Rack end learning method and device, medium and steer-by-wire steering system
By directly utilizing rack position signals and motor parameters to determine the end position, the end-of-line learning process of the steer-by-wire system is simplified, reducing system complexity and sensor dependence, and improving system safety and response efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI TONGYU AUTOMOTIVE TECHNOLOGY CO LTD
- Filing Date
- 2026-01-23
- Publication Date
- 2026-06-23
AI Technical Summary
The end-of-line learning technology of existing steer-by-wire systems relies on complex calibration work and steering torque sensor signals, resulting in low system response efficiency and high risks to reliability and stability.
By combining the rack's current position, end value, and stroke parameters with the motor speed and torque, it can directly determine whether the rack has reached the end position and update it based on the linear position sensor signal, thus achieving end position learning.
It simplifies the end-point learning process, reduces system complexity and sensor dependence, and improves system security and response efficiency.
Smart Images
Figure CN122264159A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of steer-by-wire technology, and particularly relates to a rack end learning method, device, medium, and vehicle. Background Technology
[0002] In automotive steer-by-wire systems, end-point learning is a key technology aimed at accurately identifying and setting the physical limits of the steering wheel angle. Traditional techniques typically establish a correlation between steering wheel angle and rack displacement, relying on steering torque sensor signals to indirectly infer the end point. This ensures the steering system operates within safe mechanical limits and prevents impacts on the actuators at the endpoints.
[0003] However, existing end-point learning technologies have several obvious problems: First, the system relies on the complex correspondence between the steering wheel angle and the rack displacement, which requires tedious calibration work; second, the solution must rely on the steering torque angle sensor signal to achieve this, increasing the system's dependence on external sensors; and finally, the entire end-point position calculation process is complex, which not only affects the system's response efficiency but also brings potential risks to the system's reliability and stability. Summary of the Invention
[0004] This application provides a rack end learning method, device, medium, and steer-by-wire system, which can effectively prevent mechanical collisions and significantly improve the safety of steer-by-wire system operation.
[0005] In a first aspect, embodiments of this application provide a rack end-learning method for end-learning of the rack in the upper steering subsystem of a steer-by-wire system. The method includes:
[0006] Based on the current position of the rack, the rack end value, the maximum stroke and the minimum stroke, determine whether the current position meets the end-learning condition;
[0007] If the current position meets the end-effector learning condition, then based on the rack speed and motor operating parameters, it is determined whether the rack has reached the end position, and the motor is the motor used to drive the rack to move;
[0008] When it is determined that the rack has reached the end position, the rack end value is updated based on the current position.
[0009] In a further embodiment, based on the rack's current position, rack end value, maximum stroke, and minimum stroke, it is determined whether the current position satisfies the end-effector learning condition, including:
[0010] The soft-end interval is determined based on the rack end value, the maximum stroke value, and the minimum stroke value;
[0011] If the current position is within the soft-end interval, then the current position is determined to meet the end-learning condition.
[0012] In a further embodiment, the first end of the rack corresponds to the maximum travel value, and the other end corresponds to the minimum travel value. The first end can be any one of the two ends of the rack. The rack end value includes the corresponding first end value and second end value. The soft end interval includes the first end interval and the second end interval. The method includes:
[0013] The interval between the first end value and the maximum travel value is defined as the first end interval;
[0014] The interval between the second terminal value and the minimum travel value is defined as the second terminal interval.
[0015] In a further embodiment, the motor's operating parameters include motor speed and motor torque. Based on the rack's speed and the motor's operating parameters, determining whether the rack has reached its end position includes:
[0016] If the speed of the rack is less than the preset speed threshold, the motor speed is less than the preset speed threshold, and the motor torque is greater than the preset torque threshold, then the rack is determined to have reached the end position.
[0017] In a further embodiment, the method further includes:
[0018] Obtain the end reset signal;
[0019] When the end reset signal is received
[0020] And / or,
[0021] At least when the preset end-reset condition is met based on the current position,
[0022] Reset the value at the end of the rack;
[0023] Otherwise, perform the step of determining whether the current position meets the preset conditions based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value.
