accuracy of speed control.

[0047] In addition, the present invention also provides a speed control device based on an electric tailgate, a speed control apparatus based on an electric tailgate, and a computer-readable storage medium, which have the same beneficial effects as the above-mentioned speed control method based on an electric tailgate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A flow chart of a speed control method based on an electric tailgate provided in an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of feedback control of a third-order ESO provided by an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of conventional PID feedback control;

[0052] Figure 4 A structural diagram of a speed control device based on an electric tailgate provided by an embodiment of the present invention;

[0053] Figure 5 This is a structural diagram of a speed control device based on an electric tailgate provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] The core of the present invention is to provide a speed control method, device, equipment and medium based on an electric tailgate to solve the problem of reduced accuracy of speed control.

[0056] In order to enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0057] The opening and closing of an electric tailgate is accomplished by driving a strut motor. Different target drive speeds are set throughout the tailgate's range of motion. Conventional solutions employ a second-order extended state observer for real-time observation and compensation to improve system responsiveness and disturbance immunity, but this still presents the problem of low speed accuracy. The speed control method for an electric tailgate provided by this invention addresses this technical issue by incorporating the derivative of the system disturbance as a state variable.

[0058] Figure 1 A flow chart of a speed control method based on an electric tailgate is provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0059] S11: Obtain system disturbance parameters and corresponding disturbance derivatives;

[0060] S12: The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance;

[0061] S13: Adjust the input torque of the strut motor according to the system disturbance to control the speed output of the strut motor.

[0062] Specifically, the system disturbance parameters in step S11 take into account the existence of uncertainties such as system disturbances and model deviations in the strut motor system. In this embodiment, these uncertainties can be processed separately as state variables, or these uncertainties can be processed as a total disturbance parameter. The purpose here is to use them as observable and controllable variables so that they can be effectively modeled and compensated. By using them as control states, we can better understand the interference conditions of the system and take corresponding control measures to reduce the impact on system performance.

[0063] The disturbance derivative is designed as another control state, and its derivative can reflect the changing trend of the disturbance to improve the modeling accuracy of the system disturbance.

[0064] It should be noted that the perturbation derivative in this embodiment can be a first-order derivative or a multi-order derivative, which is not limited here and can be set according to actual circumstances. Regarding the setting of multi-order derivatives, of course, more orders will increase the accuracy, but the actual computing resources and computing speed must also be considered to achieve a trade-off.

[0065] Regarding the system disturbance parameters in this embodiment, in the process of setting various disturbance parameters, the corresponding disturbance derivative can be the derivative of one disturbance parameter, or the derivative of all or part of the disturbance parameters. There is no limitation here and it can be set according to actual conditions.

[0066] The construction process of the expansion state observer of the strut motor in step S12 is as follows:

[0067] 1. Establish the state space equation of the strut motor system:

[0068] According to the kinematic equation of the strut motor:

[0069] ;

[0070] Among them, w m is the angular velocity of the strut motor, J is the moment of inertia, B is the damping coefficient, T e is the input torque, T L is the total load torque, including friction resistance, wind resistance, slope and model error, and is a nonlinear function of parameters such as friction coefficient and slope.

[0071] The total load torque here can be summarized as a total system disturbance by its uncertainty factors. If it is divided into multiple disturbance parameters, T L It is refined into T1, T2, T3, and T4, where T1 is load change, T2 is model error, T3 is temperature change, and T4 is friction resistance and wind resistance.

[0072] The above-mentioned refined formula can be:

[0073] .

[0074] In some embodiments, taking a system disturbance parameter, i.e., a total disturbance parameter, as an example, the system disturbance parameter and a disturbance derivative are input as state variables into an extended state observer of the strut motor to predict the system disturbance, including:

[0075] Derivative the system disturbance parameter once to obtain the disturbance derivative;

[0076] The system disturbance parameter and the disturbance derivative are respectively used as the first state variable and the second state variable, and are input into the extended state observer of the strut motor to predict the system disturbance; wherein the system disturbance parameter is the total load torque.

