A clutch position signal filtering method, device, storage medium and terminal

Abstract

The present invention discloses a clutch position signal filtering method, device, storage medium and terminal, the method comprising: when the clutch position signal value at the current moment is obtained, determining the first a priori estimated value corresponding to the previous moment of the current moment; performing Kalman filtering according to the first a priori estimated value and the clutch position signal value at the current moment, generating the Kalman filtering result corresponding to the current moment; judging whether to perform secondary filtering according to the Kalman filtering result, and determining the final filtering result based on the judged result. Since the present application uses Kalman filtering to perform the initial filtering of the clutch position signal, and then performs secondary filtering of the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of setting the Kalman filtering parameters, the optimization of the control parameters is achieved, and the frequency stability of the clutch position signal is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of speed change control, and in particular to a clutch position signal filtering method, device, storage medium and terminal. Background Art

[0002] The clutch position signal is very critical. If the clutch position signal is not transmitted accurately, it is easy to cause unreasonable torque transmission, which will cause the vehicle to jerk when starting and shifting gears, and poor NVH. At the same time, it is easy to cause the clutch to still transmit torque when shifting gears, causing gear knocking. It will also mistakenly identify the clutch slipping stage as the fully engaged stage, and the clutch is prone to burning plates, which will lead to failures in shifting, shifting, and separating. Therefore, it is very important to improve the accuracy of the clutch position signal.

[0003] In the prior art solution, the prior art applies low-pass filtering or PTI filtering to filter the clutch position. The low-pass filtering expression is: Y n =T*X n +(1-T)*Y n-1 ; Where T is the time constant, which depends on the filter time constant and sampling period, X n is the filter input of the nth sampling, Y n is the filtered output of the nth sampling. The PTI filtering expression is: y = y (x-1) +(xy (x-1) )*DT / TI; where x is the input signal before filtering, DT is the time interval, TI is the delay time constant, and y is the output signal after filtering. Due to low-pass filtering and PTI filtering, there is a certain error in the accuracy of the signal, resulting in a slow signal response. Summary of the invention

[0004] The embodiments of the present application provide a clutch position signal filtering method, device, storage medium and terminal. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a clutch position signal filtering method, the method comprising:

[0006] When the clutch position signal value at the current moment is obtained, a first a priori estimate corresponding to the moment before the current moment is determined;

[0007] Performing Kalman filtering based on the first a priori estimated value and the clutch position signal value at the current moment to generate a Kalman filtering result corresponding to the current moment;

[0008] Whether to perform secondary filtering is determined based on the Kalman filtering result, and the final filtering result is determined based on the result of the determination.

[0009] Optionally, when the clutch position signal value at the current moment is obtained, before determining the first a priori estimation value corresponding to the moment before the current moment, the method further includes:

[0010] Reconstruct the Kalman equation to generate the state estimation and correction estimation equation of the clutch position;

[0011] Get the equation initialization parameters at the initial moment;

[0012] The prior estimate of the Kalman filter at the initial moment is calculated based on the equation initialization parameters and state estimation and the revised estimation equation.

[0013] Optionally, when the clutch position signal value at the current moment is obtained, determining a first a priori estimate value corresponding to the moment before the current moment includes:

[0014] Get the clutch position signal value at the current moment;

[0015] When the current moment is the next moment of the initial moment, the a priori estimation value of the Kalman filter at the initial moment is determined as the first a priori estimation value corresponding to the moment before the current moment.

[0016] Optionally, the Kalman filter includes a prediction process and an update process;

[0017] Performing Kalman filtering according to the first a priori estimated value and the clutch position signal value at the current moment, generating a Kalman filtering result corresponding to the current moment, including:

[0018] Predicting the state value at the current moment according to the first prior estimation value;

[0019] The state value is updated according to the clutch position signal value at the current moment to generate a second prior estimation value;

[0020] A Kalman filter result corresponding to the current moment is generated according to the second priori estimate value.