[0024] In a further embodiment, the method includes:
[0025] When the current position is greater than the maximum travel value or less than the minimum travel value, the end-of-journey reset condition is met.
[0026] In a further embodiment, the current position is determined based on position data collected by a linear position sensor. Before updating the rack end position based on the current position after determining that the rack has reached its end position, the method further includes:
[0027] The position data is verified to determine whether the rack has reached the end position.
[0028] If the verification passes, the rack end value will be updated to the current position.
[0029] Secondly, embodiments of this application provide a rack end-learning device for end-learning of the rack in the lower steering subsystem of a steer-by-wire system. The device includes:
[0030] The first judgment module is used to determine whether the current position meets the end-point learning condition based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value.
[0031] The second determination module is used to determine whether the rack has reached the end position based on the rack speed and the motor operating parameters if the current position meets the end-learning condition. The motor is a motor used to drive the rack to move.
[0032] The end value update module is used to update the end value of the rack based on the current position when it is determined that the rack has reached the end position.
[0033] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the rack end learning method as described above.
[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the rack end learning method described above.
[0035] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform any of the rack end learning methods described above.
[0036] Sixthly, embodiments of this application provide a steer-by-wire system, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the rack end learning method as described above by executing the instructions stored in the memory.
[0037] This application discloses a rack end-learning method, apparatus, medium, and steer-by-wire system. The method is used for end-learning of the rack in the lower steering subsystem of a steer-by-wire system. The method includes: determining whether the current position meets the end-learning requirements based on the rack's current position, rack end value, maximum travel value, and minimum travel value; if the current position meets the end-learning requirements, determining whether the rack has reached the end position based on the rack's speed and motor operating parameters (the motor is used to drive the rack's movement); when it is determined that the rack has reached the end position, updating the rack end value based on the current position. Thus, in this application embodiment, by comprehensively considering the current position, current rack end value, and rack travel parameters, it is determined whether the current rack end value needs to be learned and updated. During the learning process, it is determined whether the end position has been reached based on the rack position signal and motor parameters; once it is confirmed that the end position has been reached, the current rack position is updated to the new rack end value. End-position learning can be completed solely based on the rack position signal, making the operation simple and efficient. Meanwhile, the embodiments of this application directly achieve end-position learning based on existing LPS sensor signals, without relying on TAS sensor signals, thereby achieving decoupling of the up and down steering systems in the steer-by-wire system, which not only reduces system complexity but also reduces reliance on additional sensors. Attached Figure Description
[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is one of the flowcharts illustrating the rack end learning method provided in the embodiments of this application;
[0040] Figure 2 This is a second schematic flowchart of the rack end learning method provided in the embodiments of this application;
[0041] Figure 3 This is a schematic diagram of the structure of the rack end learning device provided in the embodiments of this application;
[0042] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0043] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.
[0044] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.
[0045] To address the problems of existing technologies, this application provides a rack end-learning method, device, medium, and steer-by-wire system. The rack end-learning method provided in this application is described below. A rack end-learning method is used for end-learning of the rack in the lower steering subsystem of a steer-by-wire system. A steer-by-wire system is an advanced system that eliminates traditional mechanical connections and achieves steering control through electrical signals. It mainly includes an upper steering subsystem and a lower steering subsystem. Compared to previous end-learning schemes that convert the left and right steering wheel angle end signals in the upper steering subsystem into the left and right end positions of the rack in the lower steering subsystem before calculating the end-position, this application directly utilizes the rack position signals in the lower steering subsystem to calculate the rack end position, thereby completing the end-learning process, which is simpler and more efficient.
[0046] Figure 1 This document illustrates one of the flowcharts of the rack end learning method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps:
[0047] S101. Based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value, determine whether the current position satisfies the end-learning condition.
[0048] In this step, the soft-end interval is first determined based on the rack end value, the maximum travel value, and the minimum travel value. The soft-end interval serves as a buffer zone, and the rack end value is used to implement software position restrictions to ensure that the rack does not enter the soft-end interval during normal system operation, thereby preventing it from moving to its physical limit position and achieving rack end protection.