[0077] In this embodiment, the construction process of the corresponding expansion state observer of the strut motor is as follows:

[0078] Let: x1=W m , x2=f, x3= , the derivative of x3 = h, Te = u, then the expanded state equation of the system is:

[0079] ;

[0080] in, ;

[0081] Assume that the estimated value of the state variable x is , the estimated value of the output y is , the observer gain matrix is , the extended state observer ESO is:

[0082] ;

[0083] If L is configured so that the eigenvalues of the A-LC matrix are in the left half of the complex plane, then the state observer is stable.

[0084] In this embodiment, the total system disturbance parameter and the disturbance derivative are respectively used as the first state variable (x2 in the above formula) and the second state variable (x3 in the above formula), and the remaining state variable (x1 in the above formula) is the angular velocity of the strut motor.

[0085] This embodiment provides a method of using a total disturbance parameter as a system disturbance parameter, taking the disturbance derivative obtained by first derivative as a state variable, and adding the change trend corresponding to the disturbance derivative observation to effectively compensate for the total disturbance of the system and reduce the impact on system performance.

[0086] In some embodiments, the system disturbance parameters are divided into refined disturbance parameters, and the system disturbance parameters and disturbance derivatives are used as state variables and input into an extended state observer of the strut motor to predict the system disturbance, including:

[0087] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0088] Derivatives are taken of the first disturbance parameter, the second disturbance parameter, the third disturbance parameter, and the fourth disturbance parameter to obtain a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative;

[0089] A first disturbance parameter, a second disturbance parameter, a third disturbance parameter, a fourth disturbance parameter, a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative are respectively used as state variables and input into an extended state observer of the strut motor to predict system disturbances; wherein the system disturbance parameter is the total load torque.

[0090] Specifically, let x1=W m , x2=T1=f1, x3=T2=f2, x4=T3=f3, x5=T4=f3, x6=derivative of f1, x7=derivative of f2, x8=derivative of f3, x9=derivative of f4, let the derivative of x6 be h, the derivative of x7 be j, the derivative of x8 be k, and the derivative of x9 be m. Then the expanded state equation of the system is:

[0091] ;

[0092] in:

[0093] ;

[0094] Assume that the estimated value of the state variable x is ; The estimated value of the output y is , the observer gain matrix is , then the extended state observer ESO is:

[0095] ;

[0096] If L is configured so that the eigenvalues of the A-LC matrix are in the left half of the complex plane, then the state observer is stable.

[0097] The method provided in this embodiment is to refine a system disturbance parameter into four disturbance parameters, and take the derivative of each of them once to obtain the corresponding disturbance derivative as the corresponding state variable, so that the corresponding change trend of each disturbance parameter is observed at present to achieve effective compensation and improve prediction accuracy.

[0098] According to the predicted system disturbance, the input torque of the strut motor is adjusted, that is, Te is precisely controlled, thereby controlling W m The precise output of the motor speed control.

[0099] Figure 2 A schematic diagram of feedback control of a third-order ESO provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the uncertain factors are summarized as a system disturbance parameter. According to the motor kinematic equation, the motor is driven by the input torque T e Control angular velocity W mThe output of the motor is used to estimate the total disturbance of the system in advance and accurately, and to feed it back to the system control in time. The feedback control based on the motor motion model, that is, the feedback control law, continuously adjusts the angular velocity W through the first-order proportional control. m Approaching the target angular velocity W ref .

[0100] The PID control scheme is usually adopted. Figure 3 This is a conventional PID feedback control diagram, such as Figure 3 As shown, the input of the PID controller is the deviation between the actual speed and the target speed, and the output is the proportional control parameter K p , integral control parameter K i And the differential control parameter K d , and then adjust the actual speed toward the target speed.

[0101] The proportional control in PID can meet the requirements of closed-loop feedback, so:

[0102] ;

[0103] From this we can get:

[0104] ;

[0105] Where W ref is the system set speed, and f is the system disturbance estimated by the system.

[0106] Combined with the above formula, the input torque of the strut motor is adjusted according to the system disturbance to control the speed output of the strut motor.

[0107] An embodiment of the present invention provides a speed control method for an electric tailgate. The method obtains system disturbance parameters and their corresponding disturbance derivatives; uses these as state variables and inputs them into an extended state observer (ESO) for a strut motor to predict system disturbances; and adjusts the input torque of the strut motor based on the system disturbances to control the strut motor's output speed. By using the system disturbance parameters and their corresponding disturbance derivatives as state variables for the ESO and then incorporating the disturbance change trend underlying the disturbance derivatives as state variables, the method achieves early cancellation of system disturbances while focusing on the rate of change of the disturbance, thereby improving the accuracy of speed control.