[0021] Optionally, generating a Kalman filter result corresponding to the current moment according to the second a priori estimate value includes:

[0022] When the second a priori estimate value is within the preset state value interval, determining the second a priori estimate value as the Kalman filter result corresponding to the current moment;

[0023] or,

[0024] When the second prior estimation value is not within the preset state value interval, the equation parameters of the state estimation and correction estimation equation are adjusted, and the step of determining the first prior estimation value corresponding to the previous moment of the current moment when the clutch position signal value of the current moment is obtained is continued.

[0025] Optionally, judging whether to perform secondary filtering according to the Kalman filtering result includes:

[0026] Get the final filtering result of the previous moment corresponding to the current moment;

[0027] Calculate the target difference between the Kalman filter result and the final filter result at the previous moment;

[0028] Calculate the absolute value of the target difference;

[0029] When the absolute value is greater than or equal to a preset threshold, it is determined that secondary filtering is required;

[0030] or,

[0031] When the absolute value is less than the preset threshold, it is determined that secondary filtering is not required.

[0032] Optionally, the final filtering result is determined based on the judgment result, including:

[0033] When secondary filtering is not required, the Kalman filtering result is determined as the final filtering result;

[0034] or,

[0035] When secondary filtering is required, the signals whose absolute values ​​are greater than or equal to a preset threshold are counted to generate a count value;

[0036] The final filtering result is determined according to the count value, including:

[0037] When the count value is less than the pre-designed value, the final filtering result at the last moment is determined as the final filtering result;

[0038] or,

[0039] When the count value is greater than or equal to the pre-designed value, the Kalman filtering result of the previous moment corresponding to the current moment is determined as the final filtering result.

[0040] In a second aspect, an embodiment of the present application provides a clutch position signal filtering device, the device comprising:

[0041] A priori estimation value determination module, used to determine a first priori estimation value corresponding to a moment before the current moment when the clutch position signal value at the current moment is acquired;

[0042] A Kalman filter result generating module, used for performing Kalman filtering according to the first prior estimation value and the clutch position signal value at the current moment, and generating a Kalman filter result corresponding to the current moment;

[0043] The final filtering result determination module is used to determine whether to perform secondary filtering according to the Kalman filtering result, and determine the final filtering result based on the result after the determination.

[0044] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0045] In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0046] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0047] In the embodiment of the present application, the clutch position signal filtering device first determines the first a priori estimate corresponding to the previous moment of the current moment when the clutch position signal value at the current moment is obtained, and then performs Kalman filtering based on the first a priori estimate and the clutch position signal value at the current moment to generate the Kalman filtering result corresponding to the current moment, and finally determines whether to perform secondary filtering based on the Kalman filtering result, and determines the final filtering result based on the result after judgment. Since the present application uses Kalman filtering to perform the initial filtering of the clutch position signal, and then performs secondary filtering on the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of Kalman filtering parameter setting, the optimization of control parameters is achieved, and the frequency stability of the clutch position signal is improved.

[0048] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] Figure 1 It is a flow chart of a clutch position signal filtering method provided in an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of a Kalman filter process provided by an embodiment of the present application;

[0052] Figure 3This is a schematic block diagram of a process of re-filtering a Kalman filter provided by an embodiment of the present application;

[0053] Figure 4 It is a structural schematic diagram of a clutch position signal filtering device provided in an embodiment of the present application;

[0054] Figure 5 It is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.

[0056] It should be clear that the described embodiments are only some embodiments of the present invention, not all 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.

[0057] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the attached claims.

[0058] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, in the description of the present invention, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previously associated objects are in an "or" relationship.

[0059] The present application provides a clutch position signal filtering method, device, storage medium and terminal to solve the problems existing in the above-mentioned related technical problems. In the technical solution provided by the present application, since the present application uses Kalman filtering to perform primary filtering on the clutch position signal, and then performs secondary filtering on the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of Kalman filtering parameter setting, thereby achieving optimization of control parameters and improving the frequency stability of the clutch position signal, an exemplary embodiment is used for detailed description below.

[0060] The following will be combined with the attached Figure 1 -Attached Figure 3 , a clutch position signal filtering method provided in an embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on a clutch position signal filtering device based on a von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0061] See also Figure 1 , is a schematic flow chart of a clutch position signal filtering method provided in an embodiment of the present application. Figure 1 As shown, the method of the embodiment of the present application may include the following steps:

[0062] S101, when the clutch position signal value at the current moment is obtained, determining a first a priori estimation value corresponding to the moment before the current moment;

[0063] The filtering is an operation of filtering out a specific frequency band in the clutch position signal. The clutch position signal value is a signal obtained by the clutch position sensor.