[0049] Specifically, the first end of the rack corresponds to the maximum travel value, and the other end corresponds to the minimum travel value. The first end is either of the two ends of the rack. The rack end value includes a corresponding first end value and a second end value. The soft end interval includes a first end interval and a second end interval. This step includes: determining the interval between the first end value and the maximum travel value as the first end interval; and determining the interval between the second end value and the minimum travel value as the second end interval.
[0050] In this embodiment, the first end and the second end are the left and right ends of the rack, respectively. The maximum and minimum travel values are the physical limit values of the left and right ends of the rack, respectively. The first end value and the second end value are the left end value and the right end value learned previously, respectively, used to implement the end protection function to prevent the rack from moving to its physical limit position, thereby avoiding structural damage caused by mechanical collision. Taking the left end of the rack as an example, the interval between the left end value and the maximum travel value is the aforementioned first end interval. Under normal system operation, when the rack moves to the left end value position, the system determines the corresponding rack end protection torque based on signals such as the current motor speed and rack speed, and drives the motor to output, so that the rack no longer moves to the left end, thereby avoiding mechanical collision with the physical end of the rack and realizing rack end protection.
[0051] If the current position is within the soft-terminal interval, then the current position is determined to meet the end-terminal learning condition.
[0052] For example, when the rack moves to the left to the first end interval, it indicates that the system is malfunctioning. This is usually due to an error in the left end value. At this time, it is necessary to update and learn the end value. Therefore, it can be determined that the current position meets the end learning condition.
[0053] Based on the rack position and rack travel parameters, this embodiment determines that the end value needs to be updated when the rack position exceeds the preset normal operating range and is within the preset soft-end interval. This determination process is simple and efficient. Furthermore, this embodiment only relies on the rack position signal provided by the lower steering subsystem, without needing the TAS sensor signal from the upper steering subsystem, thus achieving decoupling of the upper and lower steering subsystems and effectively reducing the overall system complexity.
[0054] S102. If the current position satisfies the end-learning condition, then based on the speed of the rack and the operating parameters of the motor, it is determined whether the rack has reached the end position, wherein the motor is a motor used to drive the rack to move.
[0055] In this embodiment of the application, the operating parameters of the motor include motor speed and motor torque. If the speed of the rack is less than a preset speed threshold, the motor speed is less than a preset speed threshold, and the motor torque is greater than a preset torque threshold, then it is determined that the rack has reached the end position.
[0056] It should be noted that the end position is a physical structural position preset by the user. When the absolute position of the rack reaches this position, the system will generate a corresponding reverse torque to prevent the rack from continuing to move in the original direction. Therefore, by comprehensively judging the rack speed, motor speed, and motor torque, it is possible to accurately identify whether the rack has reached the end position.
[0057] In this embodiment, by comprehensively analyzing the speed of the rack, the motor speed, and the motor torque in the lower steering subsystem, it is determined whether the rack has reached its end position. This effectively improves the accuracy and robustness of the detection, preventing misjudgments caused by fluctuations in a single signal. Furthermore, this step eliminates the need for additional physical position sensors, effectively reducing system hardware costs and structural complexity.
[0058] S103. When it is determined that the rack has reached the end position, the end value of the rack is updated based on the current position.
[0059] In this step, once the rack reaches its end position, the rack end value can be updated to the current position, thereby achieving accurate calibration and dynamic updating of the rack end position and providing a reliable data benchmark for subsequent motion control.
[0060] Furthermore, the current position is determined based on position data collected by a linear position sensor. Figure 2 This illustrates a second flowchart of the rack end learning method provided in an embodiment of this application, as shown below. Figure 2 As shown, after determining that the rack has reached the end position, before updating the rack end based on the current position, this embodiment of the application also performs LPS data verification to verify the reliability of the data at the current position. This step includes: verifying the position data to determine a second time whether the rack has reached the end position.