[0108] In some embodiments, the system disturbance parameters and disturbance derivatives are input as state variables to an extended state observer of the strut motor to predict the system disturbance, including:

[0109] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0110] Set the corresponding disturbance impact factor for each disturbance parameter;

[0111] Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters;

[0112] The target disturbance parameter is differentiated multiple times to obtain the target first disturbance derivative;

[0113] Perform a derivation on the disturbance parameters other than the target disturbance parameter to obtain the second disturbance derivative;

[0114] The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter, the target first disturbance derivative and the second disturbance derivative are respectively used as state variables and input into the extended state observer of the strut motor to predict the system disturbance.

[0115] In conjunction with the above embodiment, the derivative of the same order of each refined disturbance parameter is taken as the disturbance derivative. On the basis of the accuracy of the corresponding observation, in order to further improve the accuracy of the observation, it is necessary to continue to derive the disturbance parameter with a larger disturbance effect, that is, to achieve multiple derivatives to obtain the target first disturbance derivative. The order of the multiple derivatives here is not limited and can be set according to the actual situation. In this embodiment, the target disturbance parameter corresponding to the larger influence factor is the target disturbance parameter corresponding to the target disturbance influence factor corresponding to the preset disturbance influence factor, which corresponds to the target first disturbance derivative obtained by multiple derivatives.

[0116] This embodiment provides multiple derivatives of disturbance parameters with greater disturbance influence, and single derivatives of disturbance parameters with smaller disturbance influence, all of which are used as state variables to further differentiate the derivatives based on the change trend corresponding to each disturbance parameter observation to improve accuracy.

[0117] In some other embodiments, the system disturbance parameters and disturbance derivatives are input as state variables to an extended state observer of the strut motor to predict the system disturbance, including:

[0118] Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter;

[0119] Set the corresponding disturbance impact factor for each disturbance parameter;

[0120] Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters;

[0121] Derivative the target disturbance parameter once to obtain the target second disturbance derivative;

[0122] The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter and the target second disturbance derivative are respectively used as state variables and input into the extended state observer of the strut motor to predict the system disturbance.

[0123] Specifically, the method of obtaining the target disturbance parameter is the same as in the above embodiment, but considering that the derivation process occupies certain computing resources, in this embodiment, the target disturbance parameter is only derived once to obtain the target second disturbance derivative, which is used as a state variable to predict the system disturbance.

[0124] This embodiment provides a method of taking the derivative of the disturbance parameter with a greater disturbance effect, and not taking the derivative of the disturbance parameter with a smaller disturbance effect. As a state variable, this method saves computing resources and improves computing speed when the change trend corresponding to the observation of the disturbance parameter with a greater disturbance effect can be determined.

[0125] Considering that the current input torque is obtained through the state observer, in order to improve the accuracy of the speed adjustment, in some embodiments, after adjusting the input torque of the strut motor according to the system disturbance, the method further includes:

[0126] Get the operating parameters of the strut motor;

[0127] Calling artificial intelligence models;

[0128] inputting the operating parameters into the artificial intelligence model to output a first input torque;

[0129] comparing the first input torque and the adjusted input torque;

[0130] If the difference between the first input torque and the adjusted input torque is within a preset range, the adjusted input torque is used to control the output speed of the strut motor.

[0131] Specifically, AI models, without specific limitations, can include deep learning model predictions, Model Predictive Control (MPC) combined with Artificial Intelligence (AI) technology to predict torque demand, or embedded AI solutions that run AI models directly on the motor control unit (MCU), thus monitoring and predicting torque without the need for additional sensors. Examples of deep learning models include recurrent neural networks (RNNs), long short-term memory (LSTMs), or encoder-decoder architectures.

[0132] Input parameters during motor operation, namely, operating parameters of the strut motor (e.g., voltage, current, speed, etc.), are collected and input into the invoked artificial intelligence model to output a first input torque. The first input torque is compared with the adjusted input torque obtained by the state observer in the above-described embodiment. If the difference is within a preset range, indicating that the adjusted input torque obtained by the state observer is highly accurate, the adjusted input torque is directly used to control the output speed of the strut motor.