[0064] Generally, this application provides a clutch position filtering algorithm that can be applied to heavy-duty truck TCU (transmission control unit) at the application layer to ensure the clutch position accuracy, optimize the control parameters, improve the frequency stability of the clutch position signal, and optimize the shifting efficiency and accuracy.

[0065] In the embodiment of the present application, it is also necessary to calculate the prior estimate of the Kalman filter at the initial moment before step S101. First, the Kalman equation is reconstructed to generate the state estimation and correction estimation equation of the clutch position, and then the equation initialization parameters at the initial moment are obtained. Finally, the prior estimate of the Kalman filter at the initial moment is calculated based on the equation initialization parameters and the state estimation and correction estimation equation.

[0066] In one possible implementation, the clutch position signal value at the current moment is first obtained, and then when the current moment is the next moment of the initial moment, the prior estimation value of the Kalman filter at the initial moment is determined as the first prior estimation value corresponding to the moment before the current moment.

[0067] S102, performing Kalman filtering according to the first a priori estimation value and the clutch position signal value at the current moment, to generate a Kalman filtering result corresponding to the current moment;

[0068] Among them, Kalman filtering specifically uses the optimal result of the previous moment to predict the state value of the current moment, and uses the observed value to correct the current state prediction value to obtain the optimal state estimate value of the current moment.

[0069] Usually, Kalman filtering is divided into two processes: prediction and update. Let time t be the current time and time t-1 be the previous time.

[0070] In the prediction process, the optimal result at time t-1 is used to predict the state value at time t to obtain the prior estimate. Its expression is as follows:

[0071] in, and They represent the prior estimate and the optimal state estimate at time t-1, respectively. t-1 represents the control quantity of the system at time t-1, F and B represent the state transfer matrix and control matrix respectively, express The covariance matrix of , Q is the system process covariance.

[0072] In the updating process, the state value at time t is measured by using sensors and other measuring instruments, and the prior estimate is corrected to obtain the optimal state estimate at time t. Its expression is as follows:

[0073]

[0074] Among them, k t is the Kalman gain, H is the measurement system matrix, R is the covariance of the measurement noise, is the optimal state estimate at time t, Z t is the measured value at time t, P t is the error covariance.

[0075] In an embodiment of the present application, if the moment before the current moment is the initial moment, when performing Kalman filtering, the state value of the current moment is first predicted based on the first prior estimate value, and then the state value is updated based on the clutch position signal value at the current moment to generate a second prior estimate value, and finally the Kalman filtering result corresponding to the current moment is generated based on the second prior estimate value.

[0076] Specifically, when generating the Kalman filter result corresponding to the current moment based on the second prior estimate, first, when the second prior estimate is in the preset state value interval, the second prior estimate is determined as the Kalman filter result corresponding to the current moment; or, when the second prior estimate is not in the preset state value interval, the equation parameters of the state estimation and correction estimation equations are adjusted, and the step of determining the first prior estimate corresponding to the previous moment when the clutch position signal value at the current moment is obtained is continued.

[0077] In one possible implementation, for example Figure 2As shown in the figure, the steps for generating the Kalman filter result are as follows: S1: Construct the Kalman equation. Since the clutch position motion model has no input, the position value at the next moment is estimated only by the position value at the previous moment, so the state transfer matrix F and the measurement system matrix H are set to 1, and the control quantity u t-1 is 0, and the clutch position state estimation and correction estimation equations are obtained. S2: Initialization parameters. P0, Q, and R are initialized and substituted into the state estimation and correction estimation equation of the clutch position to obtain the prior estimation value of the Kalman filter of the clutch position at the initial moment. S3: The clutch position signal value at the current moment is obtained through the clutch position sensor, and it is used as the clutch position observation value. S4: The clutch position signal value at the current moment is obtained through the clutch position sensor, and it is used as the clutch position observation value. S5: According to the prior estimation value and observation value of the Kalman filter of the clutch position at the initial moment, the prior estimation value at the current moment is corrected through the Kalman filter algorithm to obtain the optimal state estimation value of the clutch position at the current moment, and it is used as the prior estimation value of the clutch position at the next moment. After the cycle of S3, S4, and S5, the clutch position signal completes the prediction and update process, and the abnormal points are filtered out. In this process, it is necessary to adjust the hyperparameters Q and R through manual tuning to obtain the clutch position signal after Kalman filtering under more ideal conditions, that is, the Kalman filter result.