[0061] Specifically, once the rack reaches its end, the system verifies the rationality, continuity, and signal quality of the position data to ensure it conforms to the expected operating logic. If the verification passes, the system indicates that the current position data is reliable, updates the rack end value to the currently detected position, and sets the left and right end detection flags to 1. If the verification fails, the system considers the data abnormal and sets a fault status to notify the user of the current anomaly.
[0062] This application embodiment verifies the reliability of the current position data after determining that the rack has reached the end position. The rack end value is only updated when the data is reliable; otherwise, a fault state is set in a timely manner. This can effectively improve the reliability and security of the system, avoid misoperation caused by incorrect position data, and enhance the ability to perceive and respond to operational anomalies.
[0063] The rack end-point learning method of this application embodiment is used for end-point learning of the rack in the lower steering subsystem of a steer-by-wire system. The method includes: determining whether the current position meets the end-point learning conditions based on the rack's current position, rack end-point value, maximum travel value, and minimum travel value; if the current position meets the end-point learning conditions, determining whether the rack has reached the end-point position based on the rack's speed and motor operating parameters, where the motor is the motor used to drive the rack's movement; when it is determined that the rack has reached the end-point position, updating the rack end-point value based on the current position. Thus, in this embodiment, by comprehensively considering the current position, current rack end-point value, and rack travel parameters, it is determined whether the current rack end-point value needs to be learned and updated. During the learning process, it is determined whether the end-point position has been reached based on the rack position signal and motor parameters; once it is confirmed that the end-point position has been reached, the current rack position is updated to the new rack end-point value. The end-point position learning can be completed solely based on the rack position signal, making the operation simple and efficient. Meanwhile, the embodiments of this application directly achieve end-position learning based on existing LPS sensor signals, without relying on TAS sensor signals, thereby achieving decoupling of the up and down steering systems in the steer-by-wire system, which not only reduces system complexity but also reduces reliance on additional sensors.
[0064] It is worth noting that, prior to step S101, this embodiment of the application further includes: obtaining an end reset signal; when the end reset signal is received, and / or, at least based on the current position, it is determined that a preset end reset condition is met, resetting the rack end value and setting the left and right end detection flags to 0; otherwise, performing the step of determining whether the current position meets the preset condition based on the rack's current position, rack end value, maximum travel value, and minimum travel value.
[0065] Specifically, when the current position is greater than the maximum travel value or less than the minimum travel value, the end-point reset condition is determined to be met. For example, when the rack moves to the left beyond the maximum travel value, it indicates a data anomaly. In this case, the rack end-point value can be directly reset to its initial state to ensure the system returns to normal operation.
[0066] This application embodiment combines the reset signal with the real-time position parameters of the rack to determine whether to perform a reset operation on the rack end value, which can effectively deal with data anomalies and improve the reliability and stability of the system.
[0067] Based on the rack end learning method provided in the above embodiments, this application also provides specific implementations of a rack end learning device. Please refer to the following embodiments.
[0068] like Figure 3 As shown, the rack end-learning device provided in this application embodiment is used for end-learning of the rack in the lower steering subsystem of a steer-by-wire system. The device includes:
[0069] The first judgment module 301 is used to determine whether the current position satisfies the end-point learning condition based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value.
[0070] The second determination module 302 is used to determine whether the rack has reached the end position based on the speed of the rack and the operating parameters of the motor if the current position meets the end learning condition. The motor is a motor used to drive the rack to move.
[0071] The end value update module 303 is used to update the end value of the rack based on the current position when it is determined that the rack has reached the end position.
[0072] Figure 4 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.
[0073] An electronic device may include a processor 601 and a memory 602 storing computer program instructions.
[0074] Specifically, the processor 601 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0075] Memory 602 may include mass storage for data or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 602 is non-volatile solid-state memory.
[0076] In a particular embodiment, memory 602 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.
[0077] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any of the rack end learning methods in the above embodiments.
[0078] In one example, the electronic device may also include a communication interface 603 and a bus 610. For example, Figure 4 As shown, the processor 601, memory 602, and communication interface 603 are connected through bus 610 and complete communication with each other.
[0079] The communication interface 603 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0080] Bus 610 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 610 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.