[0133] It should be noted that the training of the deep learning model framework in this embodiment requires data collection of the input parameters (voltage, current, speed, etc.) and the actual measured torque during the operation of the motor. The deep learning framework is used to build and train the model, and the operating parameters of the strut motor are used as features and the torque is used as the target output to obtain a trained model. The model is deployed in the motor control system, and the operating data is input in real time to predict the first input torque.

[0134] This embodiment provides a method of using the first input torque output by the artificial intelligence model to verify the accuracy of the adjusted input torque obtained by the state observer in the above embodiment, thereby improving the accuracy of subsequent speed regulation.

[0135] In some embodiments, if the difference between the first input torque and the adjusted input torque is not within a preset range, the method further includes:

[0136] Performing average processing on the first input torque and the adjusted input torque to obtain a second input torque;

[0137] The speed output of the strut motor is controlled according to the second input torque.

[0138] Specifically, if the difference is not within the preset range, it means that the corresponding output results under the two methods are quite different, and then the second input torque is obtained by averaging, and then the output speed of the strut motor is controlled according to the second input torque.

[0139] In the case where the output results obtained by the two methods are significantly different from each other, it is impossible to directly use either of the two methods for subsequent control output. Therefore, for example, for authority and fairness, the two output results are averaged to improve the accuracy of data output.

[0140] The above describes in detail various embodiments corresponding to the speed control method based on the electric tailgate. On this basis, the present invention also discloses a speed control device based on the electric tailgate corresponding to the above method. Figure 4 This is a structural diagram of a speed control device based on an electric tailgate provided by an embodiment of the present invention. Figure 4 As shown, the speed control device based on the electric tailgate includes:

[0141] An acquisition module 11 is used to obtain system disturbance parameters and corresponding disturbance derivatives;

[0142] A prediction module 12 is configured to input the system disturbance parameters and disturbance derivatives as state variables into an extended state observer of the strut motor to predict the system disturbance;

[0143] The control module 13 is used to adjust the input torque of the strut motor according to the system disturbance to control the speed output of the strut motor.

[0144] Since the embodiments of the device part correspond to the above embodiments, the embodiments of the device part please refer to the description of the embodiments of the method part, which will not be repeated here.

[0145] For an introduction to a speed control device based on an electric tailgate provided by the present invention, please refer to the above method embodiment, and the present invention will not be repeated here. It has the same beneficial effects as the above speed control method based on an electric tailgate.

[0146] Figure 5 A structural diagram of a speed control device based on an electric tailgate provided in an embodiment of the present invention, such as Figure 5 As shown, the device includes:

[0147] Memory 21, for storing computer programs;

[0148] The processor 22 is configured to implement the steps of the speed control method based on the electric tailgate when executing the computer program.

[0149] The speed control device based on the electric tailgate provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer or a desktop computer.

[0150] The processor 22 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 22 may be implemented in at least one hardware form: a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 22 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 22 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 22 may also include an AI processor for handling computational operations related to machine learning.

[0151] The memory 21 may include one or more computer-readable storage media, which may be non-transitory. The memory 21 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 21 is at least used to store the following computer program 211, wherein, after the computer program is loaded and executed by the processor 22, it can implement the relevant steps of the speed control method based on the electric tailgate disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 21 may also include an operating system 212 and data 213, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 212 may include Windows, Unix, Linux, etc. The data 213 may include but is not limited to data involved in the speed control method based on the electric tailgate, etc.

[0152] In some embodiments, the speed control device based on the electric tailgate may further include a display screen 23 , an input / output interface 24 , a communication interface 25 , a power supply 26 , and a communication bus 27 .

[0153] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the speed control device based on the electric tailgate, and may include more or less components than those shown in the figure.

[0154] The processor 22 implements the speed control method based on the electric tailgate provided in any of the above embodiments by calling the instructions stored in the memory 21 .

[0155] For an introduction to a speed control device based on an electric tailgate provided by the present invention, please refer to the above method embodiment, and the present invention will not be repeated here. It has the same beneficial effects as the above speed control method based on an electric tailgate.

[0156] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by the processor 22 , the steps of the speed control method based on the electric tailgate are implemented.