[0078] S103, judging whether to perform secondary filtering according to the Kalman filtering result, and determining the final filtering result based on the judgment result.

[0079] Among them, one of the difficulties of Kalman filtering lies in the setting and coordination of parameters. The adjustment of Q and R must ensure that the abnormal points can be filtered out, and the initial value must respond quickly. The two are difficult to coordinate. For this reason, secondary filtering is performed on the basis of Kalman filtering. The purpose is to further filter out the abnormal points under the premise of ensuring the rapid response of the initial value. At this time, the adjustment of Kalman filtering Q and R only needs to focus on the rapid response of the initial value, and the abnormal point filtering can be controlled within a certain range. The secondary filtering can reasonably filter out the abnormal points. This algorithm is conducive to reducing the difficulty of parameter setting and coordination, thereby optimizing the control parameters and improving the frequency stability of the clutch position signal.

[0080] In the embodiment of the present application, when determining whether to perform secondary filtering according to the Kalman filtering result, first obtain the final filtering result of the previous moment corresponding to the current moment, then calculate the target difference between the Kalman filtering result and the final filtering result of the previous moment, and then calculate the absolute value of the target difference, and finally when the absolute value is greater than or equal to the preset threshold, determine that secondary filtering is required. Alternatively, when the absolute value is less than the preset threshold, determine that secondary filtering is not required.

[0081] Specifically, when determining the final filtering result based on the judgment result, first, when secondary filtering is not required, the Kalman filtering result is determined as the final filtering result; or, when secondary filtering is required, the signal whose absolute value is greater than or equal to the preset threshold is counted to generate a count value, and then the final filtering result is determined based on the count value.

[0082] Specifically, when determining the final filtering result based on the count value, first, when the count value is less than the pre-designed value, the final filtering result of the previous moment is determined as the final filtering result; or, when the count value is greater than or equal to the pre-designed value, the Kalman filtering result of the previous moment corresponding to the current moment is determined as the final filtering result.

[0083] like Figure 3 As shown, the secondary filtering algorithm includes the following steps: S6, judging | Kalman filter result - value A | ≥ threshold, where value A is the final filtering result corresponding to the value of the previous moment of the current moment, and judging the absolute value of the difference between the Kalman filter result and value A by the threshold, the threshold is not fixed and can be set according to the actual situation. If yes, this value is an abnormal point, and enter S7 for counting; if not, this value is not an abnormal point, and the deviation within the threshold range can be accepted, and the final filtering result is the Kalman filtering result. S7, counting the signal of | Kalman filter result - value A | ≥ threshold, plays the role of a counter. S8, judging the count value < set value, the set value is not fixed, and can be set according to the actual situation. If yes, the final filtering result is the final filtering result of the previous moment, and there is an accumulation of errors at this time, and it is considered that the accumulated errors are within a reasonable range; if not, the accumulated errors at this time exceed the reasonable range, and the final filtering result needs to be corrected, and the final filtering result is the Kalman filtering result of the previous moment.

[0084] It should be noted that the Kalman filter is used for the first time to filter the clutch position signal, and the signal delay is small, which can effectively improve the accuracy of the clutch position signal; a counter function is added to the secondary filter, and the count value can be calibrated, which realizes the recording of abnormal points and improves the filtering effect; the Kalman filter result is fed back to the secondary filter, which can effectively reduce the difficulty of Kalman filter parameter setting, achieve rapid response of initial values, and optimize the filtering results.

[0085] Furthermore, the clutch position filtering algorithm of the heavy truck TCU of the present application is applicable to but not limited to the clutch position signal.