[0081] The electronic device can execute the rack end learning method in the embodiments of this application, thereby achieving the combination Figure 1 and Figure 3 The described rack end learning method and apparatus.
[0082] Furthermore, in conjunction with the rack end learning method in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the rack end learning methods in the above embodiments.
[0083] In conjunction with the rack end learning method in the above embodiments, this application embodiment can provide a computer program product, in which the instructions in the computer program product, when executed by the processor of an electronic device, cause the electronic device to execute any of the rack end learning methods described above.
[0084] In conjunction with the rack end learning method in the above embodiments, this application provides a steer-by-wire system to implement this method. The steer-by-wire system includes at least one of the following: the rack end learning device as described above; the computer-readable storage medium as described above; the computer program product as described above; a processor; and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the rack end learning method as described above.
[0085] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0086] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0087] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0088] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0089] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. A method for learning the end of a rack, characterized in that, The method for end-effector learning of the rack in the lower steering subsystem of a steer-by-wire system includes: Based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value, determine whether the current position satisfies the end-learning condition; If the current position satisfies the end-learning condition, then based on the speed of the rack and the operating parameters of the motor, it is determined whether the rack has reached the end position, and the motor is a motor used to drive the rack to move; When it is determined that the rack has reached the end position, the end value of the rack is updated based on the current position.
2. The rack end learning method according to claim 1, characterized in that, The determination of whether the current position satisfies the end-effector learning condition based on the rack's current position, rack end value, maximum stroke, and minimum stroke includes: The soft end interval is determined based on the rack end value, the maximum stroke value, and the minimum stroke value; If the current position is within the soft-terminal interval, then the current position is determined to meet the end-terminal learning condition.
3. The rack end learning method according to claim 2, characterized in that, The first end of the rack corresponds to the maximum travel value, and the other end corresponds to the minimum travel value. The first end can be any one of the two ends of the rack. The rack end value includes a corresponding first end value and a second end value. The soft end interval includes a first end interval and a second end interval. The method includes: The interval between the first end value and the maximum travel value is defined as the first end interval; The interval between the second terminal value and the minimum travel value is defined as the second terminal interval.
4. The rack end learning method according to claim 1, characterized in that, The operating parameters of the motor include motor speed and motor torque. Determining whether the rack has reached its end position based on the rack speed and the motor's operating parameters includes: If the speed of the rack is less than a preset speed threshold, the speed of the motor is less than a preset speed threshold, and the torque of the motor is greater than a preset torque threshold, then it is determined that the rack has reached the end position.
5. The rack end learning method according to claim 1, characterized in that, The method further includes: Obtain the end reset signal; When the end reset signal is received And / or, At least when the preset end-reset condition is met based on the current position, The value at the end of the rack is reset; Otherwise, the step of determining whether the current position meets the preset conditions based on the current position of the rack, the rack end value, the maximum stroke value, and the minimum stroke value is executed.
6. The rack end learning method according to claim 5, characterized in that, The method includes: When the current position is greater than the maximum travel value or less than the minimum travel value, the end reset condition is determined to be met.
7. The rack end learning method according to claim 1, characterized in that, The current position is determined based on position data collected by a linear position sensor. Before updating the rack end position based on the current position after determining that the rack has reached the end position, the method further includes: The position data is verified to determine for the second time whether the rack has reached the end position; If the verification passes, the rack end value is updated to the current position.
8. A rack end learning device, characterized in that, The device for end-effector learning of the rack in the lower steering subsystem of a steer-by-wire system includes: The first judgment module is used to determine whether the current position of the rack meets the end-point learning condition based on the rack's current position, rack end value, maximum stroke value, and minimum stroke value. The second determination module is used to determine whether the rack has reached the end position based on the speed of the rack and the operating parameters of the motor if the current position meets the end-learning condition. The motor is a motor used to drive the rack to move. The end value update module is used to update the end value of the rack based on the current position when it is determined that the rack has reached the end position.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the rack end learning method as described in any one of claims 1-7.
10. A steer-by-wire system, characterized in that, The steer-by-wire system includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the rack end learning method as described in any one of claims 1-7.