[0157] It is understood that if the methods in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0158] For an introduction to a computer-readable storage medium provided by the present invention, please refer to the above method embodiment, and the present invention will not be repeated here. It has the same beneficial effects as the above-mentioned speed control method based on the electric tailgate.

[0159] The above is a detailed introduction to the speed control method, device, equipment and medium based on the electric tailgate provided by the present invention. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the present invention.

[0160] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A speed control method based on an electric tailgate, characterized in that: include: Obtain system disturbance parameters and corresponding disturbance derivatives; The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance; The input torque of the strut motor is adjusted according to the system disturbance to control the speed output of the strut motor.

2. The speed control method based on the electric tailgate according to claim 1, characterized in that: The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including: Derivative the system disturbance parameter once to obtain a disturbance derivative; The system disturbance parameter and the disturbance derivative are respectively used as the first state variable and the second state variable, and input into the extended state observer of the strut motor to predict the system disturbance; wherein the system disturbance parameter is the total load torque.

3. The speed control method based on the electric tailgate according to claim 1, characterized in that: The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including: Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter; Derivatives are taken of the first disturbance parameter, the second disturbance parameter, the third disturbance parameter, and the fourth disturbance parameter to obtain a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative; A first disturbance parameter, a second disturbance parameter, a third disturbance parameter, a fourth disturbance parameter, a first disturbance derivative, a second disturbance derivative, a third disturbance derivative, and a fourth disturbance derivative are respectively used as state variables and input into an extended state observer of the strut motor to predict system disturbances; wherein the system disturbance parameter is the total load torque.

4. The speed control method based on the electric tailgate according to claim 1, characterized in that: The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including: Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter; Set the corresponding disturbance impact factor for each disturbance parameter; Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters; Derivative the target disturbance parameter multiple times to obtain a target first disturbance derivative; Performing a derivation on the disturbance parameters other than the target disturbance parameter to obtain a second disturbance derivative; The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter, the target first disturbance derivative and the second disturbance derivative are respectively input as state variables to the extended state observer of the strut motor to predict the system disturbance.

5. The speed control method based on the electric tailgate according to claim 1, characterized in that: The system disturbance parameters and disturbance derivatives are used as state variables and input into the extended state observer of the strut motor to predict the system disturbance, including: Obtaining a first disturbance parameter, a second disturbance parameter, a third disturbance parameter, and a fourth disturbance parameter of the system disturbance parameter; Set the corresponding disturbance impact factor for each disturbance parameter; Selecting target disturbance parameters that exceed the target disturbance influence factor corresponding to the preset disturbance influence factor from among the disturbance parameters; Derivative the target disturbance parameter once to obtain a target second disturbance derivative; The first disturbance parameter, the second disturbance parameter, the third disturbance parameter, the fourth disturbance parameter and the target second disturbance derivative are respectively used as state variables and input into the extended state observer of the strut motor to predict the system disturbance.

6. The speed control method based on the electric tailgate according to claim 1, characterized in that: After adjusting the input torque of the strut motor according to the system disturbance, it also includes: Get the operating parameters of the strut motor; Calling artificial intelligence models; inputting the operating parameters into an artificial intelligence model to output a first input torque; comparing the first input torque with the adjusted input torque; If the difference between the first input torque and the adjusted input torque is within a preset range, the adjusted input torque is used to control the output speed of the strut motor.

7. The speed control method based on the electric tailgate according to claim 6, characterized in that: If the difference between the first input torque and the adjusted input torque is not within a preset range, the method further includes: Performing average processing on the first input torque and the adjusted input torque to obtain a second input torque; The strut motor speed output is controlled according to the second input torque.

8. A speed control device based on an electric tailgate, characterized in that: include: An acquisition module is used to obtain system disturbance parameters and corresponding disturbance derivatives; A prediction module is used for inputting system disturbance parameters and disturbance derivatives as state variables into an extended state observer of the strut motor to predict system disturbances; The control module is used to adjust the input torque of the strut motor according to the system disturbance to control the speed output of the strut motor.

9. A speed control device based on an electric tailgate, characterized in that: include: Memory for storing computer programs; A processor is configured to implement the steps of the speed control method based on an electric tailgate according to any one of claims 1 to 7 when executing the computer program.

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 speed control method based on the electric tailgate according to any one of claims 1 to 7 are implemented.

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