[0086] In the embodiment of the present application, the clutch position signal filtering device first determines the first a priori estimate corresponding to the previous moment of the current moment when the clutch position signal value at the current moment is obtained, and then performs Kalman filtering based on the first a priori estimate and the clutch position signal value at the current moment to generate the Kalman filtering result corresponding to the current moment, and finally determines whether to perform secondary filtering based on the Kalman filtering result, and determines the final filtering result based on the result after judgment. Since the present application uses Kalman filtering to perform the initial filtering of the clutch position signal, and then performs secondary filtering on the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of Kalman filtering parameter setting, the optimization of control parameters is achieved, and the frequency stability of the clutch position signal is improved.

[0087] The following are embodiments of the device of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments of the present invention, please refer to the method embodiments of the present invention.

[0088] See also Figure 4 , which shows a schematic diagram of the structure of a clutch position signal filtering device provided by an exemplary embodiment of the present invention. The clutch position signal filtering device can be implemented as all or part of a terminal through software, hardware or a combination of both. The device 1 includes a priori estimation value determination module 10, a Kalman filter result generation module 20, and a final filter result determination module 30.

[0089] A priori estimation value determination module 10 is used to determine a first priori estimation value corresponding to a moment before the current moment when the clutch position signal value at the current moment is acquired;

[0090] A Kalman filter result generating module 20, configured to perform Kalman filtering according to the first prior estimation value and the clutch position signal value at the current moment, and generate a Kalman filter result corresponding to the current moment;

[0091] The final filtering result determination module 30 is used to determine whether to perform secondary filtering according to the Kalman filtering result, and determine the final filtering result based on the result after the determination.

[0092] It should be noted that the clutch position signal filtering device provided in the above embodiment only uses the division of the above functional modules as an example when executing the clutch position signal filtering method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the clutch position signal filtering device provided in the above embodiment and the clutch position signal filtering method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0093] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0094] In the embodiment of the present application, the clutch position signal filtering device first determines the first a priori estimate corresponding to the previous moment of the current moment when the clutch position signal value at the current moment is obtained, and then performs Kalman filtering based on the first a priori estimate and the clutch position signal value at the current moment to generate the Kalman filtering result corresponding to the current moment, and finally determines whether to perform secondary filtering based on the Kalman filtering result, and determines the final filtering result based on the result after judgment. Since the present application uses Kalman filtering to perform the initial filtering of the clutch position signal, and then performs secondary filtering on the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of Kalman filtering parameter setting, the optimization of control parameters is achieved, and the frequency stability of the clutch position signal is improved.

[0095] The present invention also provides a computer readable medium having program instructions stored thereon, which, when executed by a processor, implements the clutch position signal filtering method provided by each of the above method embodiments. The present invention also provides a computer program product containing instructions, which, when executed on a computer, enables the computer to execute the clutch position signal filtering method of each of the above method embodiments.

[0096] See also Figure 5 , which is a schematic diagram of the structure of a terminal provided in an embodiment of the present application. Figure 5 As shown, the terminal 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0097] The communication bus 1002 is used to realize the connection and communication between these components.

[0098] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0099] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0100] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts within the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0101] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 5 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a clutch position signal filtering application.

[0102] exist Figure 5In the terminal 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 1001 can be used to call the clutch position signal filtering application stored in the memory 1005, and specifically perform the following operations:

[0103] When the clutch position signal value at the current moment is obtained, a first a priori estimate corresponding to the moment before the current moment is determined;

[0104] Performing Kalman filtering based on the first a priori estimated value and the clutch position signal value at the current moment to generate a Kalman filtering result corresponding to the current moment;

[0105] Whether to perform secondary filtering is determined based on the Kalman filtering result, and the final filtering result is determined based on the result of the determination.

[0106] In one embodiment, when the processor 1001 obtains the clutch position signal value at the current moment, before determining the first a priori estimation value corresponding to the moment before the current moment, it further performs the following operations:

[0107] Reconstruct the Kalman equation to generate the state estimation and correction estimation equation of the clutch position;

[0108] Get the equation initialization parameters at the initial moment;

[0109] The prior estimate of the Kalman filter at the initial moment is calculated based on the equation initialization parameters and state estimation and the revised estimation equation.

[0110] In one embodiment, when the processor 1001 obtains the clutch position signal value at the current moment and determines the first a priori estimation value corresponding to the moment before the current moment, the processor 1001 specifically performs the following operations:

[0111] Get the clutch position signal value at the current moment;

[0112] When the current moment is the next moment of the initial moment, the a priori estimation value of the Kalman filter at the initial moment is determined as the first a priori estimation value corresponding to the moment before the current moment.

[0113] In one embodiment, when the processor 1001 performs Kalman filtering based on the first a priori estimated value and the clutch position signal value at the current moment to generate a Kalman filtering result corresponding to the current moment, the processor 1001 specifically performs the following operations:

[0114] Predicting the state value at the current moment according to the first prior estimation value;

[0115] The state value is updated according to the clutch position signal value at the current moment to generate a second prior estimation value;

[0116] A Kalman filter result corresponding to the current moment is generated according to the second priori estimate value.

[0117] In one embodiment, when the processor 1001 generates a Kalman filter result corresponding to the current moment according to the second a priori estimation value, the processor 1001 specifically performs the following operations:

[0118] When the second a priori estimate value is within the preset state value interval, determining the second a priori estimate value as the Kalman filter result corresponding to the current moment;

[0119] or,

[0120] When the second prior estimation value is not within the preset state value interval, the equation parameters of the state estimation and correction estimation equation are adjusted, and the step of determining the first prior estimation value corresponding to the previous moment of the current moment when the clutch position signal value of the current moment is obtained is continued.

[0121] In one embodiment, when the processor 1001 determines whether to perform secondary filtering according to the Kalman filtering result, the processor 1001 specifically performs the following operations:

[0122] Get the final filtering result of the previous moment corresponding to the current moment;

[0123] Calculate the target difference between the Kalman filter result and the final filter result at the previous moment;

[0124] Calculate the absolute value of the target difference;

[0125] When the absolute value is greater than or equal to a preset threshold, it is determined that secondary filtering is required;

[0126] or,

[0127] When the absolute value is less than the preset threshold, it is determined that secondary filtering is not required.

[0128] In one embodiment, when the processor 1001 determines the final filtering result based on the result after the judgment, the processor 1001 specifically performs the following operations:

[0129] When secondary filtering is not required, the Kalman filtering result is determined as the final filtering result;

[0130] or,

[0131] When secondary filtering is required, the signals whose absolute values ​​are greater than or equal to a preset threshold are counted to generate a count value;

[0132] The final filtering result is determined according to the count value, including:

[0133] When the count value is less than the pre-designed value, the final filtering result at the last moment is determined as the final filtering result;

[0134] or,

[0135] When the count value is greater than or equal to the pre-designed value, the Kalman filtering result of the previous moment corresponding to the current moment is determined as the final filtering result.

[0136] In the embodiment of the present application, the clutch position signal filtering device first determines the first a priori estimate corresponding to the previous moment of the current moment when the clutch position signal value at the current moment is obtained, and then performs Kalman filtering based on the first a priori estimate and the clutch position signal value at the current moment to generate the Kalman filtering result corresponding to the current moment, and finally determines whether to perform secondary filtering based on the Kalman filtering result, and determines the final filtering result based on the result after judgment. Since the present application uses Kalman filtering to perform the initial filtering of the clutch position signal, and then performs secondary filtering on the signal through secondary filtering to further filter out abnormal points, while reducing the difficulty of Kalman filtering parameter setting, the optimization of control parameters is achieved, and the frequency stability of the clutch position signal is improved.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the clutch position signal filtering program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0138] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A clutch position signal filtering method, characterized in that: The method comprises: When the clutch position signal value at the current moment is obtained, determining a first a priori estimation value corresponding to a moment before the current moment; Performing Kalman filtering according to the first a priori estimation value and the clutch position signal value at the current moment to generate a Kalman filtering result corresponding to the current moment; Determine whether to perform secondary filtering according to the Kalman filtering result, and determine the final filtering result based on the result after the determination; wherein, The determining whether to perform secondary filtering according to the Kalman filtering result includes: Get the final filtering result of the previous moment corresponding to the current moment; Calculate the target difference between the Kalman filter result and the final filter result at the previous moment; Calculating the absolute value of the target difference; When the absolute value is greater than or equal to a preset threshold, it is determined that secondary filtering is required; or, When the absolute value is less than a preset threshold, it is determined that secondary filtering is not required; wherein, The determining of the final filtering result based on the result after the judgment includes: When secondary filtering is not required, determining the Kalman filtering result as the final filtering result; or, When secondary filtering is required, the signals whose absolute values ​​are greater than or equal to a preset threshold are counted to generate a count value; Determining a final filtering result according to the count value includes: When the count value is less than a pre-designed value, the final filtering result at the last moment is determined as the final filtering result; or, When the count value is greater than or equal to a pre-designed value, the Kalman filtering result at the previous moment corresponding to the current moment is determined as the final filtering result.

2. The method according to claim 1, characterized in that When the clutch position signal value at the current moment is obtained, before determining the first a priori estimation value corresponding to the moment before the current moment, the method further includes: Reconstruct the Kalman equation to generate the state estimation and correction estimation equation of the clutch position; Get the equation initialization parameters at the initial moment; The prior estimation value of the Kalman filter at the initial moment is calculated according to the equation initialization parameters and the state estimation and correction estimation equation.

3. The method according to claim 2, characterized in that When the clutch position signal value at the current moment is obtained, determining a first a priori estimation value corresponding to a moment before the current moment includes: Get the clutch position signal value at the current moment; When the current moment is the next moment of the initial moment, the a priori estimation value of the Kalman filter at the initial moment is determined as the first a priori estimation value corresponding to the moment before the current moment.

4. The method according to claim 2, characterized in that: The Kalman filter includes a prediction process and an update process; The performing Kalman filtering according to the first a priori estimation value and the clutch position signal value at the current moment to generate a Kalman filtering result corresponding to the current moment includes: Predicting the state value at the current moment according to the first a priori estimated value; updating the state value according to the clutch position signal value at the current moment to generate a second priori estimate value; Generate a Kalman filtering result corresponding to the current moment according to the second prior estimation value.

5. The method according to claim 4, characterized in that Generating a Kalman filter result corresponding to the current moment according to the second a priori estimate value includes: When the second a priori estimate value is within a preset state value interval, determining the second a priori estimate value as a Kalman filtering result corresponding to the current moment; or, When the second prior estimation value is not within the preset state value range, the equation parameters of the state estimation and correction estimation equation are adjusted, and the step of determining the first prior estimation value corresponding to the previous moment when the clutch position signal value at the current moment is obtained is continued.

6. A clutch position signal filtering device, characterized in that: The device comprises: A priori estimation value determination module, configured to determine a first priori estimation value corresponding to a moment before the current moment when a clutch position signal value at the current moment is acquired; A Kalman filter result generating module, configured to perform Kalman filtering according to the first a priori estimation value and the clutch position signal value at the current moment, and generate a Kalman filter result corresponding to the current moment; The final filtering result determination module is used to determine whether to perform secondary filtering according to the Kalman filtering result, and determine the final filtering result based on the result after the determination; wherein, The determining whether to perform secondary filtering according to the Kalman filtering result includes: Get the final filtering result of the previous moment corresponding to the current moment; Calculate the target difference between the Kalman filter result and the final filter result at the previous moment; Calculating the absolute value of the target difference; When the absolute value is greater than or equal to a preset threshold, it is determined that secondary filtering is required; or, When the absolute value is less than a preset threshold, it is determined that secondary filtering is not required; wherein, The determining of the final filtering result based on the result after the judgment includes: When secondary filtering is not required, determining the Kalman filtering result as the final filtering result; or, When secondary filtering is required, the signals whose absolute values ​​are greater than or equal to a preset threshold are counted to generate a count value; Determining a final filtering result according to the count value includes: When the count value is less than a pre-designed value, the final filtering result at the last moment is determined as the final filtering result; or, When the count value is greater than or equal to a pre-designed value, the Kalman filtering result at the previous moment corresponding to the current moment is determined as the final filtering result.

7. A computer storage medium, characterized in that: The computer storage medium stores a plurality of instructions, which are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 5.

8. A terminal, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 5.

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